title: Gate Enhanced Power UMOSFET With Ultralow On-Resistance

abstract: Gate enhanced power UMOSFET (GE-UMOS) is proposed to decrease the specific on -resistance of the device. The key feature of this structure is that the deep trench polysilicon electrode is contacted to the gate electrode, maintaining the breakdown voltage and forming the high electron current density at side n-drift region, thus resulting in a lower on -resistance compared to the superjunction structure and gradient oxide-bypassed (GOB) structure. Furthermore, the performance of GE-UMOS is proved by comparing with the GOB-UMOS structure.


title: A new method to detect the short circuit current in DC supply system based on the flexible Rogowski coil

abstract: The short circuit current in DC (Direct Current) supply system will rise to a risk value within a few milliseconds. The current limiter must detect this fault on microsecond level to restrict the amplitude of short circuit current more effectively. For the existing method, the shunt is always employed for such fault detection, where the time to detect the fault current is relative long and hard for the rapid detection. In this paper, a novel method based on the flexible Rogowski coil is proposed, where the differential signal and the integral signal of the Rogowski coil are combined to perform the relay protection function. The design method of the coil, the relative experiments and the simulation result of the relay protection system are all discussed in sequence. Firstly, the equivalent circuit of the Rogowski coil is established, in which the distributed parameters of the coil are calculated with the Finite Element Method. Secondly, the frequency response of the coil is calculated and validated experimentally. The parameters of the Rogowski coil can be adjusted and designed according to the simulation result. Finally, the simulation of the relay protection system is performed, which shows that the demand of short current protection in ship DC distribution system can be satisfied and the method proposed in this paper is verified.


title: End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning

abstract: We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as inputs and produces the corresponding camera control signals as outputs (e.g., move forward, turn left, etc.). Conventional methods tackle tracking and camera control tasks separately, and the resulting system is difficult to tune jointly. Such an approach also requires significant human efforts for image labeling and expensive trial-and-error system tuning in real-world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning. A ConvNet-LSTM function approximator is adopted for the direct frame-to-action prediction. We further propose environment augmentation techniques and a customized reward function which are crucial for successful training. The tracker trained in simulators (ViZDoom, Unreal Engine) demonstrates good generalization behaviors in the case of unseen object moving paths, unseen object appearances, unseen backgrounds, and distracting objects. The system is robust and can restore tracking after occasional lost of the target being tracked. We also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios. We demonstrate successful examples of such transfer, via experiments over the VOT dataset and the deployment of a real-world robot using the proposed active tracker trained in simulation.


title: Using the original and 'symmetrical face' training samples to perform representation based two-step face recognition

abstract: A limited number of available training samples have become one bottleneck of face recognition. In real-world applications, the face image might have various changes owing to varying illumination, facial expression and poses. However, non-sufficient training samples cannot comprehensively convey these possible changes, so it is hard to improve the accuracy of face recognition. In this paper, we propose to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition. The new training samples really reflect some possible appearance of the face. The devised representation based method simultaneously uses the original and new training samples to perform a two-step classification, which ultimately uses a small number of classes that are 'near' to the test sample to represent and classify it and has a similar advantage as the sparse representation method. This method also takes advantages of the score level fusion, which has proven to be very competent and usually performs better than the decision level and feature level fusion. The experimental results show that the proposed method outperforms state-of-the-art face recognition methods including the sparse representation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparse representation (TPTSSR).


title: The Facial Platysma and Its Underappreciated Role in Lower Face Dynamics and Contour

abstract: BACKGROUND ::: The platysma is a superficial muscle involved in important features of the aging neck. Vertical bands, horizontal lines, and loss of lower face contour are effectively treated with botulinum toxin A (BoNT-A). However, its pars facialis, mandibularis, and modiolaris have been underappreciated. ::: ::: ::: OBJECTIVE ::: To demonstrate the role of BoNT-A treatment of the upper platysma and its impact on lower face dynamics and contour. ::: ::: ::: MATERIAL AND METHODS ::: Retrospective analysis of cases treated by an injection pattern encompassing the facial platysma components, aiming to block the lower face as a whole complex. It consisted of 2 intramuscular injections into the mentalis muscle and 2 horizontal lines of BoNT-A injections superficially performed above and below the mandible (total dose, 16 onabotulinumtoxinA U/side). Photographs were taken at rest and during motion (frontal and oblique views), before and after treatment. ::: ::: ::: RESULTS ::: A total of 161 patients have been treated in the last 2 years with the following results: frontal and lateral enhancement of lower facial contour, relaxation of high horizontal lines located just below the lateral mandibular border, and lower deep vertical smile lines present lateral to the oral commissures and melomental folds. ::: ::: ::: CONCLUSION ::: The upper platysma muscle plays a relevant role in the functional anatomy of the lower face that can be modulated safely with neuromodulators.


title: Prevention of bicycle accidents.

abstract: OBJECTIVE ::: To elucidate the way of reducing the number of bicycle accidents. A prospective study of all casualties from bicycle accidents attending an accident unit for a 29-month period was performed (1831 patients). A more detailed questionnaire on the causes of accidents was used for the last 12 months of the study (818 patients). ::: ::: ::: RESULTS ::: In the 0-7 and 8-12 years age groups, 87.5 per cent and 66.2 per cent, respectively, were due to cyclist error. The 8-12-year-old cyclists were twice as likely to have caused the accident if they had not had formal training (risk ratio = 2.0). Over the age of 18 years, 41.4 per cent were due to another road user. A motor vehicle was involved in 633 of the 1831 accidents. ::: ::: ::: CONCLUSIONS ::: Children under the age of 8 years should not be allowed on public roads. Older children should only be allowed on the roads after formal training. This should become part of the school curriculum. A campaign to increase the awareness of motorists would be expected to reduce the number of cycle accidents. It would be beneficial to dedicate more roads and tracks to cycle use. Cyclists should be encouraged to wear more protective gear.


title: Energy efficient automated control of irrigation in agriculture by using wireless sensor networks

abstract: We propose a model that calculates quantity of water needed for irrigation.The system efficiently manages water in cultivated fields in an automated way.The historical data and the change on the climate values are considered.The model uses a threshold for data collection to minimize the sensor energy. Many agricultural activities can be highly enhanced by using digital technologies. One of these activities is the regulation of the quantity of water in cultivated fields, a process which is directly interwoven with the sustainability and the productivity of crops, since insufficient or excessive irrigation may not only be obstructive, but also destructive. This paper proposes a scheme based on the collaboration of an integrated system for automated irrigation management with an advanced novel routing protocol for Wireless Sensor Networks (WSNs), named ECHERP (Equalized Cluster Head Election Routing Protocol). At its core, the proposed system aims at efficiently managing water supply in cultivated fields in an automated way. The system takes into consideration the historical data and the change on the climate values to calculate the quantity of water that is needed for irrigation. In case that the change on the collected values is above a threshold more frequent data collection is proposed to minimize the necessary quantity of water. On the other hand, in case that the change of the values is below a preset threshold then the time interval to collect data can increase to save sensor energy, leading to a prolonged sensor lifetime. The results show that network lifetime using ECHERP is improved up to 1825min and if a round is 110s the model provides energy efficiency using smaller water quantities.


title: Efficient Region Tracking With Parametric Models of Geometry and Illumination

abstract: As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane; complications arise due to the fact that the object undergoes changes in pose relative to the viewing camera, in illumination relative to light sources, and may even become partially or fully occluded. We develop an efficient general framework for object tracking, which addresses each of these complications. We first develop a computationally efficient method for handling the geometric distortions produced by changes in pose. We then combine geometry and illumination into an algorithm that tracks large image regions using no more computation than would be required to track with no accommodation for illumination changes. Finally, we augment these methods with techniques from robust statistics and treat occluded regions on the object as statistical outliers. Experimental results are given to demonstrate the effectiveness of our methods.


title: Evaluation of output embeddings for fine-grained image classification

abstract: Image classification has advanced significantly in recent years with the availability of large-scale image sets. However, fine-grained classification remains a major challenge due to the annotation cost of large numbers of fine-grained categories. This project shows that compelling classification performance can be achieved on such categories even without labeled training data. Given image and class embeddings, we learn a compatibility function such that matching embeddings are assigned a higher score than mismatching ones; zero-shot classification of an image proceeds by finding the label yielding the highest joint compatibility score. We use state-of-the-art image features and focus on different supervised attributes and unsupervised output embeddings either derived from hierarchies or learned from unlabeled text corpora. We establish a substantially improved state-of-the-art on the Animals with Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate that purely unsupervised output embeddings (learned from Wikipedia and improved with finegrained text) achieve compelling results, even outperforming the previous supervised state-of-the-art. By combining different output embeddings, we further improve results.


An Empirical Analysis of Software Vendors’ Patching Behavior: Impact of Vulnerability Disclosure


title: The stability of centrality measures when networks are sampled

abstract: The ability to measure centrality in social networks has been a particularly useful development in social network analysis. For researchers trying to decide which centrality measure is most meaningful and valid for their research purposes, various papers have explored the conceptual foundations of centrality measures. Less well documented is the empirical performance of centrality measures under different research scenarios or constraints. This study uses bootstrap sampling procedures to determine how sampling affects the stability of 11 different network centrality measures. Results indicate that some measures are more stable than others, and that stability is also a function of network and study properties.


title: Scalable and efficient data distribution for distributed computing of all-to-all comparison problems

abstract: All-to-all comparison problems represent a class of big data processing problems widely found in many application domains. To achieve high performance for distributed computing of such problems, storage usage, data locality and load balancing should be considered during the data distribution phase in the distributed environment. Existing data distribution strategies, such as the Hadoop one, are designed for problems with MapReduce pattern and do not consider comparison tasks at all. As a result, a huge amount of data must be re-arranged at runtime when the comparison tasks are executed, degrading the overall computing performance significantly. Addressing this problem, a scalable and efficient data distribution strategy is presented in this paper with comparison tasks in mind for distributed computing of all-to-all comparison problems. Specifically designed for problems with all-to-all comparison pattern, it not only saves storage space and data distribution time but also achieves load balancing and good data locality for all comparison tasks of the all-to-all comparison problems. Experiments are conducted to demonstrate the presented approaches. It is shown that about 90% of the ideal performance capacity of the multiple machines can be achieved through using the approach presented in this paper.


title: Education set design for smart home applications

abstract: Along with the increasing number of smart home applications, the need for the education of those systems has been unavoidable. Smart homes are a building and occupy large area. It has a lot of special control systems, communication systems and wiring. Thus, education of smart home systems requires special laboratories. At the places where special laboratories do not exist, the education is carried on theoretically. This is a problem. To achieve good smart home education a close collaboration between practice and theory is required. In this study, it has been tried to make an approach to the problem. So, the education set has been developed for better education than only theoretical education. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 631–638, 2011


title: CRMA: collision-resistant multiple access

abstract: Efficiently sharing spectrum among multiple users is critical to wireless network performance. In this paper, we propose a novel spectrum sharing protocol called Collision-Resistant Multiple Access (CRMA) to achieve high efficiency. In CRMA, each transmitter views the OFDM physical layer as multiple orthogonal but sharable channels, and independently selects a few channels for transmission. The transmissions that share the same channel naturally add up in the air. The receiver extracts the received signals from all the channels and efficiently decodes the transmissions by solving a simple linear system. We implement our approach in the Qualnet simulator and show that it yields significant improvement over existing spectrum sharing schemes. We also demonstrate the feasibility of our approach using implementation and experiments on GNU Radios.


title: Lightweight Classification of IoT Malware Based on Image Recognition

abstract: The Internet of Things (IoT) is an extension of the traditional Internet, which allows a very large number of smart devices, such as home appliances, network cameras, sensors and controllers to connect to one another to share information and improve user experiences. Current IoT devices are typically micro-computers for domain-specific computations rather than traditional functionspecific embedded devices. Therefore, many existing attacks, targeted at traditional computers connected to the Internet, may also be directed at IoT devices. For example, DDoS attacks have become very common in IoT environments, as these environments currently lack basic security monitoring and protection mechanisms, as shown by the recent Mirai and Brickerbot IoT botnets. In this paper, we propose a novel light-weight approach for detecting DDos malware in IoT environments.We firstly extract one-channel gray-scale images converted from binaries, and then utilize a lightweight convolutional neural network for classifying IoT malware families. The experimental results show that the proposed system can achieve 94.0% accuracy for the classification of goodware and DDoS malware, and 81.8% accuracy for the classification of goodware and two main malware families.


title: On security challenges and open issues in Internet of Things

abstract: Abstract When Internet of Things (IoT) applications become a part of people’s daily life, security issues in IoT have caught significant attention in both academia and industry. Compared to traditional computing systems, IoT systems have more inherent vulnerabilities, and meanwhile, could have higher security requirements. However, the current design of IoT does not effectively address the higher security requirements posed by those vulnerabilities. Many recent attacks on IoT systems have shown that novel security solutions are needed to protect this emerging system. This paper aims to analyze security challenges resulted from the special characteristics of the IoT systems and the new features of the IoT applications. This could help pave the road to better security solution design. In addition, three architectural security designs are proposed and analyzed. Examples of how to implement these designs are discussed. Finally, for each layer in IoT architecture, open issues are also identified.


title: Pectoral muscle identification in mammograms

abstract: In most of the approaches of computer-aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pectoral muscle in mediolateral oblique view mammograms. The watershed transformation of the mammogram shows interesting properties that include the appearance of a unique watershed line corresponding to the pectoral muscle edge. In addition to this, it is observed that the pectoral muscle region is oversegmented due to the existence of several catchment basins within the pectoral muscle. Hence, a suitable merging algorithm is proposed to combine the appropriate catchment basins to obtain the correct pectoral muscle region. A total of 84 mammograms from the mammographic image analysis database were used to validate this approach. The mean false positive and mean false negative rates, obtained by comparing the results of the proposed approach with manually-identified (ground truth) pectoral muscle boundaries, respectively, were 0.85% and 4.88%. A comparison of the results of the proposed method with related state-of-the-art methods shows that the performance of the proposed approach is better than the existing methods in terms of the mean false negative rate. Using Hausdorff distance metric, the comparison of the results of the proposed method with ground truth shows low Hausdorff distances, the mean and standard deviation being 3.85 ± 1.07 mm. In most of the approaches of computer-aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal\suppression of pectoral muscle as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pectoral muscle in medio-lateral oblique view mammograms. The watershed transformation of the mammogram shows interesting properties that include the appearance of a unique watershed line corresponding to the pectoral muscle edge. In addition to this, it is observed that the pectoral muscle region is over-segmented due to the existence of several catchment basins within the pectoral muscle. Hence, a suitable merging algorithm is proposed here to combine the appropriate catchment basins to obtain the correct pectoral muscle region. Totally 84 mammograms from the mammographic image analysis database were used to validate this approach. The mean false positive and mean false negative rates, obtained by comparing the results of the proposed approach with manually identified (ground truth) pectoral muscle boundaries, respectively, were 0.85% and 4.88%. A comparison of the results of the proposed method with related state-of-the-art methods shows that the performance of the proposed approach is better than the existing methods in terms of the mean false negative rate. Using Hausdorff distance metric, the comparison of the results of the proposed method with ground truth shows low Hausdorff distances, the mean and standard deviation being 3.85 ± 1.07 mm. In most of the approaches of computer-aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pectoral muscle in mediolateral oblique view mammograms. The watershed transformation of the mammogram shows interesting properties that include the appearance of a unique watershed line corresponding to the pectoral muscle edge. In addition to this, it is observed that the pectoral muscle region is oversegmented due to the existence of several catchment basins within the pectoral muscle. Hence, a suitable merging algorithm is proposed to combine the appropriate catchment basins to obtain the correct pectoral muscle region. A total of 84 mammograms from the mammographic image analysis database were used to validate this approach. The mean false positive and mean false negative rates, obtained by comparing the results of the proposed approach with manually-identified (ground truth) pectoral muscle boundaries, respectively, were 0.85% and 4.88%. A comparison of the results of the proposed method with related state-of-the-art methods shows that the performance of the proposed approach is better than the existing methods in terms of the mean false negative rate. Using Hausdorff distance metric, the comparison of the results of the proposed method with ground truth shows low Hausdorff distances, the mean and standard deviation being 3.85 ± 1.07 mm.


title: Inexpensive 1024-channel 3D telesonography system on FPGA

abstract: Volumetrie ultrasound (US) is a very promising development of medical US imaging. An under-exploited advantage of volumetric US is the mitigation of the strict probe positioning constrains necessary to acquire 2D scans, potentially allowing the decoupling of US image acquisition and diagnosis. However, today's 3D US systems are large and beset by high power and cost requirements, making them only available in well-equipped hospitals. In this study, we propose the first telesonography-capable medical imaging system that supports up to 1024 channels, on par with the state of the art. As a first embodiment, we have implemented our design in a single development FPGA board of 26.7cm×14cm×0.16cm, with an estimated power consumption of 6.1 W. Moreover, we have equipped our platform with an automatic positioning module to help any operator defining the scan location, hence allowing for better remote diagnosis. Our design supports two types of data inputs: real-time via an optical connection and offline over Ethernet. The reconstructed images can be visualized on an HDMI screen. The estimated cost of the proposed prototype materials is less than 4000€.


title: Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

abstract: Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.


title: Demo paper: Libdash - An open source software library for the MPEG-DASH standard

abstract: Dynamic Adaptive Streaming over HTTP (DASH) is an ISO/IEC MPEG standard which enables the convenient and smooth transportation of multimedia data to heterogeneous end devices over networks with variable bandwidth conditions. This kind of streaming technology is mainly used with HTTP 1.0 and 1.1 respectively, which both have some drawbacks. Therefore, the IETF has started the development of HTTP 2.0, which is based on Google's SPDY proposal and already supported by several major companies, e.g., Facebook, Twitter, Akamai, Mozilla and obviously Google. Furthermore, Content Centric Networking (CCN) is another novel approach for future networks that is considered as an revolutionary approach compared to HTTP 2.0. The CCN communication paradigm is completely different and does not rely on direct connections between hosts, it rather focuses on the content. This paper demonstrates DASH with HTTP 2.0/SPDY and CCN using our universal libdash library. Moreover, different mechanisms of DASH will be shown that can be used to provide on-demand and live content in an efficient and comfortable way.


title: Using Emoticons To Reduce Dependency In Machine Learning Techniques For Sentiment Classification

abstract: Sentiment Classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.


title: A Wideband Dual-Cavity-Backed Circularly Polarized Crossed Dipole Antenna

abstract: A novel wideband dual-cavity-backed circularly polarized (CP) crossed dipole antenna is presented in this letter. The exciter of the antenna comprises two classical orthogonal straight dipoles for a simple design. Dual-cavity structure is employed to achieve unidirectional radiation and improve the broadside gain. In particular, the rim edges of the cavity act as secondary radiators, which contribute to significantly enhance the overall CP performance of the antenna. The final design with an overall size of 0.57λo × 0.57λo × 0.24λo where λo is the free-space wavelength at the lowest CP operating frequency of 2.0 GHzb yields a measured –10 dB impedance bandwidth (BW) of 79.4% and 3 dB axial-ratio BW of 66.7%. The proposed antenna exhibits right-handed circular polarization with a maximum broadside gain of about 9.7 dBic.


title: Music Structure Analysis from Acoustic Signals

abstract: Music is full of structure, including sections, sequences of distinct musical textures, and the repetition of phrases or entire sections. The analysis of music audio relies upon feature vectors that convey information about music texture or pitch content. Texture generally refers to the average spectral shape and statistical fluctuation, often reflecting the set of sounding instruments, e.g. strings, vocal, or drums. Pitch content reflects melody and harmony, which is often independent of texture. Structure is found in several ways. Segment boundaries can be detected by observing marked changes in locally averaged texture. Similar sections of music can be detected by clustering segments with similar average textures. The repetition of a sequence of music often marks a logical segment. Repeated phrases and hierarchical structures can be discovered by finding similar sequences of feature vectors within a piece of music. Structure analysis can be used to construct music summaries and to assist music browsing.


title: Depression among Chinese University Students: Prevalence and Socio-Demographic Correlates

abstract: The purpose of the present study was to estimate the prevalence of depression in Chinese university students, and to identify the socio-demographic factors associated with depression in this population. A multi-stage stratified sampling procedure was used to select university students (N = 5245) in Harbin (Heilongjiang Province, Northeastern China), who were aged 16-35 years. The Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores of 14 or higher were categorized as depressive for logistic regression analysis. Depression was diagnosed by the Structured Clinical Interview (SCID) for the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV). 11.7% of the participants had a BDI score 14 or higher. Major Depressive Disorder was seen in 4.0% of Chinese university students. There were no statistical differences in the incidence of depression when gender, ethnicity, and university classification were analyzed. Multivariate analysis showed that age, study year, satisfaction with major, family income situation, parental relationship and mother's education were significantly associated with depression. Moderate depression is prevalent in Chinese university students. The students who were older, dissatisfied with their major, had a lower family income, poor parental relationships, and a lower level of mother's education were susceptible to depression.


title: Real time monitoring of the environmental parameters of an aquaponic system based on Internet of Things

abstract: Aquaponics is a system which is a junction of aquaculture and hydroponics, in which the waste produced by the aquatic creatures are passed as nutrients to grow plants hydroponically, which in turn purifies the water. This system can be made use of to produce organic food to match the ever-growing demand for food in the world. Since, the system continuouslyrecycles the water used, the quality of water must be constantly monitored at regular intervals. Manuallydoing this task can be very tiring. Hence, Internet of Things(IoT) can be used as an effective application for this purpose. This will considerably reduce the human intervention in the system and also improve the efficiency of the system. The latest technologies such as Internet of Things can help in implementing this system in urban areas like smart cities where traditional farming is not feasible and produce locally grown organic food. The system proposed here consists of continuous monitoring of the water quality and the plant's environment by using different sensors. The information collected by the sensors are accessed remotely by using the Internet of things.


title: Impairment of social cognition in multiple sclerosis: Amygdala atrophy is the main predictor

abstract: BACKGROUND ::: Patients with multiple sclerosis (MS) frequently reveal social behavior disturbance. Nevertheless, little is known regarding the impact of MS on social cognition, particularly theory of mind (ToM), and its neural basis. ::: ::: ::: OBJECTIVES ::: To explore how ToM is affected in MS and its neural correlates. ::: ::: ::: METHODS ::: Enrolled 60 consecutive MS patients and 60 healthy controls (HC) matched on age, sex, and education. Participants underwent ToM testing (Eyes Test, Videos Test) and 3 T brain magnetic resonance imaging (MRI). Using Freesurfer software, cortical and subcortical gray matter (GM) volumes were calculated. ::: ::: ::: RESULTS ::: MS patients performed worse on Eyes Test (58.7% ± 13.8% vs 81.9% ± 10.4%, p < 0.001) and Videos Test (75.3% ± 9.3% vs 88.1% ± 7.1%, p < 0.001). Eyes Test performance in MS was positively correlated with the volume of subcortical structures (amygdala, putamen) and cortical regions (entorhinal cortex, fusiform gyrus, superior temporal gyrus, superior parietal gyrus, supramarginal gyrus, medial orbitofrontal cortex, anterior and posterior cingulate gyrus). In regression analysis, amygdala volume was the single predictor of performance ( R2 change = 0.064, p = 0.031), and a mediation analysis indicated that it contributes for the differences observed between MS and HC. ::: ::: ::: CONCLUSION ::: Patients with MS have impairment on social cognition. Amygdala atrophy was the main predictor probably due to its central position within the "social brain" network.


title: A Review of Motion Planning Techniques for Automated Vehicles

abstract: Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.


Applications of Topics Models to Analysis of Disaster-Related Twitter Data


title: Sparse representation for face recognition based on discriminative low-rank dictionary learning

abstract: In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm.


title: Workspace satisfaction: The privacy-communication trade-off in open-plan offices

abstract: Open-plan office layout is commonly assumed to facilitate communication and interaction between co-workers, promoting workplace satisfaction and team-work effectiveness. On the other hand, open-plan layouts are widely acknowledged to be more disruptive due to uncontrollable noise and loss of privacy. Based on the occupant survey database from Center for the Built Environment (CBE), empirical analyses indicated that occupants assessed Indoor Environmental Quality (IEQ) issues in different ways depending on the spatial configuration (classified by the degree of enclosure) of their workspace. Enclosed private offices clearly outperformed open-plan layouts in most aspects of IEQ, particularly in acoustics, privacy and the proxemics issues. Benefits of enhanced ‘ease of interaction’ were smaller than the penalties of increased noise level and decreased privacy resulting from open-plan office configuration.


Formulation and Calibration of Fast , Accurate Vehicle Motion Models


title: A model for the effects of job insecurity on performance, turnover intention, and absenteeism

abstract: This study investigates the effects of job insecurity on four organizationally important outcomes: in-role behaviour, organizational citizenship behaviour, turnover intention, and absenteeism. A model is tested in which job insecurity is simultaneously a hindrance and a challenge stressor. In particular, job insecurity is proposed to have a predominantly harmful effect on performance, turnover intention, and absenteeism, and it is argued that these effects are mediated by (reduced) work attitudes. In addition, job insecurity is also assumed to affect these behaviours in the opposite way (i.e. a suppressor effect) because job insecurity might motivate employees to make themselves more valuable to the organization by working harder and being less absent. The model is tested with a sample of 136 German non-managerial employees. Data from supervisors (i.e. in-role behaviour and organizational citizenship behaviour), the company's personnel files (i.e. absenteeism), and self-reports (i.e. job insecurity, work attitudes, turnover intention, in-role behaviour, and organizational citizenship behaviour) were used. Structural equation modelling showed that a model that included both negative and positive effects fitted the data best. The negative effect was stronger than the positive effect. The results show that the effects of job insecurity are more complex than previously assumed. In addition, the results also extend previous research into hindrance and challenge stressors because they show that stressors should not be categorized as either hindrance or challenge. Instead, it might be more appropriate to conceptualize hindrance and challenge as two dimensions.


title: A Permutation Entropy-based Importance Measure for Condition Monitoring Data Fusion in Fault Diagnosis

abstract: In condition monitoring and fault diagnosis, how to measure the importance degree of different condition monitoring (CM) data before data fusion is a vital issue. We propose an importance measure that can be modeled using a weighted average function. The weight is measured with the relative scale of the permutation entropy from each fault feature variable. Compared with some other importance measures in data fusion, the proposed measure focuses on the degradation trend represented by the permutation entropy, instead of the information volume represented by the Shannon entropy. Then, a multiple fault feature variable fusion method based on the proposed importance measure is further proposed in the D-S evidence theory framework. Finally, a case study involving an oil analysis-based dataset from a power-shift steering transmission is carried out to investigate the superiority of the proposed method.


title: Building Phylogenetic Trees from Molecular Data with MEGA

abstract: Phylogenetic analysis is sometimes regarded as being an intimidating, complex process that requires expertise and years of experience. In fact, it is a fairly straightforward process that can be learned quickly and applied effectively. This Protocol describes the several steps required to produce a phylogenetic tree from molecular data for novices. In the example illustrated here, the program MEGA is used to implement all those steps, thereby eliminating the need to learn several programs, and to deal with multiple file formats from one step to another (Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. 2011. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 28:2731‐2739). The first step, identification of a set of homologous sequences and downloading those sequences, is implemented by MEGA’s own browser built on top of the Google Chrome toolkit. For the second step, alignment of those sequences, MEGA offers two different algorithms: ClustalW and MUSCLE. For the third step, construction of a phylogenetic tree from the aligned sequences, MEGA offers many different methods. Here we illustrate the maximum likelihood method, beginning with MEGA’s Models feature, which permits selecting the most suitable substitution model. Finally, MEGA provides a powerful and flexible interface for the final step, actually drawing the tree for publication. Here a step-by-step protocol is presented in sufficient detail to allow a novice to start with a sequence of interest and to build a publication-quality tree illustrating the evolution of an appropriate set of homologs of that sequence. MEGA is available for use on PCs and Macs from www. megasoftware.net.


title: STFCN: Spatio-Temporal FCN for Semantic Video Segmentation

abstract: This paper presents a novel method to involve both spatial and temporal features for semantic video segmentation. Current work on convolutional neural networks(CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for both image and video analysis, especially for the semantic segmentation task. We investigate how involving temporal features also has a good effect on segmenting video data. We propose a module based on a long short-term memory (LSTM) architecture of a recurrent neural network for interpreting the temporal characteristics of video frames over time. Our system takes as input frames of a video and produces a correspondingly-sized output; for segmenting the video our method combines the use of three components: First, the regional spatial features of frames are extracted using a CNN; then, using LSTM the temporal features are added; finally, by deconvolving the spatio-temporal features we produce pixel-wise predictions. Our key insight is to build spatio-temporal convolutional networks (spatio-temporal CNNs) that have an end-to-end architecture for semantic video segmentation. We adapted fully some known convolutional network architectures (such as FCN-AlexNet and FCN-VGG16), and dilated convolution into our spatio-temporal CNNs. Our spatio-temporal CNNs achieve state-of-the-art semantic segmentation, as demonstrated for the Camvid and NYUDv2 datasets.


title: Design of Intelligent Agriculture Management Information System Based on IoT

abstract: Agricultural information technology (AIT) has been broadly applied to every aspect of agriculture and has become the most effective means & tools for enhancing agricultural productivity and for making use of full agricultural resources. As an important sub-technology of AIT, the using of technology of Agriculture Information Management directly affects the degree of agricultural informatization and efficiency of agricultural production's decision. In this paper, on the basis of introducing the concept of agricultural information management and analyzing the features of Agricultural data, the designing method and architecture of Intelligent Agriculture MIS was discussed in detail, finally, this paper gives an implementation example of system in agricultural production.


title: The ICSI Meeting Recorder Dialog Act (MRDA) Corpus

abstract: Abstract : We describe a new corpus of over 180,000 hand- annotated dialog act tags and accompanying adjacency pair annotations for roughly 72 hours of speech from 75 naturally-occurring meetings. We provide a brief summary of the annotation system and labeling procedure, inter-annotator reliability statistics, overall distributional statistics, a description of auxiliary files distributed with the corpus, and information on how to obtain the data.


title: DefDroid: Towards a More Defensive Mobile OS Against Disruptive App Behavior

abstract: The mobile app market is enjoying an explosive growth. Many people, including teenagers, set about developing mobile apps. Unfortunately, due to developers' inexperience and the unique mobile programming paradigms, a growing number of immature apps are released to users. Despite having useful functionalities, these apps exhibit disruptive behaviors that are inconsiderate to the mobile system as a whole, e.g.,, retrying network connections too aggressively, waking up the device too frequently, or holding resources for unnecessarily long. These behaviors adversely affect other apps running on the same device and frustrate users with battery drain, excessive cellular data consumption, storage overuse, etc. In this paper, we investigate Disruptive App Behavior (DAB) with a study on 287 real-world DAB issues. Guided by the study, we present DefDroid, a mobile OS designed to protect users from the negative impact of DAB at runtime. DefDroid monitors important app activities and tries to adjust app behaviors using fine-grained actions (e.g., enforce back-off to continuous retries, decrease aggressive timer frequency) without breaking app main functionality. Our experiments show that DefDroid effectively curbs 125 real-world DAB cases with small overhead and marginal impact to the usability of both the misbehaving apps and normal apps. During a small-scale user trial, DefDroid also found 6 new DAB issues. We further deployed DefDroid to 185 real users through the PhoneLab testbed for 43 days and found DAB issues from at least 57 apps.


title: Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments

abstract: We present a practical approach to global motion planning and terrain assessment for ground robots in generic three-dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six-dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot-sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling-based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint-aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments—rough outdoor terrain, a two-level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain.


title: How Attributional Ambiguity Shapes Physiological and Emotional Responses to Social Rejection and Acceptance

abstract: The authors examined White and Black participants’ emotional, physiological, and behavioral responses to same-race or different-race evaluators, following rejecting social feedback or accepting social feedback. As expected, in ingroup interactions, the authors observed deleterious responses to social rejection and benign responses to social acceptance. Deleterious responses included cardiovascular (CV) reactivity consistent with threat states and poorer performance, whereas benign responses included CV reactivity consistent with challenge states and better performance. In intergroup interactions, however, a more complex pattern of responses emerged. Social rejection from different-race evaluators engendered more anger and activational responses, regardless of participants’ race. In contrast, social acceptance produced an asymmetrical race pattern—White participants responded more positively than did Black participants. The latter appeared vigilant and exhibited threat responses. Discussion centers on implications for attributional ambiguity theory and potential pathways from discrimination to health outcomes.


Convolutional Neural Networks for So-Matching N-Grams in Ad-hoc Search Zhuyun Dai


title: Approximate dynamic programming using fluid and diffusion approximations with applications to power management

abstract: TD learning and its refinements are powerful tools for approximating the solution to dynamic programming problems. However, the techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach for TD learning based on choosing the function class using the solutions to associated fluid and diffusion approximations. In order to illustrate this new approach, the paper focuses on an application to dynamic speed scaling for power management.


title: Deep Embedding for Spatial Role Labeling

abstract: This paper introduces the visually informed embedding of word (VIEW), a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW is applied to transferring multimodal background knowledge to Spatial Role Labeling (SpRL) algorithms, which recognize spatial relations between objects mentioned in the text. This work also contributes with a new method to select complementary features and a fine-tuning method for MLP that improves the $F1$ measure in classifying the words into spatial roles. The VIEW is evaluated with the Task 3 of SemEval-2013 benchmark data set, SpaceEval.


title: EmojiNet: Building a Machine Readable Sense Inventory for Emoji

abstract: Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or ‘sense’ of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning. It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date. The paper discusses its construction, evaluates the automatic resource creation process, and presents a use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is available online for use at http://emojinet.knoesis.org.


title: Non-negative Matrix Factorization Meets Word Embedding

abstract: Document clustering is central in modern information retrieval applications. Among existing models, non-negative-matrix factorization (NMF) approaches have proven effective for this task. However, NMF approaches, like other models in this context, exhibit a major drawback, namely they use the bag-of-word representation and, thus, do not account for the sequential order in which words occur in documents. This is an important issue since it may result in a significant loss of semantics. In this paper, we aim to address the above issue and propose a new model which successfully integrates a word embedding model, word2vec, into an NMF framework so as to leverage the semantic relationships between words. Empirical results, on several real-world datasets, demonstrate the benefits of our model in terms of text document clustering as well as document/word embedding.


title: A geometric approach to shape from defocus

abstract: We introduce a novel approach to shape from defocus, i.e., the problem of inferring the three-dimensional (3D) geometry of a scene from a collection of defocused images. Typically, in shape from defocus, the task of extracting geometry also requires deblurring the given images. A common approach to bypass this task relies on approximating the scene locally by a plane parallel to the image (the so-called equifocal assumption). We show that this approximation is indeed not necessary, as one can estimate 3D geometry while avoiding deblurring without strong assumptions on the scene. Solving the problem of shape from defocus requires modeling how light interacts with the optics before reaching the imaging surface. This interaction is described by the so-called point spread function (PSF). When the form of the PSF is known, we propose an optimal method to infer 3D geometry from defocused images that involves computing orthogonal operators which are regularized via functional singular value decomposition. When the form of the PSF is unknown, we propose a simple and efficient method that first learns a set of projection operators from blurred images and then uses these operators to estimate the 3D geometry of the scene from novel blurred images. Our experiments on both real and synthetic images show that the performance of the algorithm is relatively insensitive to the form of the PSF Our general approach is to minimize the Euclidean norm of the difference between the estimated images and the observed images. The method is geometric in that we reduce the minimization to performing projections onto linear subspaces, by using inner product structures on both infinite and finite-dimensional Hilbert spaces. Both proposed algorithms involve only simple matrix-vector multiplications which can be implemented in real-time.


title: Inferring mass in complex scenes by mental simulation

abstract: Abstract After observing a collision between two boxes, you can immediately tell which is empty and which is full of books based on how the boxes moved. People form rich perceptions about the physical properties of objects from their interactions, an ability that plays a crucial role in learning about the physical world through our experiences. Here, we present three experiments that demonstrate people’s capacity to reason about the relative masses of objects in naturalistic 3D scenes. We find that people make accurate inferences, and that they continue to fine-tune their beliefs over time. To explain our results, we propose a cognitive model that combines Bayesian inference with approximate knowledge of Newtonian physics by estimating probabilities from noisy physical simulations. We find that this model accurately predicts judgments from our experiments, suggesting that the same simulation mechanism underlies both peoples’ predictions and inferences about the physical world around them.


title: A General Architecture of IoT System

abstract: This paper defined a unified architecture of IoT system, on which IoT node model, virtual things, the basic service of things and overall hierarchical model of services had been described. IoT nodes must be connected directly to Internet and provide basic services of things in this architecture, and started from the base services which were compatible with Internet applications system of SOA, built the IoT application system with the middle layer of the Internet-based services and the base services of things, in order to implement a complete IoT system. this study also provides a practical application for monitoring water resources in the IoT way.


title: A prototype of multistage dynamic braking of three phase squirrel cage induction motor

abstract: This paper discussed a fast dynamic braking method of three phase induction motor. This braking method consists of two conventional braking methods i.e. direct current injection braking and capacitor self excitation braking. Those mathods were arranged in a such grading time to become a multistage dynamic braking. Simulation was done using MATLAB/Simulink software for design and predicting the behaviour. The results showed that the propossed method gave faster braking than the other two methods carried out separately.


title: A Beam-Steerable

abstract: A method to design and realize the beam-steerable lens is proposed and tested in this paper. Such a lens is composed with varactor-diodes-loaded metamaterials. The electromagnetic properties of the lens, in particular, the refractive index distribution, can be controlled by reverse bias voltages added to the metamaterials. Therefore, the lens obtains flexible beam forming functions when a variety of reverse bias voltages are applied. A prototype of the lens is manufactured, and its beam-forming function is successfully demonstrated in experiment.


title: Agile Software Development at Scale

abstract: Since 2001 agile software development approaches are being adopted across a wide range of organizations and are now being applied at scale. There are eight factors to consider --- team size, geographical distribution, entrenched culture, system complexity, legacy systems, regulatory compliance, organizational distribution, governance and enterprise focus --- when scaling agile. Luckily a collection of techniques and strategies exist which scale agile approaches, including considering the full development lifecycle, Agile Model Driven Development (AMDD), continuous independent testing, adopting proven strategies, agile database techniques, and lean development governance. It is possible to scale agile approaches, but you will need to look beyond the advice provided by the "agile in the small" literature.


title: Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving

abstract: Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, it tends to change the mean brightness of the image to the middle level of the gray-level range, which is not desirable in the case of images from consumer electronics products. In the latter case, preserving the input brightness of the image is required to avoid the generation of non-existing artifacts in the output image. To surmount this drawback, Bi- HE methods for brightness preserving and contrast enhancement have been proposed. Although these methods preserve the input brightness on the output image with a significant contrast enhancement, they may produce images with do not look as natural as the input ones. In order to overcome this drawback, this work proposes a novel technique called Multi-HE, which consists of decomposing the input image into several sub-images, and then applying the classical HE process to each one. This methodology performs a less intensive image contrast enhancement, in a way that the output image presents a more natural look. We propose two discrepancy functions for image decomposing, conceiving two new Multi-HE methods. A cost function is also used for automatically deciding in how many sub-images the input image will be decomposed on. Experiments show that our methods preserve more the brightness and produce more natural looking images than the other HE methods.


title: Fine-Grained Categorization via CNN-Based Automatic Extraction and Integration of Object-Level and Part-Level Features

abstract: Fine-grained categorization can benefit from part-based features which reveal subtle visual differences between object categories. Handcrafted features have been widely used for part detection and classification. Although a recent trend seeks to learn such features automatically using powerful deep learning models such as convolutional neural networks (CNN), their training and possibly also testing require manually provided annotations which are costly to obtain. To relax these requirements, we assume in this study a general problem setting in which the raw images are only provided with object-level class labels for model training with no other side information needed. Specifically, by extracting and interpreting the hierarchical hidden layer features learned by a CNN, we propose an elaborate CNN-based system for fine-grained categorization. When evaluated on the Caltech-UCSD Birds-200-2011, FGVC-Aircraft, Cars and Stanford dogs datasets under the setting that only object-level class labels are used for training and no other annotations are available for both training and testing, our method achieves impressive performance that is superior or comparable to the state of the art. Moreover, it sheds some light on ingenious use of the hierarchical features learned by CNN which has wide applicability well beyond the current fine-grained categorization task.


title: Segmentation of breast cancer lesion in digitized mammogram images

abstract: Segmentation or abnormality detection is an essential step in mammographic computer-aided diagnosis (CAD) systems. This paper presents a novel computerized method to automatically detect mass lesions (i.e. detect suspicious locations containing abnormalities inside the breast area) on digitized mammogram images. In particular, we implement an enhanced version of the region growing algorithm for segmentation of mass lesions that can be implemented in a complete CAD system. The proposed algorithm uses region growing technique with a novel automatic threshold estimation method to detect and segment mass lesions. The proposed algorithm detects masses by analyzing a single view of the breast (i. e. Medio-Lateral oblique (MLO) view or Cranio-Caudal (CC) view). The performance of the proposed algorithm was evaluated using two mammogram databases from two different hospitals. The matching percentage of the segmented regions obtained by the proposed algorithm is 83% with respect to the ground truth (i.e. reference determined by an expert radiologist). The proposed algorithm showed promising performance when compared with other commonly used segmentation techniques.


title: gr-isdbt: An ISDB-T 1-segment receiver implementation on GNU Radio

abstract: Several countries in the world are undertaking an immensely challenging task: successfully perform the so-called "analogical blackout" of television. In Latin America in particular, the chosen transmission scheme is, in most cases, ISDB-T (the Japanese standard, later adapted by Brazil). Key to the success of this blackout is a thorough understanding of the technology of choice. The present paper intends to be a contribution to this understanding: an open, free and software-based ISDB-T 1-segment receiver. Such receiver may for instance be used to evaluate improvements to the standard (or different algorithms for the receivers), or even be used as a measurement tool (since one has access to the whole receiving chain). In addition to presenting our implementation, we discuss the technology that enables the software-based receiver: Software Defined Radio, and in particular the software framework GNU Radio.


title: Attack detection in time series for recommender systems

abstract: Recent research has identified significant vulnerabilities in recommender systems. Shilling attacks, in which attackers introduce biased ratings in order to influence future recommendations, have been shown to be effective against collaborative filtering algorithms. We postulate that the distribution of item ratings in time can reveal the presence of a wide range of shilling attacks given reasonable assumptions about their duration. To construct a time series of ratings for an item, we use a window size of k to group consecutive ratings for the item into disjoint windows and compute the sample average and sample entropy in each window. We derive a theoretically optimal window size to best detect an attack event if the number of attack profiles is known. For practical applications where this number is unknown, we propose a heuristic algorithm that adaptively changes the window size. Our experimental results demonstrate that monitoring rating distributions in time series is an effective approach for detecting shilling attacks.


title: A novel Maximum Power Point tracking control of photovoltaic system under partial and rapidly fluctuating shadow conditions using Differential Evolution

abstract: Photovoltaic (PV) system performance extremely depends on local insolation and temperature conditions. Under partial shading, P-I characteristics of PV systems are complicated and may have multiple local maxima. Conventional Maximum Power Point Tracking (MPPT) techniques can easily fail to track global maxima and may be trapped in local maxima under partial shading; this can be one of main causes for reduced energy yield for many PV systems. In order to solve this problem, this paper proposes a novel Maximum Power Point tracking algorithm based on Differential Evolution (DE) that is capable of tracking global MPP under partial shaded conditions. The ability of proposed algorithm and its excellent performances are evaluated with conventional and popular algorithm by means of simulation. The proposed algorithm works in conjunction with a Boost (step up) DC-DC converter to track the global peak. Moreover, this paper includes a MATLAB-based modeling and simulation scheme suitable for photovoltaic characteristics under partial shading.


title: Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

abstract: A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.


title: Ringo: Interactive Graph Analytics on Big-Memory Machines

abstract: We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.


title: Crocodiles in the Regulatory Swamp: Navigating The Dangers of Outsourcing, SaaS and Shadow IT

abstract: Corporates are entering the brave new world of the internet and digitization without much regard for the fine print of a growing regulation regime. More traditional outsourcing arrangements are already falling foul of the regulators as rules and supervision intensifies. Furthermore, ‘shadow IT’ is proliferating as the attractions of SaaS, mobile, cloud services, social media, and endless new ‘apps’ drive usage outside corporate IT. Initial cost-benefit analyses of the Cloud make such arrangements look immediately attractive but losing control of architecture, security, applications and deployment can have far reaching and damaging regulatory consequences. From research in financial services, this paper details the increasing body of regulations, their inherent risks for businesses and how the dangers can be pre-empted and managed. We then delineate a model for managing these risks specifically focused on investigating, strategizing and governing outsourcing arrangements and related regulatory obligations


title: Particle-based Pedestrian Path Prediction using LSTM-MDL Models

abstract: Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a probability density function over the state space. Therefore, recurrent models are a natural choice to address path prediction tasks, where a trained model is used to generate future expectations from past observations. When applied to security applications, like predicting pedestrian paths for risk assessment, a point-wise greedy evaluation of the output pdf is not feasible, since the environment often allows multiple choices. Therefore, a robust risk assessment has to take all options into account, even if they are overall not very likely. Towards this end, a combination of particle filtering strategies and a LSTM-MDL model is proposed to address a multimodal path prediction task. The capabilities and viability of the proposed approach are evaluated on several synthetic test conditions, yielding the counter-intuitive result that the simplest approach performs best. Further, the feasibility of the proposed approach is illustrated on several real world scenes.


title: End-to-End Deep Learning for Robotic Following

abstract: Robotic following has many practical applications and involves several cognitive sub-tasks including object identification, localization, tracking, path planning, and motion control. We propose a system that uses a single deep neural network (DNN), with video camera pixels as the only input, to handle all the cognitive perception and visuomotor control functions needed to perform robotic following behaviors. Our approach combines 1) end-to-end deep learning for inferring motion control outputs from visual inputs, 2) multi-task learning for simultaneously producing multiple outputs with the same DNN, and 3) spatio-temporal deep learning for perceiving motion across multiple video frames. We use simulations of truck platooning to quantitatively show that spatio-temporal DNNs increase driving accuracy and improve machine perception of scene kinematics. Experiments conducted with mobile robots in a laboratory test track show real-time embedded systems performance and indicate that an end-to-end deep learning robot is able to follow its leader for long periods of time, while keeping within lanes and avoiding obstacles.


title: 5D data modelling: full integration of 2D/3D space, time and scale dimensions

abstract: This paper proposes an approach for data modelling in five dimensions. Apart from three dimensions for geometrical representation and a fourth dimension for time, we identify scale as fifth dimensional characteristic. Considering scale as an extra dimension of geographic information, fully integrated with the other dimensions, is new. Through a formal definition of geographic data in a conceptual 5D continuum, the data can be handled by one integrated approach assuring consistency across scale and time dimensions. Because the approach is new and challenging, we choose to step-wise studying several combinations of the five dimensions, ultimately resulting in the optimal 5D model. We also propose to apply mathematical theories on multidimensional modelling to well established principles of multidimensional modelling in the geo-information domain. The result is a conceptual full partition of the 3Dspace+time+scale space (i.e. no overlaps, no gaps) realised in a 5D data model implemented in a Database Management System.


title: Activating and optimizing MoS2 basal planes for hydrogen evolution through the formation of strained sulphur vacancies

abstract: Molybdenum disulphide is a promising non-precious catalyst for hydrogen evolution because it contains active edge sites and an inert basal plane. Introducing sulphur vacancies and strain now leads to activation and optimization of the basal plane.


title: Amplifying community content creation with mixed initiative information extraction

abstract: Although existing work has explored both information extraction and community content creation, most research has focused on them in isolation. In contrast, we see the greatest leverage in the synergistic pairing of these methods as two interlocking feedback cycles. This paper explores the potential synergy promised if these cycles can be made to accelerate each other by exploiting the same edits to advance both community content creation and learning-based information extraction. We examine our proposed synergy in the context of Wikipedia infoboxes and the Kylin information extraction system. After developing and refining a set of interfaces to present the verification of Kylin extractions as a non primary task in the context of Wikipedia articles, we develop an innovative use of Web search advertising services to study people engaged in some other primary task. We demonstrate our proposed synergy by analyzing our deployment from two complementary perspectives: (1) we show we accelerate community content creation by using Kylin's information extraction to significantly increase the likelihood that a person visiting a Wikipedia article as a part of some other primary task will spontaneously choose to help improve the article's infobox, and (2) we show we accelerate information extraction by using contributions collected from people interacting with our designs to significantly improve Kylin's extraction performance.


title: The porous nature of ZnO thin films deposited by sol–gel Spin-Coating technique

abstract: Zinc oxide (ZnO) thin films were deposited on silicon and quartz substrates, by sol-gel method, using zinc acetate dehydrate [Zn(CH3COO)2.2H2O] dissolved in isopropanol and glycerol. The structural, morphologic and optical properties of ZnO thin films subsequently annealed at 700°C in air for 30 min have leads to a porous nature of these films. To calculate, the refraction index and the extinction coefficient values, Cauchy formalism is used to evaluate the Spectroscopic Ellipsometry results. Two distinct configurations were proposed for each sample: in the first, the film is considered as mixture of randomly distributed voids and ZnO crystallites while in the second, the effect of porosity gradient is highlighted. This optical analysis gives a better agreement between experiment and theory for a wide range of wavelengths regarding the second configuration.


title: A LLC resonant converter with a notch filter at secondary side for high step-up and wide voltage range applications

abstract: A multi-resonant converter is proposed by embedding a notch filter at secondary side in the conventional LLC converter for both high step-up and wide voltage range applications. Compared to LLC converter, the proposed converter can achieve wide voltage regulation within relatively narrow switching frequency range, and also the proposed resonant tank can effectively transfer both fundamental and third harmonics elements, resulting in high conversion efficiency. By moving the LC notch filter of the LCLCL converter to the secondary side, the proposed converter avoids the circulating currents at primary side flowing through LC notch filter, which benefits to reducing conduction losses. The operation principles and characteristics are analyzed in detail using first harmonic approximation. A 400W prototype with input voltage range from 25V to 50V was built, and experimental results show overall efficiency of the proposed converter is 1.5% higher than LCLCL converter, which indicates the feasibility of the proposed converter in high step-up and wide voltage range applications.


Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry.


title: Design and probe based measurement of 77 GHz antennas for antenna in package applications

abstract: The development and the measurement of two planar antenna structures is presented. These antennas are prospective candidates for the integration into compact millimeter-wave packages for 77 GHz automotive Radar applications. The antennas were manufactured using only one metal layer on top of a 127 µm Alumina substrate. Gain, radiation pattern and Return Loss of the antennas are characterised using a novel probe based measurement setup.


title: Design science research post hevner et al.: criteria, standards, guidelines, and expectations

abstract: There is ongoing debate about how the quality (rigour and relevance) of Design Science Research (DSR) should be judged This research investigates the state of the debate by surveying the opinions of IS scholars who write, review, edit, and publish DSR papers The survey respondents rated the relative importance of the seven guidelines (often used as evaluation criteria) laid out in Hevner et al (2004) [6], more specific criteria about the evaluation activity in DSR, criteria concerning IS Design Theories, and miscellaneous other criteria, and made general open-ended comments The findings indicate a lack of consensus, with much variability in ratings The Hevner et al [6] guidelines are largely endorsed, but caution is also raised to apply them less mechanistically than at present Some criteria/guidelines are seen to be less important at earlier stages of research Caution is also urged not to expect single papers to fit all criteria/guidelines.


title: Bayesian Optimization with Gradients

abstract: Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective function evaluations required for good performance. In particular, we develop a novel Bayesian optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for which we show one-step Bayes-optimality, asymptotic consistency, and greater one-step value of information than is possible in the derivative-free setting. Our procedure accommodates noisy and incomplete derivative information, comes in both sequential and batch forms, and can optionally reduce the computational cost of inference through automatically selected retention of a single directional derivative. We also compute the d-KG acquisition function and its gradient using a novel fast discretization-free technique. We show d-KG provides state-of-the-art performance compared to a wide range of optimization procedures with and without gradients, on benchmarks including logistic regression, deep learning, kernel learning, and k-nearest neighbors.


title: Modeling Relationships in Referential Expressions with Compositional Modular Networks

abstract: People often refer to entities in an image in terms of their relationships with other entities. For example, the black cat sitting under the table refers to both a black cat entity and its relationship with another table entity. Understanding these relationships is essential for interpreting and grounding such natural language expressions. Most prior work focuses on either grounding entire referential expressions holistically to one region, or localizing relationships based on a fixed set of categories. In this paper we instead present a modular deep architecture capable of analyzing referential expressions into their component parts, identifying entities and relationships mentioned in the input expression and grounding them all in the scene. We call this approach Compositional Modular Networks (CMNs): a novel architecture that learns linguistic analysis and visual inference end-to-end. Our approach is built around two types of neural modules that inspect local regions and pairwise interactions between regions. We evaluate CMNs on multiple referential expression datasets, outperforming state-of-the-art approaches on all tasks.


title: Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods

abstract: Abstract Background Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. Methods In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. Results The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Conclusions Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity.


title: Histological analysis of acute toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in zebrafish.

abstract: Previous studies have demonstrated that acute exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) by injection leads to inhibition of caudal fin regeneration in zebrafish. Since the TCDD exposure in these studies is systemic, it is possible that pathology in organs other than the fin could result in inhibition of fin regeneration. Therefore, histopathology of adult zebrafish (Danio rerio) organs was characterized following abdominal cavity injection of a TCDD dose (70ng/g). The most pronounced histopathologic changes 5 days post-injection included lipidosis and hypertrophy of liver hepatocytes and hypertrophy of gill lamellae. Effects of TCDD exposure on immunolocalization of the zebrafish aryl hydrocarbon receptor nuclear translocator (ARNT2), the heterodimer partner of the aryl hydrocarbon receptor (AHR2), and an AHR regulated gene cytochrome P450 1A (CYP1A) was also determined. ARNT2 was immunolocalized to the gastrointestinal tract, gill lamellae, kidney, ventricle of the heart, caudal fin, brain and liver of zebrafish. TCDD exposure had no measurable effect on ARNT2 abundance or localization. CYP1A was immunolocalized in TCDD exposed fish as a biomarker for cells with an activated AHR pathway. CYP1A was not detected in any tissue from vehicle exposed fish. Significant TCDD-dependent induction of CYP1A was detected in the proximal tubules of the kidney, in liver hepatocytes and in the gastrointestinal tract of TCDD exposed fish. Significant but lower TCDD-dependent CYP1A expression was evident in the gill, caudal fin and ventricle of the heart. Overall, TCDD exposure in adult zebrafish leads to histopathology similar to that reported in other fish species, and it appears unlikely that the histopathology in these organs completely explains the inhibition of fin regeneration.


title: Microfibre–nanowire hybrid structure for energy scavenging

abstract: A self-powering nanosystem that harvests its operating energy from the environment is an attractive proposition for sensing, personal electronics and defence technologies. This is in principle feasible for nanodevices owing to their extremely low power consumption. Solar, thermal and mechanical (wind, friction, body movement) energies are common and may be scavenged from the environment, but the type of energy source to be chosen has to be decided on the basis of specific applications. Military sensing/surveillance node placement, for example, may involve difficult-to-reach locations, may need to be hidden, and may be in environments that are dusty, rainy, dark and/or in deep forest. In a moving vehicle or aeroplane, harvesting energy from a rotating tyre or wind blowing on the body is a possible choice to power wireless devices implanted in the surface of the vehicle. Nanowire nanogenerators built on hard substrates were demonstrated for harvesting local mechanical energy produced by high-frequency ultrasonic waves. To harvest the energy from vibration or disturbance originating from footsteps, heartbeats, ambient noise and air flow, it is important to explore innovative technologies that work at low frequencies (such as <10 Hz) and that are based on flexible soft materials. Here we present a simple, low-cost approach that converts low-frequency vibration/friction energy into electricity using piezoelectric zinc oxide nanowires grown radially around textile fibres. By entangling two fibres and brushing the nanowires rooted on them with respect to each other, mechanical energy is converted into electricity owing to a coupled piezoelectric-semiconductor process. This work establishes a methodology for scavenging light-wind energy and body-movement energy using fabrics.


title: The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox

abstract: To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to support training complex models on large datasets. Unfortunately, the design of these systems largely ignores a critical component of the overall analytics process: the deployment and serving of models at scale. In this work, we present Velox, a new component of the Berkeley Data Analytics Stack. Velox is a data management system for facilitating the next steps in real-world, large-scale analytics pipelines: online model management, maintenance, and serving. Velox provides end-user applications and services with a low-latency, intuitive interface to models, transforming the raw statistical models currently trained using existing offline large-scale compute frameworks into full-blown, end-to-end data products capable of recommending products, targeting advertisements, and personalizing web content. To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting). In this paper, we describe the challenges and architectural considerations required to achieve this functionality, including the abilities to span online and offline systems, to adaptively adjust model materialization strategies, and to exploit inherent statistical properties such as model error tolerance, all while operating at "Big Data" scale.


title: Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns

abstract: Abstract Objective To develop a single trial motor imagery (MI) classification strategy for the brain–computer interface (BCI) applications by using time–frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from electroencephalogram (EEG) rhythmic components as the feature description. Methods The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which forms the spatial patterns for a given electrode montage at a time–frequency grid. Time–frequency weights determined by training process are used to synthesize the contributions from the time–frequency domains. Results The present method was tested in nine human subjects performing left or right hand movement imagery tasks. The overall classification accuracies for nine human subjects were about 80% in the 10-fold cross-validation, without rejecting any trials from the dataset. The loci of MI activity were shown in the spatial topography of differential-mode patterns over the sensorimotor area. Conclusions The present method does not contain a priori subject-dependent parameters, and is computationally efficient. The testing results are promising considering the fact that no trials are excluded due to noise or artifact. Significance The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification.


title: Eutectic Ga-In liquid metal based flexible capacitive pressure sensor

abstract: A novel flexible pressure sensor has been successfully developed for detecting varying applied pressures. Polydimethylsiloxane (PDMS) and eutectic gallium indium (EGaIn) based liquid metal, as the conductive electrodes, were used to fabricate the sensor. The sensor was designed with four capacitors (C1, C2, C3 and C4) which were formed by overlapping the liquid metal based electrodes. The capability of the fabricated capacitive pressure sensor was investigated by applying varying pressures. A maximum average capacitance change of 10.14%, 11.56%, 11.57% and 11.82% was obtained for C1, C2, C3 and C4 respectively, when pressures were applied from 0.25 MPa to 1.1 MPa. A sensitivity of 0.11%/MPa and correlation coefficient of 0.9875 was obtained for the fabricated pressure sensor. The results thus demonstrated the potential of using liquid metal based electrodes for the development of flexible pressure sensors.


title: V.: Fully Homomorphic Encryption over the Integers

abstract: We construct a simple fully homomorphic encryption scheme, using only elementary modular arithmetic. We use Gentry’s technique to construct a fully homomorphic scheme from a “bootstrappable” somewhat homomorphic scheme. However, instead of using ideal lattices over a polynomial ring, our bootstrappable encryption scheme merely uses addition and multiplication over the integers. The main appeal of our scheme is the conceptual simplicity. ::: ::: We reduce the security of our scheme to finding an approximate integer gcd – i.e., given a list of integers that are near-multiples of a hidden integer, output that hidden integer. We investigate the hardness of this task, building on earlier work of Howgrave-Graham.


title: An exploration of climate data using complex networks

abstract: To discover patterns in historical data, climate scientists have applied various clustering methods with the goal of identifying regions that share some common climatological behavior. However, past approaches are limited by the fact that they either consider only a single time period (snapshot) of multivariate data, or they consider only a single variable by using the time series data as multi-dimensional feature vector. In both cases, potentially useful information may be lost. Moreover, clusters in high-dimensional data space can be difficult to interpret, prompting the need for a more effective data representation. We address both of these issues by employing a complex network (graph) to represent climate data, a more intuitive model that can be used for analysis while also having a direct mapping to the physical world for interpretation. A cross correlation function is used to weight network edges, thus respecting the temporal nature of the data, and a community detection algorithm identifies multivariate clusters. Examining networks for consecutive periods allows us to study structural changes over time. We show that communities have a climatological interpretation and that disturbances in structure can be an indicator of climate events (or lack thereof). Finally, we discuss how this model can be applied for the discovery of more complex concepts such as unknown teleconnections or the development of multivariate climate indices and predictive insights.


title: Gait Speed and Survival in Older Adults

abstract: Context: Survival estimates help individualize goals of care for geriatric patients, but life tables fail to account for the great variability in survival. Physical performance measures, such as gait speed, might help account for variability, allowing clinicians to make more individualized estimates. Objective: To evaluate the relationship between gait speed and survival. Design, Setting, and Participants: Pooled analysis of 9 cohort studies (collected between 1986 and 2000), using individual data from 34 485 community-dwelling older adults aged 65 years or older with baseline gait speed data, followed up for 6 to 21 years. Participants were a mean (SD) age of 73.5 (5.9) years; 59.6%, women; and 79.8%, white; and had a mean (SD) gait speed of 0.92 (0.27) m/s. Main Outcome Measures: Survival rates and life expectancy. Results: There were 17 528 deaths; the overall 5-year survival rate was 84.8% (confidence interval [CI],79.6%-88.8%)and 10-year survival ratewas59.7%(95%CI,46.5%-70.6%). Gait speed was associated with survival in all studies (pooled hazard ratio per 0.1 m/s, 0.88; 95% CI, 0.87-0.90; P<.001). Survival increased across the full range of gait speeds, with significant increments per 0.1 m/s. At age 75, predicted 10-year survival across the range of gait speeds ranged from 19% to 87% in menand from35% to 91% in women. Predicted survival based on age, sex, and gait speed was as accurate as predicted based on age, sex, use of mobility aids, and self-reported function or as age, sex, chronic conditions, smoking history, blood pressure, body mass index, and hospitalization. Conclusion: In this pooled analysis of individual data from 9 selected cohorts, gait speed was associated with survival in older adults. ©2011 American Medical Association. All rights reserved.


title: A Graph Theoretical Analysis of the Web Using DNS Traffic Traces

abstract: The structure of the web has been extensively studied using HTML-based data. However, the increase in dynamic and personalized content has made the analysis of HTML-based data more difficult. A viable alternative to studying the web using HTML data is to study the web using DNS traffic traces. In this paper, we conduct a preliminary study to investigate the question - What can DNS traffic traces tell us about the structure of the web? In particular, we analyze the connectivity properties of this web graph by using eigenvalue analysis and spectral clustering. We also discuss the implications of this analysis with respect to web development and user privacy.


title: Elements of a spoken language programming interface for robots

abstract: In many settings, such as home care or mobile environments, demands on users' attention, or users' anticipated level of formal training, or other on-site conditions will make standard keyboard-and monitor-based robot programming interfaces impractical. In such cases, a spoken language interface may be preferable. However, the open-ended task of programming a machine is very different from the sort of closed-vocabulary, data-rich applications (e.g. call routing) for which most speaker-independent spoken language interfaces are designed. This paper will describe some of the challenges of designing a spoken language programming interface for robots, and will present an approach that uses these semantic-level resources as extensively as possible in order to address these challenges.


title: Concurrent MAC unit design using VHDL for deep learning networks on FPGA

abstract: Deep neural network algorithms have proven their enormous capabilities in wide range of artificial intelligence applications, specially in Printed/Handwritten text recognition, Multimedia processing, Robotics and many other high end technological trends. The most challenging aspect nowadays is to overcome the extremely computational processing demands in applying such algorithms, especially in real-time systems. Recently, the Field Programmable Gate Array (FPGA) has been considered as one of the optimum hardware accelerator platform for accelerating the deep neural network architectures due to its large adaptability and the high degree of parallelism it offers. In this paper, the proposed 8-bits fixed-point parallel multiply-accumulate (MAC) unit architecture aimed to create a fully-customize MAC unit for the Convolutional Neural Networks (CNN) instead of depending on the conventional DSP blocks and embedded memories units on the FPGAs architecture silicon fabrics. The proposed 8-bits fixed-point parallel multiply-accumulate (MAC) unit architecture is designed using VHDL language and can performs a computational speed up to 4.17 Giga Operation per Second (GOPS) using high-density FPGAs.