title: BDMA for Millimeter-Wave/Terahertz Massive MIMO Transmission with Per-Beam Synchronization

abstract: We propose beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for wideband massive multiple-input multiple-output (MIMO) transmission over millimeter-wave (mmW)/Terahertz (THz) bands. We first introduce a physically motivated beam domain channel model for massive MIMO and demonstrate that the envelopes of the beam domain channel elements tend to be independent of time and frequency when both the numbers of antennas at base station and user terminals (UTs) tend to infinity. Motivated by the derived beam domain channel properties, we then propose PBS for mmW/THz massive MIMO. We show that both the effective delay and Doppler frequency spreads of wideband massive MIMO channels with PBS are reduced by a factor of the number of UT antennas compared with the conventional synchronization approaches. Subsequently, we apply PBS to BDMA, investigate beam scheduling to maximize the achievable ergodic rates for both uplink and downlink BDMA, and develop a greedy beam scheduling algorithm. Simulation results verify the effectiveness of BDMA with PBS for mmW/THz wideband massive MIMO systems in typical mobility scenarios.


Synthetic Occlusion Augmentation for 3 D Human Pose Estimation with Volumetric Heatmaps


title: Environmentalism and the "Ecological Indian" in Avatar : A Visual Analysis

abstract: James Cameron's 2009 blockbuster, Avatar , is a tale of Indigenous resistance to environmental destruction. In the film, Earth moves outward to distant planets in order to satisfy its resource hunger. In their search, Earthlings arrive on Pandora, a biologically rich planet with a diverse and complex ecosystem. In defence of their home planet, the Indigenous people of Pandora (the Na'vi) engage in combat with the earthlings over which aspect of the planet is more important: the life above or the resources below. The environmental message in Avatar is one which promotes balance and harmony between humans and nature. However, this balance is represented by the film’s essentialized Indigenous population. Thus, as a foil for Earth's technology-dependent resource-intensive society, the Na'vi are represented as a stereotypical Indigenous population; they are cast as closer to nature in their role as the "ecological Indian." By using archaic portrayals of Indigenous peoples, the film uses an "Indigenous" voice to propel its environmental message. This article visually analyzes how the film uses, produces, and perpetuates stereotypical representations of Indigenous peoples and how these representations effect and advance the film’s environmental message.


title: Quantitative Phase Imaging and Artificial Intelligence: A Review

abstract: Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI enables the rapid generation of large-scale and uniform-quality imaging data in two, three, and four dimensions. Subsequently, the AI-assisted interrogation of QPI data using data-driven machine learning techniques results in a variety of biomedical applications. Also, machine learning enhances QPI itself. Herein, we review the synergy between QPI and machine learning with a particular focus on deep learning. Furthermore, we provide practical guidelines and perspectives for further development.


title: Malnutrition, body size and proportion

abstract: A number of published and unpublished collections of data on stature, weight, and sitting height are compared with the data collected in Heliconia, Colombia, an area of known protein deficiency. The degree of sexual dimorphism for body size within populations is compared across populations. The relative proportion of sitting height to stature is compared for the Heliconia, U.S., and Nunoa, Peru populations. In these comparisons, it is found that body proportions and sexual dimorphism are relatively constant in various parts of the world, but that this constancy is maintained by differential growth rates in males and females, with males growing more slowly but for a longer period of time under stress. The probable influence of other factors, including sociological and psychological ones is pointed out, as is the possible role of selection producing some genetic differences in body size.


title: Independent voltage outputs control for VIENNA rectifier considering multiple loads situations

abstract: The VIENNA rectifiers have advantages of high efficiency as well as low output harmonics and are widely utilized in power conversion system when dc power sources are needed for supplying dc loads. VIENNA rectifiers based on three-phase/level can provide two voltage outputs with a neutral line at relatively low costs. However, total harmonic distortion (THD) of input current deteriorates seriously when unbalanced voltages occur. In addition, voltage outputs depend on system parameters, especially multiple loads. Therefore, unbalance output voltage controller and modified carrier-based pulse-width modulation (CBPWM) are proposed in this paper to solve the above problems. Unbalanced output voltage controller is designed based on average model considering independent output voltage and loads conditions. Meanwhile, reference voltages are modified according to different neutral point voltage conditions. The simulation and experimental results are presented to verify the proposed method.


title: Combinatorial Multi-Armed Bandits with Filtered Feedback

abstract: Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class of feedback, filtered semibandit feedback. In a CMAB problem an agent pulls a combination of arms from a set $\{1,...,k\}$ in each round, generating random outcomes from probability distributions associated with these arms and receiving an overall reward. Under semibandit feedback it is assumed that the random outcomes generated are all observed. Filtered semibandit feedback allows the outcomes that are observed to be sampled from a second distribution conditioned on the initial random outcomes. This feedback mechanism is valuable as it allows CMAB methods to be applied to sequential search and detection problems where combinatorial actions are made, but the true rewards (number of objects of interest appearing in the round) are not observed, rather a filtered reward (the number of objects the searcher successfully finds, which must by definition be less than the number that appear). We present an upper confidence bound type algorithm, Robust-F-CUCB, and associated regret bound of order $\mathcal{O}(\ln(n))$ to balance exploration and exploitation in the face of both filtering of reward and heavy tailed reward distributions.


title: The Protruding Ear: Cosmetic and Reconstruction

abstract: Ear prominence is a relatively common cosmetic deformity with no associated functional deficits, but with profound psychosocial impact, especially in young patients. Protruding ears in children have propagated surgical advances that incorporate reconstructive techniques. Here we outline a systematic framework to evaluate the protruding ear and present various reconstructive surgical options for correction. Both cosmetic and reconstructive perspectives should be entertained when addressing this anatomical deformity.


title: Working conditions of animators: The real face of the Japanese animation industry

abstract: ABSTRACTJapan is recognized as the leading country in the global animation industry. Nevertheless, the real circumstances behind the scenes have not been fully unveiled to a wider audience. In the past half century, Japanese animation has developed without government support. However, now it finds itself at a crossroads. The Japanese Animation Creators Association investigated animators' working conditions in 2008 as part of the first assessment of this field and revealed some important facts. In addition to the industry's heavy reliance on outsourcing to countries such as China and South Korea, the assessment finds that young animators face poor working conditions. This article demonstrates the main points arising from these facts, and outlines the recent approach of the Japanese government in response to the problems.


title: Jigg: A Framework for an Easy Natural Language Processing Pipeline

abstract: We present Jigg, a Scala (or JVMbased) NLP annotation pipeline framework, which is easy to use and is extensible. Jigg supports a very simple interface similar to Stanford CoreNLP, the most successful NLP pipeline toolkit, but has more flexibility to adapt to new types of annotation. On this framework, system developers can easily integrate their downstream system into a NLP pipeline from a raw text by just preparing a wrapper of it.


title: Accelerating Habanero-Java programs with OpenCL generation

abstract: The initial wave of programming models for general-purpose computing on GPUs, led by CUDA and OpenCL, has provided experts with low-level constructs to obtain significant performance and energy improvements on GPUs. However, these programming models are characterized by a challenging learning curve for non-experts due to their complex and low-level APIs. Looking to the future, improving the accessibility of GPUs and accelerators for mainstream software developers is crucial to bringing the benefits of these heterogeneous architectures to a broader set of application domains. A key challenge in doing so is that mainstream developers are accustomed to working with high-level managed languages, such as Java, rather than lower-level native languages such as C, CUDA, and OpenCL. The OpenCL standard enables portable execution of SIMD kernels across a wide range of platforms, including multi-core CPUs, many-core GPUs, and FPGAs. However, using OpenCL from Java to program multi-architecture systems is difficult and error-prone. Programmers are required to explicitly perform a number of low-level operations, such as (1) managing data transfers between the host system and the GPU, (2) writing kernels in the OpenCL kernel language, (3) compiling kernels & performing other OpenCL initialization, and (4) using the Java Native Interface (JNI) to access the C/C++ APIs for OpenCL. In this paper, we present compile-time and run-time techniques for accelerating programs written in Java using automatic generation of OpenCL as a foundation. Our approach includes (1) automatic generation of OpenCL kernels and JNI glue code from a parallel-for construct (forall) available in the Habanero-Java (HJ) language, (2) leveraging HJ's array view language construct to efficiently support rectangular, multi-dimensional arrays on OpenCL devices, and (3) implementing HJ's phaser (next) construct for all-to-all barrier synchronization in automatically generated OpenCL kernels. Our approach is named HJ-OpenCL. Contrasting with past approaches to generating CUDA or OpenCL from high-level languages, the HJ-OpenCL approach helps the programmer preserve Java exception semantics by generating both exception-safe and unsafe code regions. The execution of one or the other is selected at runtime based on the safe language construct introduced in this paper. We use a set of ten Java benchmarks to evaluate our approach, and observe performance improvements due to both native OpenCL execution and parallelism. On an AMD APU, our results show speedups of up to 36.7× relative to sequential Java when executing on the host 4-core CPU, and of up to 55.0x on the integrated GPU. For a system with an Intel Xeon CPU and a discrete NVIDIA Fermi GPU, the speedups relative to sequential Java are 35.7× for the 12-core CPU and 324.0× for the GPU. Further, we find that different applications perform optimally in JVM execution, in OpenCL CPU execution, and in OpenCL GPU execution. The language features, compiler extensions, and runtime extensions included in this work enable portability, rapid prototyping, and transparent execution of JVM applications across all OpenCL platforms.


From conceptualization to operationalization of mindfulness.


title: Automatic soccer field line recognition by minimum information

abstract: Automatic analysis in soccer scenes is still a difficult task in the absence of soccer field information. The first and most important step in almost all analysis, is soccer field line recognition and homography extraction. The aim of this paper is introducing a novel approach for automatic detection and recognition of soccer field lines and arcs by minimal information. A simple camera model and perspective map is assumed to reduce unknown parameters. An accurate method is utilized for detecting line pixels. The side of playfield area is determined based on the orientation of lines and arcs. Based on the detected playfield area side, an initial perspective map is obtained. An optimization algorithm then adjusts the parameters of perspective transform and camera. The proposed method needs only some minimal information in theory and practice. It is applied to some typical soccer videos. The achieved results demonstrate its robustness and accuracy.


title: Forecasting Intraday stock price trends with text mining techniques

abstract: In this paper, we describe NewsCATS (news categorization and trading system), a system implemented to predict stock price trends for the time immediately after the publication of press releases. NewsCATS consists mainly of three components. The first component retrieves relevant information from press releases through the application of text preprocessing techniques. The second component sorts the press releases into predefined categories. Finally, appropriate trading strategies are derived by the third component by means of the earlier categorization. The findings indicate that a categorization of press releases is able to provide additional information that can be used to forecast stock price trends, but that an adequate trading strategy is essential for the results of the categorization to be fully exploited.


title: Iranian Grapevine Rootstocks and Hormonal Effects on Graft Union, Growth and Antioxidant Responses of Asgari Seedless Grape

abstract: Abstract This research investigated physiological and biochemical changes at rootstock–scion union of graft combinations between Iranian rootstocks and Asgari scion in response to NAA and BAP application. Grafting cuts of scion and rootstock were dipped into NAA (0, 200 and 400 mg⋅L−1) and BAP (0, 250 and 500 mg⋅L−1) solutions for 30 min, immediately after the cuttings were grafted using the wedge grafting technique. The highest successful grafting rate (82%) was recorded with Asgari–Asgari combination treated with 200 mg⋅L−1 NAA + 500 mg⋅L−1 BAP. Treating the graft cut–surfaces with 200 mg⋅L−1 NAA + 500 mg⋅L−1 BAP caused the lowest H2O2 and MDA contents. Among all the graft combinations, the highest increase in chlorophyll content with a high photosynthesis efficiency occurred in Asgari–Shahani when treated with 200 mg⋅L−1 NAA + 500 mg⋅L−1 BAP. The graft union formation in the presence of 200 mg⋅L−1 NAA + 500 mg⋅L−1 BAP application enhanced CAT and POX activities. In conclusion, treatment of graft zone with 200 mg⋅L−1 NAA + 500 mg⋅L−1 BAP enhanced antioxidant activities and reduced lipid peroxidation in scion leaves. However, several grapevine rootstocks tested in our research were also impacting the scion performance.


title: Lightweight security algorithm for low power IoT devices

abstract: In today's technology, ever increasing number of electronics applications require secure communication, for example the Internet of things devices. Elliptic Curve Diffie Hellman (EC-DH) Algorithm has emerged as an attractive and effective public-key cryptosystem. Elliptic curves are widely used in various key exchange techniques that include the Diffie-Hellman Key agreement scheme. When contrasted with conventional cryptosystems like RSA, ECC offers equivalent security with smaller key sizes, which results in lower power consumption, speedier calculations, and also lower memory and transmission capacity (bandwidth) reserve. This is particularly valid and helpful for applications like IoT gadgets, which are regularly constrained regarding their CPU processing speed, power, and area. This work includes the software and hardware implementation of Diffie-Hellman, Elliptic Curve Diffie-Hellman (ECDH) Key agreement algorithm, and RSA algorithm. The proposed work also involves analysis of power, performance, area, and their comparisons thereof. The comparison is based on metrics obtained, after implementing the algorithms in synopsys using 90 nm UMC Faraday library. The ECDH algorithm is found to be better than others as far as power and area are concerned.


Convolutional neural networks: an illustration in TensorFlow


title: Toward a Clinical Model of Suicidal Behavior in Psychiatric Patients

abstract: OBJECTIVE: Risk factors for suicide attempts have rarely been studied comprehensively in more than one psychiatric disorder, preventing estimation of the relative importance and the generalizability of different putative risk factors across psychiatric diagnoses. The authors conducted a study of suicide attempts in patients with mood disorders, psychoses, and other diagnoses. Their goal was to determine the generalizability and relative importance of risk factors for suicidal acts across diagnostic boundaries and to develop a hypothetical, explanatory, and predictive model of suicidal behavior that can subsequently be tested in a prospective study. METHOD: Following admission to a university psychiatric hospital, 347 consecutive patients who were 14–72 years old (51% were male and 68% were Caucasian) were recruited for study. Structured clinical interviews generated axis I and axis II diagnoses. Lifetime suicidal acts, traits of aggression and impulsivity, objective and subjective severity of acute psycho...


title: The copula echo state network

abstract: Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple, computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. This paper studies the formulation of a class of copula-based semiparametric models for sequential data modeling, characterized by nonparametric marginal distributions modeled by postulating suitable echo state networks, and parametric copula functions that help capture all the scale-free temporal dependence of the modeled processes. We provide a simple algorithm for the data-driven estimation of the marginal distribution and the copula parameters of our model under the maximum-likelihood framework. We exhibit the merits of our approach by considering a number of applications; as we show, our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs, without significant compromises in the algorithm's computational efficiency.


title: Outcomes of Congenital Diaphragmatic Hernia: A Population-Based Study in Western Australia

abstract: Objectives. There have been many recent reports of improved survival rates for congenital diaphragmatic hernia (CDH), largely derived from institution-based data. These are often flawed by case selection bias. The objectives of this study were to document the true incidence, management, and outcomes of CDH in a geographically defined population over a 12-year period and to determine the changing trends in these over time. We also sought to ascertain the prenatal and postnatal factors associated with morbidity and death among these infants. Methods. A retrospective study of all cases of CDH in Western Australia from 1991 to 2002 was conducted. Cases were identified from 5 independent databases within the Western Australian health network, including the Western Australian Birth Defects Registry. All fetuses and neonates diagnosed with CDH in Western Australia during this period were identified, including miscarriages, stillbirths, and terminations of pregnancies in which a diagnosis of fetal CDH had been made, as well as those diagnosed postnatally. Cases not known to involve CDH until diagnosis at autopsy were also included. Infants with diaphragmatic eventration were excluded from the study. Detailed information was obtained from review of maternal and infant medical records. Results. One hundred sixteen cases of CDH were identified. Of these, 71 (61%) infants were born alive and 37 survived beyond 1 year of age (52% of live-born infants, 32% of all cases of CDH). Pregnancies involving 38 (33%) fetuses were terminated electively, 4 (3%) fetuses were aborted spontaneously, and 3 (3%) fetuses were stillborn. Another major congenital anomaly was present in 54 (47%) cases. Twenty-one (18%) cases had other anomalies that were likely to be fatal. Of all cases with an additional major anomaly, 42 (78%) died. Twenty-seven (71%) of 38 fetuses for whom the pregnancy was terminated had another major anomaly. Twenty-three (32%) live-born infants had another major anomaly (4 of which were considered fatal conditions); however, this did not affect their survival rates. Fifty-three percent of cases were diagnosed prenatally, and 49% of these pregnancies were then terminated. Of live-born infants with prenatally diagnosed CDH, 10 (33%) survived beyond 1 year of age. The gestational age at diagnosis did not affect the survival rate for live-born infants. Postnatal diagnosis occurred in 55 (47%) cases. Of these, 41 (74%) case subjects were born alive and diagnosed on clinical grounds after birth. In the remaining 14 cases, the diagnosis was made in postmortem examinations of fetuses from pregnancies that were terminated for other reasons (8 cases) or after spontaneous abortion or stillbirth (5 cases). Significant differences were found between prenatally and postnatally diagnosed live-born infants. Among live-born infants, prenatal diagnosis was associated with a significantly reduced survival rate (33%, compared with 66% for postnatally diagnosed infants). Prenatally diagnosed live-born infants were of lower birth weight and were born at an earlier gestational age. There was no statistically significant difference between the 2 groups in the onset of labor (spontaneous or induced) or in the rate of elective cesarean sections. Prenatally diagnosed live-born infants were more likely to be delivered in a tertiary perinatal center and were intubated more commonly at delivery. No difference was found in the Apgar scores at either 1 or 5 minutes between the groups. Of 71 live-born infants, 37 (52%) survived to 1 year of age. The majority of deaths occurred within the first 7 days of life (44%). Preoperative air leaks occurred for 16 (22%) infants, of whom 14 (88%) died. Factors found to predict death of live-born infants included prenatal diagnosis, right-sided hernia, major air leak, earlier gestational age at birth, lower birth weight, and lower Apgar scores at 1 and 5 minutes. Over the course of the decade, there were significant increases in the proportion of cases in which the diagnosis of CDH was made with prenatal ultrasonography and in the number of live-born infants born at the tertiary perinatal center. The mortality rate for all cases, the mortality rate for live-born infants, and the proportion of pregnancies involving prenatally diagnosed cases that were terminated electively were all greater in the later epoch but not significantly so. Conclusions. This was a comprehensive, population-based study of CDH, with full case ascertainment, large sample size, and complete outcome data for all cases. The majority of published studies of CDH examined specific patient populations, such as neonates referred to tertiary pediatric surgical centers. Invariably, those studies failed to detect the demise of cases with CDH before arrival at the referral center, whether through termination of pregnancy, in utero fetal demise, or postnatal death occurring before transfer. Exclusion of these cases from calculations of mortality rates results in significant case selection bias. In our study, 35% of live-born infants died before referral or transport. The population of infants reaching the tertiary surgical center represented only 40% of the total cases of CDH. Wide variations in reported survival rates occur throughout the literature. These differences reflect the influence of this case selection bias, as well as variable referral policies and management practices. For our study population, survival rates differed vastly depending on the subgroup analyzed. Ninety-two percent of postoperative infants survived beyond 1 year of age, as did 80% of infants who reached the surgical referral center. However, only 52% of live-born infants, 32% of all cases, and 16% of all prenatally diagnosed cases survived. Therefore, the overall mortality rate for this condition remains high, despite increased prenatal detection, transfer to tertiary institutions for delivery, and advances in neonatal care, and is influenced significantly by the rate of prenatal termination. In our study, 33% of all cases of CDH and 49% of prenatally diagnosed fetuses underwent elective termination of pregnancy. This large number of fetal terminations confounds the accurate assessment of the true outcomes of this condition.


title: OR Forum - An Algorithmic Approach to Linear Regression

abstract: Linear regression models are traditionally built through trial and error to balance many competing goals such as predictive power, interpretability, significance, robustness to error in data, and sparsity, among others. This problem lends itself naturally to a mixed integer quadratic optimization (MIQO) approach but has not been modeled this way because of the belief in the statistics community that MIQO is intractable for large scale problems. However, in the last 25 years (1991–2015), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 450 billion factor speedup in solving mixed integer optimization problems. We present an MIQO-based approach for designing high quality linear regression models that explicitly addresses various competing objectives and demonstrate the effectiveness of our approach on both real and synthetic data sets.


title: Clinical use of MRI for the evaluation of acute appendicitis during pregnancy

abstract: Purpose ::: The purpose of this study was to determine the diagnostic accuracy of MRI for detecting acute appendicitis in pregnancy in a multi-institution study involving general body MR readers with no specific expertise in MR imaging of the pregnant patient.


CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons


title: Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices

abstract: To verify cluster separation in high-dimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between these visual encoding choices, we conducted an empirical data study in which two human coders manually inspected a broad set of 816 scatterplots derived from 75 datasets, 4 DR techniques, and the 3 previously mentioned scatterplot techniques. Each coder scored all color-coded classes in each scatterplot in terms of their separability from other classes. We analyze the resulting quantitative data with a heatmap approach, and qualitatively discuss interesting scatterplot examples. Our findings reveal that 2D scatterplots are often 'good enough', that is, neither SPLOM nor interactive 3D adds notably more cluster separability with the chosen DR technique. If 2D is not good enough, the most promising approach is to use an alternative DR technique in 2D. Beyond that, SPLOM occasionally adds additional value, and interactive 3D rarely helps but often hurts in terms of poorer class separation and usability. We summarize these results as a workflow model and implications for design. Our results offer guidance to analysts during the DR exploration process.


title: DeepText: A new approach for text proposal generation and text detection in natural images

abstract: In this paper, we develop a new approach called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the novel inception region proposal network (Inception-RPN), which slides an inception network with multi-scale windows over the top of convolutional feature maps and associates a set of text characteristic prior bounding boxes with each sliding position to generate high recall word region proposals. Next, we present a powerful text detection network that embeds ambiguous text category (ATC) information and multi-level region-of-interest pooling (MLRP) for text and non-text classification and accurate localization refinement. Our approach achieves an F-measure of 0.83 and 0.85 on the ICDAR 2011 and 2013 robust text detection benchmarks, outperforming previous state-of-the-art results.


title: Caries detection and diagnosis: Novel technologies

abstract: Recent years have seen an increase in research activity surrounding diagnostic methods, particularly in the assessment of early caries lesions. The drive for this has come from two disparate directions. The first is from the dentifrice industry who are keen to develop techniques that would permit caries clinical trials (CCTs) to be reduced in duration and subject numbers to permit the investigation of novel new anti-caries actives. The second is from clinicians who, armed with the therapies to remineralise early lesions are now seeking methods to reliably detect such demineralised areas and implement true preventative dentistry. This review examines novel technologies and the research supporting their use. Techniques based on visual, optical, radiographic and some emerging technologies are discussed. Each have their benefits although systems based on the auto-fluorescence (such as QLF) of teeth and electrical resistance (such as ECM) seem to offer the most hope for achieving reliable, accurate detection of the earliest stages of enamel demineralisation.


title: Wellness as Fairness

abstract: I argue that distinct conditions of justice lead to diverse wellness outcomes through a series of psychosocial processes. Optimal conditions of justice, suboptimal conditions of justice, vulnerable conditions of injustice, and persisting conditions of injustice lead to thriving, coping, confronting, and suffering, respectively. The processes that mediate between optimal conditions of justice and thriving include the promotion of responsive conditions, the prevention of threats, individual pursuit, and avoidance of comparisons. The mechanisms that mediate between suboptimal conditions of justice and coping include resilience, adaptation, compensation, and downward comparisons. Critical experiences, critical consciousness, critical action, and righteous comparisons mediate between vulnerable conditions of injustice and confrontation with the system. Oppression, internalization, helplessness, and upward comparisons mediate between persisting conditions of injustice and suffering. These psychosocial processes operate within and across personal, interpersonal, organizational and community contexts. Different types of justice are hypothesized to influence well-being within each context. Intrapersonal injustice operates at the personal level, whereas distributive, procedural, relational, and developmental justice impact interpersonal well-being. At the organizational level, distributive, procedural, relational and informational justice influence well-being. Finally, at the community level, distributive, procedural, retributive, and cultural justice support community wellness. Data from a variety of sources support the suggested connections between justice and well-being.


title: Application of DRFM in ECM for pulse type radar

abstract: In this paper, we develop a digital radio frequency memory (DRFM) which samples and quantizes (A/D conversion) an incoming pulse-typed radar signal, stores it and produces (D/A conversion) a jamming signal from the sampled and stored radar signal. Electronic countermeasure (ECM) techniques against radar can be enhanced by using DRFM to simulate the radar signal for subsequent re-transmission to confuse radar at any distance. This paper presents the principle of DRFM and its application in ECM against pulse type radar.


title: Center-fed traveling-wave microstrip array antenna using elliptically-shaped radiating elements in quasi millimeter-wave band

abstract: A center-feeding travelling-wave microstrip array antenna is designed to realize a high-gain and broadband two-dimensional array antenna in quasi millimeter-wave band. Microstrip antennas suffer from high feeding-loss in high frequency band. Therefore, center-feeding and travelling-wave operation are applied. To reduce manufacturing errors in etching process, we propose elliptically shaped radiating elements directly connected to the feeding line. The simulated performance of the designed two-dimensional microstrip planar array with 45-degree polarization in quasi millimeter-wave band is evaluated in this paper.


title: Cu Pillar and μ-bump electromigration reliability and comparison with high pb, SnPb, and SnAg bumps

abstract: Failures due to Electromigration (EM) in flip-chip bumps have emerged as a major reliability concern due to potential elimination of Pb from flip-chip bumps and a continuous drive to increased IO density resulting in a reduction of bump pitch and size. Additionally, the rapid development and implementation of 3D IC structures introducing new interconnects (μ-bumps, RDL, microvias, and TSVs) at much finer geometries, raises concerns about electromigration and current carrying capacity of these interconnects. This paper presents the results of multiple EM studies on Cu Pillar, High Pb, SnAg, eutectic SnPb Flip Chip bumps and μ-bumps. A special test vehicle was designed to get a head-to-head comparison of Cu Pillar EM with that of solder bumps. Tests are being conducted using three current levels and three temperatures to estimate Black's Equation parameters. A separate test vehicle is also being tested using 5 combinations of current and temperature to estimate the current carrying capacity of Cu-SnAg-Cu μ-bumps of 25um diameter. More than 8000 hours of testing is completed on flip chip solder bump and Cu Pillar, showing Cu Pillars as having the best reliability amongst the four bump metallurgies. The worst reliability was observed for High Pb bumps followed by eutectic SnPb eut and SnAg bumps. The Cu-SnAg-Cu μ-bump structure has been tested for 5500+ hours without any failures. The paper provides the detailed test matrix, failure data, failure analysis, and an estimation of Black's Equation parameters for some of the above configurations on test.


title: DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation

abstract: Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.


title: Ethnography and Emotions: Cultural Intelligence Applied to Motherhood Migration Process

abstract: Abstract In the first part, the theoretical basis of the theory of emotional intelligence and convenience intercultural training for immigrant women are exposed and that they can better adapt to the environment from a general and theoretical point of view. Then we serve authors who since the 90s as P. Fernandez Berrocal (1997) , Fernandez et al (1997) investigating the relationship between culture and emotion, that is, how the assimilation of a culture allows individuals some emotional filters to interpret reality and how certain cultures even allow emotional control (Mesquita, 2003) by supernatural and mystical references. Then we attend the practical part following the detailed exposition of a unique case in terms of a qualitative field study with immigrant Muslim women to Spain. This case, called instrumentally unique case, is an exemplification of how a culture can lead itself own emotional complication for individuals. This makes more sense to have the convenience of a basic emotional education for immigrants and provide them with other references about the most rational reality to suit her motherhood, to new country women, etc. without losing or threatening the essence of their culture.


title: ORIGINAL ARTICLE Characteristics of patients with chronic pain accessing treatment with medical cannabis in Washington State

abstract: OBJECTIVES ::: This study was conducted to better understand the characteristics of chronic pain patients seeking treatment with medicinal cannabis (MC). ::: ::: ::: DESIGN ::: Retrospective chart reviews of 139 patients (87 males, median age 47 years; 52 females, median age 48 years); all were legally qualified for MC use in Washington State. ::: ::: ::: SETTING ::: Regional pain clinic staffed by university faculty. ::: ::: ::: PARTICIPANTS ::: ::: ::: ::: INCLUSION CRITERIA ::: age 18 years and older; having legally accessed MC treatment, with valid documentation in their medical records. All data were de-identified. ::: ::: ::: MAIN OUTCOME MEASURES ::: Records were scored for multiple indicators, including time since initial MC authorization, qualifying condition(s), McGill Pain score, functional status, use of other analgesic modalities, including opioids, and patterns of use over time. ::: ::: ::: RESULTS ::: Of 139 patients, 15 (11 percent) had prior authorizations for MC before seeking care in this clinic. The sample contained 236.4 patient-years of authorized MC use. Time of authorized use ranged from 11 days to 8.31 years (median of 1.12 years). Most patients were male (63 percent) yet female patients averaged 0.18 years longer authorized use. There were no other gender-specific trends or factors. Most patients (n = 123, 88 percent) had more than one pain syndrome present. Myofascial pain syndrome was the most common diagnosis (n = 114, 82 percent), followed by neuropathic pain (n = 89, 64 percent), discogenic back pain (n = 72, 51.7 percent), and osteoarthritis (n = 37, 26.6 percent). Other diagnoses included diabetic neuropathy, central pain syndrome, phantom pain, spinal cord injury, fibromyalgia, rheumatoid arthritis, HIV neuropathy, visceral pain, and malignant pain. In 51 (37 percent) patients, there were documented instances of major hurdles related to accessing MC, including prior physicians unwilling to authorize use, legal problems related to MC use, and difficulties in finding an affordable and consistent supply of MC. ::: ::: ::: CONCLUSIONS ::: Data indicate that males and females access MC at approximately the same rate, with similar median authorization times. Although the majority of patient records documented significant symptom alleviation with MC, major treatment access and delivery barriers remain.


title: Use of fuzzy logic and wordnet for improving performance of extractive automatic text summarization

abstract: Text Summarization produces a shorter version of large text documents by selecting most relevant information. Text summarization systems are of two types: extractive and abstractive. This paper focuses on extractive text summarization. In extractive text summarization, important sentences are selected based on certain important features. The importance of some extractive features is more than the some other features, so they should have the balance weight in computations. The purpose of this paper is to use fuzzy logic and wordnet synonyms to handle the issue of ambiguity and imprecise values with the traditional two value or multi-value logic and to consider the semantics of the text. Three different methods: fuzzy logic based method, bushy path method, and wordnet synonyms method are used to generate 3 summaries. Final summary is generated by selecting common sentences from all the 3 summaries and from rest of the sentences in union of all summaries, selection is done based on sentence location. The proposed methodology is compared with three individual methods i.e. fuzzy logic based summarizer, bushy path summarizer, and wordnet synonyms summarizer by evaluating the performance of each on 95 documents from standard DUC 2002 dataset using ROUGE evaluation metrics. The analysis shows that the proposed method gives better average precision, recall, and f-measure.


Deep Air : Forecasting Air Pollution in Beijing , China


title: Neural mechanisms of brain–computer interface control

abstract: Abstract Brain–computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner. Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was significantly correlated with accuracy in the EEG-BCI task (r = 0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r = 0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session. The ability to regulate behavior and the brain through learning mechanisms involving imagery such as required to control a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.


title: Morality and nuclear energy: perceptions of risks and benefits, personal norms, and willingness to take action related to nuclear energy.

abstract: We examined factors underlying people's willingness to take action in favor of or against nuclear energy from a moral perspective. We conducted a questionnaire study among a sample of the Dutch population (N = 123). As expected, perceptions of risks and benefits were related to personal norms (PN), that is, feelings of moral obligation toward taking action in favor of or against nuclear energy. In turn, PN predicted willingness to take action. Furthermore, PN mediated the relationships between perceptions of risk and benefits and willingness to take action. In line with our hypothesis, beliefs about the risks and benefits of nuclear energy were less powerful in explaining PN for supporters compared to PN of opponents. Also, beliefs on risks and benefits and PN explained significantly more variance in willingness to take action of opponents than of supporters. Our results suggest that a moral framework is useful to explain willingness to take action in favor of and against nuclear energy, and that people are more likely to protest in favor of or against nuclear energy when PN are strong.


Microbiological Quality of Poultry Feed and Ingredients


title: Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks

abstract: Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusion based "guilt-by-association" and graph-theoretic methods have had success in inferring gene functions. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. In this paper, we introduce diffusion component analysis (DCA), a framework that plugs in a diffusion model and learns a low-dimensional vector representation of each node to encode the topological properties of a network. As a proof of concept, we demonstrate DCA's substantial improvement over state-of-the-art diffusion-based approaches in predicting protein function from molecular interaction networks. Moreover, our DCA framework can integrate multiple networks from heterogeneous sources, consisting of genomic information, biochemical experiments and other resources, to even further improve function prediction. Yet another layer of performance gain is achieved by integrating the DCA framework with support vector machines that take our node vector representations as features. Overall, our DCA framework provides a novel representation of nodes in a network that can be used as a plug-in architecture to other machine learning algorithms to decipher topological properties of and obtain novel insights into interactomes.


title: SentEval: An Evaluation Toolkit for Universal Sentence Representations

abstract: We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders. The aim is to provide a fairer, less cumbersome and more centralized way for evaluating sentence representations.


title: Image segmentation based on histogram of depth and an application in driver distraction detection

abstract: This study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected histogram region. To demonstrate the effectiveness of the proposed method, an application in driver distraction detection was introduced. After successfully extracting the driver's position inside the car, we came up with a simple solution to track the driver motion. With the analysis of the difference between initial and current frame, a change of cluster position or depth value in the interested region, which cross the preset threshold, is considered as a distracted activity. The experiment results demonstrated the success of the algorithm in detecting typical distracted driving activities such as using phone for calling or texting, adjusting internal devices and drinking in real time.


title: Multileaved Comparisons for Fast Online Evaluation

abstract: Evaluation methods for information retrieval systems come in three types: offline evaluation, using static data sets annotated for relevance by human judges; user studies, usually conducted in a lab-based setting; and online evaluation, using implicit signals such as clicks from actual users. For the latter, preferences between rankers are typically inferred from implicit signals via interleaved comparison methods, which combine a pair of rankings and display the result to the user. We propose a new approach to online evaluation called multileaved comparisons that is useful in the prevalent case where designers are interested in the relative performance of more than two rankers. Rather than combining only a pair of rankings, multileaved comparisons combine an arbitrary number of rankings. The resulting user clicks then give feedback about how all these rankings compare to each other. We propose two specific multileaved comparison methods. The first, called team draft multileave, is an extension of team draft interleave. The second, called optimized multileave, is an extension of optimized interleave and is designed to handle cases where a large number of rankers must be multileaved. We present experimental results that demonstrate that both team draft multileave and optimized multileave can accurately determine all pairwise preferences among a set of rankers using far less data than the interleaving methods that they extend.


title: Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking

abstract: Visual tracking using multiple features has been proved as a robust approach because features could complement each other. Since different types of variations such as illumination, occlusion, and pose may occur in a video sequence, especially long sequence videos, how to properly select and fuse appropriate features has become one of the key problems in this approach. To address this issue, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. In order to capture the non-linear similarity of features, we extend the proposed method into a general kernelized framework, which is able to perform feature fusion on various kernel spaces. As a result, robust tracking performance is obtained. Both the qualitative and quantitative experimental results on publicly available videos show that the proposed method outperforms both sparse representation-based and fusion based-trackers.


title: CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data

abstract: In recent years, bike-sharing systems have been deployed in many cities, which provide an economical lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions for mobile apps development. Therefore, it is significant to predict and compare the popularity of different bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps. Moreover, we conduct experiments on real-world datasets collected from 11 app stores and Sina Weibo, and the experiments demonstrate the effectiveness of our approach.


title: Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?

abstract: Machine learning seems to be eating the world with a new breed of high-value data-driven applications in image analysis, search, voice recognition, mobile, and office productivity products. To paraphrase Mike Stonebraker, machine learning is no longer a zero-billion-dollar business. As the home of high-value, data-driven applications for over four decades, a natural question for database researchers to ask is: what role should the database community play in these new data-driven machine-learning-based applications?


title: CDTS: Collaborative Detection, Tracking, and Segmentation for Online Multiple Object Segmentation in Videos

abstract: A novel online algorithm to segment multiple objects in a video sequence is proposed in this work. We develop the collaborative detection, tracking, and segmentation (CDTS) technique to extract multiple segment tracks accurately. First, we jointly use object detector and tracker to generate multiple bounding box tracks for objects. Second, we transform each bounding box into a pixel-wise segment, by employing the alternate shrinking and expansion (ASE) segmentation. Third, we refine the segment tracks, by detecting object disappearance and reappearance cases and merging overlapping segment tracks. Experimental results show that the proposed algorithm significantly surpasses the state-of-the-art conventional algorithms on benchmark datasets.


Polymorphic Encryption and Pseudonymisation for Personalised Healthcare


title: DESIGN OF A BIOMIMETIC ROBOTIC OCTOPUS ARM

abstract: This paper reports the rationale and design of a robotic arm, as inspired by an octopus arm. The octopus arm shows peculiar features, such as the ability to bend in all directions, to produce fast elongations, and to vary its stiffness. The octopus achieves these unique motor skills, thanks to its peculiar muscular structure, named muscular hydrostat. Different muscles arranged on orthogonal planes generate an antagonistic action on each other in the muscular hydrostat, which does not change its volume during muscle contractions, and allow bending and elongation of the arm and stiffness variation. By drawing inspiration from natural skills of octopus, and by analysing the geometry and mechanics of the muscular structure of its arm, we propose the design of a robot arm consisting of an artificial muscular hydrostat structure, which is completely soft and compliant, but also able to stiffen. In this paper, we discuss the design criteria of the robotic arm and how this design and the special arrangement of its muscular structure may bring the building of a robotic arm into being, by showing the results obtained by mathematical models and prototypical mock-ups.


title: Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

abstract: Neural networks were widely used for quantitative structure–activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck’s drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our ...


title: Chemical Reaction Optimization for Heterogeneous Computing Environments

abstract: Task scheduling has been proven to be NP-hard problem and we can usually approximate the best solutions with some classical algorithm, such as Heterogeneous Earliest Finish Time(HEFT), Genetic Algorithm. However, the huge types of scheduling problems and the small number of generally acknowledged methods mean that more methods are needed. In this paper, we propose a new method to schedule the execution of a group of dependent tasks for heterogeneous computing environments. The algorithm consists of two elements: An intelligent approach to assign the execution orders of tasks by task level, and an allocation algorithm based on chemical-reaction-inspired metaheuristic called Chemical Reaction Optimization (CRO) to map processors to tasks. The experiments show that the CRO-based algorithm performs consistently better than HEFT and Critical Path On a Processor (CPOP) without incurring much computational cost. Multiple runs of the algorithm can further improve the search result.


title: Evaluating approaches for supervised semantic labeling

abstract: Relational data sources are still one of the most popular ways to store enterprise or Web data, however, the issue with relational schema is the lack of a well-defined semantic description. A common ontology provides a way to represent the meaning of a relational schema and can facilitate the integration of heterogeneous data sources within a domain. Semantic labeling is achieved by mapping attributes from the data sources to the classes and properties in the ontology. We formulate this problem as a multi-class classification problem where previously labeled data sources are used to learn rules for labeling new data sources. The majority of existing approaches for semantic labeling have focused on data integration challenges such as naming conflicts and semantic heterogeneity. In addition, machine learning approaches typically have issues around class imbalance, lack of labeled instances and relative importance of attributes. To address these issues, we develop a new machine learning model with engineered features as well as two deep learning models which do not require extensive feature engineering. We evaluate our new approaches with the state-of-the-art.


title: Smartwatch-based sitting detection with human activity recognition for office workers syndrome

abstract: The main cause of Office Workers Syndrome (OWS) is habitual unhealthy behavior. One such example is the habit of siting for long periods at a computer. It has recently become simple to study this kind of human activity by using smartwatch sensors, and as a result, HAR (human activity recognition) has become a field attracting considerable interest. Smartwatch accelerometers have been especially useful in HAR, while gyroscopes are also helpful, especially when combined with the accelerometer. This study therefore suggests the use of HAR to detect sitting in order to identify the threat of OWS through the use of data from the accelerometer and gyroscope in a smartwatch. Two ensemble learning approaches have therefore been examined in order to establish their capability in recognizing periods of sitting. The results reveal that the accelerometer and gyroscope are complementary in their application and using them in combination thus improves the accuracy of recognition. The ensemble learning-based techniques were able to achieve an accuracy level of 93.57% for activity recognition when detecting sitting.


title: Encoding User as More Than the Sum of Their Parts: Recurrent Neural Networks and Word Embedding for People-to-people Recommendation

abstract: Neural networks and word embeddings are powerful tools to capture latent factors. These tools can provide effective measures of similarities between users or items in the context of sparse data. We propose a novel approach that relies on neural networks and word embeddings to the problem of matching a learner looking for mentoring, and a tutor that is willing to provide this mentoring. Tutors and learners can issue multiple offers/requests on different topics. The approach matches over the whole array of topics specified by learners and tutors. Its performance for tutor-learner matching is compared with the state of the art. It yields similar results in terms of precision, but improves the recall.


title: A Dynamic Pricing Method for Carpooling Service Based on Coalitional Game Analysis

abstract: In recent years, carpooling service provided by corporations like Uber (UberPool), Didi (DidiPool) and Lyft (Lift Link) have become more and more popular. It helps alleviating the urban traffic congestion, by decreasing the empty seats rate. To balance the supply and demand of the taxi service, a dynamic pricing method is needed. More specifically, passengers taking a same vehicle may be charged differently, even thought they shared a most part of a trip. It often challenges the current dynamic pricing policy that how to balance the service and the pricing among different passengers who shared a certain route in their personal trip. In view of this challenge, we propose a new dynamic pricing method and divide the payoff according to the contribution of each passenger. Concretely, we deploy the framework of coalitional game to analyze spatial temporal constraints that guarantee individual benefits from the carpooling coalition. Then, we explore the Nash Product to maximize the utility of passengers as a whole and reduce our problem into a geometry-programming problem. At last we use Shapley value method to measure the specific contribution of each passenger. We conduct a simulated experiment and the results show effectiveness of our method.


title: Who is Fiddling with Prices?: Building and Deploying a Watchdog Service for E-commerce

abstract: We present the design, implementation, validation, and deployment of the Price Sheriff, a highly distributed system for detecting various types of online price discrimination in e-commerce. The Price Sheriff uses a peer-to-peer architecture, sandboxing, and secure multiparty computation to allow users to tunnel price check requests through the browsers of other peers without tainting their local or server-side browsing history and state. Having operated the Price Sheriff for several months with approximately one thousand real users, we identify several instances of cross-border price discrimination based on the country of origin. Even within national borders, we identify several retailers that return different prices for the same product to different users. We examine whether the observed differences are due to personal-data-induced discrimination or A/B testing, and conclude that it is the latter.


Learning Structured Representation for Text Classification via Reinforcement Learning


title: Fine-grained news recommendation by fusing matrix factorization, topic analysis and knowledge graph representation

abstract: Most news recommendation methods focus on using textual information of news to solve data sparseness problem of collaborative filtering. While if the text is not informative enough, these methods can't work well. A collaborative model combining matrix factorization, topic analysis and knowledge graph representation is proposed by introducing the knowledge from external knowledge base to alleviate the deficiency of the text. The experiment conducted on real life news dataset shows that the joint model outperforms the state-of-the-art method by 14% in Recall@200 metric, and improves the recommendation performance on sparse items by 20%.


title: Expert Review on Big Data Analytics Implementation Model in Data-driven Decision-Making

abstract: Data-driven decision-making can offer improved insights for information value and create new business opportunity. The purpose of this paper is to present the findings of expert opinion in verifying the influencing factors in big data analytics (BDA) implementation that are beneficial in developing the BDA implementation assessment model. The study was carried out by conducting face-to-face approach sessions with three academicians and four industry experts who have vast experience in big data research and its implementation. The findings from these exercises has confirmed and verified the content of the ten factors that include organization dimension (such as big data strategy, top management support, resource commitment, organizational relationship), people dimension (such as analytics skills, managerial skills and analytics culture) and technology dimension (includes data infrastructures, information processing and quality) were appropriate for the research model. It was described using descriptive analysis such as frequency, mean and standard deviation. Once the verification process is complete, the research model will be validated through survey in the future work.


System Engineering and Integration of Controls for Advanced Life Support


Contactless Vulnerability Analysis using Google and Shodan.


title: Direct Ink Writing of 3D Functional Materials

abstract: The ability to pattern materials in three dimensions is critical for several technological applications, including composites, microfluidics, photonics, and tissue engineering. Direct-write assembly allows one to design and rapidly fabricate materials in complex 3D shapes without the need for expensive tooling, dies, or lithographic masks. Here, recent advances in direct ink writing are reviewed with an emphasis on the push towards finer feature sizes. Opportunities and challenges associated with direct ink writing are also highlighted.


title: A Data-Driven Inventory Control Policy for Cash Logistics Operations: An Exploratory Case Study Application at a Financial Institution

abstract: A number of market changes are impacting the way financial institutions are managing their automated teller machines (ATMs). We propose a new class of adaptive data-driven policies for a stochastic inventory control problem faced by a large financial institution that manages cash at several ATMs. Senior management were concerned that their current cash supply system to manage ATMs was inefficient and outdated, and suspected that using improved cash management could reduce overall system cost. Our task was to provide a robust procedure to tackle the ATM's cash deployment strategies. Current industry practice uses a periodic review system with infrequent parameter updates for cash management based on the assumption that demand is normally distributed during the review period. This assumption did not hold during our investigation, warranting a new and robust analysis. Moreover, we discovered that forecast errors are often not normally distributed and that these error distributions change dramatically over time. Our approach finds the optimal time series forecaster and the best-fitting weekly forecast error distribution. The guaranteed optimal target cash inventory level and time between orders could only be obtained through an optimization module that was embedded in a simulation routine that we built for the institution. We employed an exploratory case study methodology to collect cash withdrawal data at 21 ATMs owned and operated by the financial institution. Our new approach shows a 4.6% overall cost reduction. This reflects an annual cost savings of over $250,000 for the 2,500 ATM units that are operated by the bank.


title: Manga content extraction method for automatic mobile comic content creation

abstract: Creating a digital Manga comic for mobile devices is chalenging because of the limitation of device's screen size and time consuming of the process. In this paper, we proposed an efficient method for automatically creating mobile content of digital Manga comic from scanned of printed digital comic by extracting comic frame, comic balloon and Japanese character within a Manga comic then recompose it a new mobile content using ComicXML to store comic frame content information. Experimental results on Manga content extraction have 81.6% accuracy of non-flat comic page frame extraction, 100% accuracy of comic balloon detection, and about 93.75% accuracy of Japanese character text extraction.


title: Cortical changes in chronic low back pain: Current state of the art and implications for clinical practice

abstract: There is increasing evidence that chronic pain problems are characterised by alterations in brain structure and function. Chronic back pain is no exception. There is a growing sentiment, with accompanying theory, that these brain changes contribute to chronic back pain, although empirical support is lacking. This paper reviews the structural and functional changes of the brain that have been observed in people with chronic back pain. We cast light on the clinical implications of these changes and the possibilities for new treatments but we also advise caution against concluding their efficacy in the absence of solid evidence to this effect.


title: Poster: Smart-Contract Based Incentive Mechanism for K-Anonymity Privacy Protection in LBSs

abstract: In Location Based Services (LBSs), service providers can obtain mobile users' locations or traces while receiving their service requests. K-anonymity, which is the most commonly used location privacy protection method, needs the cooperation among mobile users to form a k-anonymous group. Though several incentive mechanisms have been proposed to motivate mobile users to participate in the k-anonymous group, most of them rely on a `trustful' center. In this paper, we propose a distributed secure incentive mechanism that applies blockchain smart contracts to motivate users for k-anonymity cooperation. To protect the reward transaction information, group encryption and blind collective signature are adopted. The simulations demonstrate the effectiveness of our proposed incentive mechanism.


title: Design of an RFID-Based Battery-Free Programmable Sensing Platform

abstract: This paper presents the wireless identification and sensing platform (WISP), which is a programmable battery-free sensing and computational platform designed to explore sensor-enhanced radio frequency identification (RFID) applications. WISP uses a 16-bit ultralow-power microcontroller to perform sensing and computation while exclusively operating from harvested RF energy. Sensors that have successfully been integrated into the WISP platform to date include temperature, ambient light, rectified voltage, and orientation. The microcontroller encodes measurements into an electronic product code (EPC) Class 1 Generation 1 compliant ID and dynamically computes the required 16-bit cyclical redundancy checking (CRC). Finally, WISP emulates the EPC protocol to communicate the ID to the RFID reader. To the authors' knowledge, WISP is the first fully programmable computing platform that can operate using power transmitted from a long-range (UHF) RFID reader and communicate arbitrary multibit data in a single response packet.


title: Deep Learning of Mouth Shapes for Sign Language

abstract: This paper deals with robust modelling of mouth shapes in the context of sign language recognition using deep convolutional neural networks. Sign language mouth shapes are difficult to annotate and thus hardly any publicly available annotations exist. As such, this work exploits related information sources as weak supervision. Humans mainly look at the face during sign language communication, where mouth shapes play an important role and constitute natural patterns with large variability. However, most scientific research on sign language recognition still disregards the face. Hardly any works explicitly focus on mouth shapes. This paper presents our advances in the field of sign language recognition. We contribute in following areas: We present a scheme to learn a convolutional neural network in a weakly supervised fashion without explicit frame labels. We propose a way to incorporate neural network classifier outputs into a HMM approach. Finally, we achieve a significant improvement in classification performance of mouth shapes over the current state of the art.


title: Approximate 2-Degree-Of-Freedom control of a class-D amplifier using FPGA

abstract: In recent years, it is desired that the bandwidth of a class-D amplifier is widened using sampling frequencies as low as possible. For example, it is expected in the application of the power amplifier of vibration generator that the bandwidth is kept 5[kHz] within the load impedance ∞~2 [Ω]. In this paper, it is shown that this demand to the class-D amplifier can be attained by an A2DOF (Approximate 2-Degree-Of-Freedom) digital controller with 200[kHz] switching and sampling frequencies. The controller is implemented by a FPGA and it is shown from experiments that this demand is attained enough.


title: State-of-the-art in artificial neural network applications: A survey

abstract: This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.


title: Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery

abstract: Imagery acquired with unmanned aerial vehicles (UAVs) has great potential for incorporation into natural resource monitoring protocols due to their ability to be deployed quickly and repeatedly and to fly at low altitudes. While the imagery may have high spatial resolution, the spectral resolution is low when lightweight off-the-shelf digital cameras are used, and the inclusion of texture measures can potentially increase the classification accuracy. Texture measures have been used widely in pixel-based image analysis, but their use in an object-based environment has not been well documented. Our objectives were to determine the most suitable texture measures and the optimal image analysis scale for differentiating rangeland vegetation using UAV imagery segmented at multiple scales. A decision tree was used to determine the optimal texture features for each segmentation scale. Results indicated the following: (1) The error rate of the decision tree was lower; (2) prediction success was higher; (3) class separability was greater; and (4) overall accuracy was higher (high 90% range) at coarser segmentation scales. The inclusion of texture measures increased classification accuracies at nearly all segmentation scales, and entropy was the texture measure with the highest score in most decision trees. The results demonstrate that UAVs are viable platforms for rangeland monitoring and that the drawbacks of low-cost off-the-shelf digital cameras can be overcome by including texture measures and using object-based image analysis which is highly suitable for very high resolution imagery.


title: Understanding Cloud Adoption Decisions in the Public Sector

abstract: Cloud computing is bringing significant changes in the way that public organizations interact with information systems. Existing research exploring cloud computing adoption in the public sector tends to privilege the technological factors. This paper contributes to the understanding of cloud computing adoption decisions in the public sector by also considering the organizational and environmental contexts of the adoption. By drawing on diffusion of innovation literature, we analyze the results of 21 interviews conducted across six European countries. The results of the study reveal that the adoption of cloud computing in the public sector is viewed with optimism and highlight that most of the factors that hinder the adoption are relevant to the environmental context.


title: Feed-Forward Compensator of Operating Frequency for APWM HB Flyback Converter

abstract: A feed-forward frequency compensator for an asymmetrical pulsewidth modulated (APWM) half-bridge (HB) flyback converter is proposed to increase its power conversion efficiency. Under a variable input voltage, the APWM HB flyback converter cannot be satisfied with the zero-current switching (ZCS) condition of output rectifiers because the variation of the turn-ON duration of main switches, which regulate the output voltage, induces their improper turn-OFF duration. This means that the resonance of a primary current during the turn-OFF of the switches is out of the ZCS operation of the output rectifiers. To avoid this improper operation, the resonant network should be designed with a marginal turn-OFF duration that guarantees the ZCS. However, this makes the converter operate with poor efficiency because of increasing conduction losses. The proposed feed-forward frequency compensator can properly change the converter's operating frequency to reduce the conduction losses according to the variation of the input voltage satisfying the ZCS condition of the output rectifiers. Operation principles, steady-state analysis, and soft-switching conditions are analyzed to derive the frequency compensation algorithm of the APWM HB flyback converter. Experimental results using a 240-W prototype converter show that the proposed compensation algorithm can improve the power conversion efficiency of the APWM HB flyback converter under the input-voltage variation.


title: A New Approach to Model-Free Tracking with 2D Lidar

abstract: This paper presents a unified and model-free framework for the detection and tracking of dynamic objects with 2D laser range finders in an autonomous driving scenario. A novel state formulation is proposed that captures joint estimates of the sensor pose, a local static background and dynamic states of moving objects. In addition, we contribute a new hierarchical data association algorithm to associate raw laser measurements to observable states, and within which, a new variant of the Joint Compatibility Branch and Bound (JCBB) algorithm is introduced for problems with large numbers of measurements. The system is calibrated systematically on 7.5K labeled object examples and evaluated on 6K test cases, and is shown to greatly outperform an existing industry standard targeted at the same problem domain.


title: Similarity-Based Approach for Positive and Unlabeled Learning

abstract: Positive and unlabelled learning (PU learning) has been investigated to deal with the situation where only the positive examples and the unlabelled examples are available. Most of the previous works focus on identifying some negative examples from the unlabelled data, so that the supervised learning methods can be applied to build a classifier. However, for the remaining unlabelled data, which can not be explicitly identified as positive or negative (we call them ambiguous examples), they either exclude them from the training phase or simply enforce them to either class. Consequently, their performance may be constrained. This paper proposes a novel approach, called similarity-based PU learning (SPUL) method, by associating the ambiguous examples with two similarity weights, which indicate the similarity of an ambiguous example towards the positive class and the negative class, respectively. The local similarity-based and global similarity-based mechanisms are proposed to generate the similarity weights. The ambiguous examples and their similarity-weights are thereafter incorporated into an SVM-based learning phase to build a more accurate classifier. Extensive experiments on real-world datasets have shown that SPUL outperforms state-of-the-art PU learning methods.


title: Population impact of mass incarceration under New York's Rockefeller drug laws: an analysis of years of life lost

abstract: BACKGROUND Now nearing the 30th year since their passage by the State’s legislature, New York’s “Rockefeller drug laws” (RDL) mandate long prison sentences for drug offenders – most commonly for possession or sale of small quantities of illicit drugs.1 The length of the sentences under RDL are based on the type and quantity of drugs involved and on the defendants prior offenses. Penalties are applied regardless of the circumstances of arrest, but most are “buy and bust” entrapments of drug users by undercover narcotic police operating in targeted communities. Facing long mandatory sentences if found guilty in a jury trial and less than adequate legal representation from poorly compensated public defenders, most drug defendants agree to plead guilty to lesser offenses than those in the original charges. Over 90% of drug cases are plea bargained directly with the prosecutors and do not involve jury trials to determine guilt or innocence.1 Thus, once the amount of drugs and prior record have been determined, the sentencing rules do not permit judicial discretion that would allow judges to take extenuating circumstances into account in determining the length of sentences on a case by case basis.


title: Non-Foster impedance matching of electrically small antennas

abstract: When the size of an antenna is electrically small, the antenna is neither efficient nor a good radiator because most of the input power is stored in the reactive near-field region and little power is radiated in the far-field region. As demonstrated in [1]- [2], the radiation quality factor of small antennas is definitely high. In other words, the input impedance of small antennas is considerably reactive. To reduce the radiation quality factor in the whole or partial frequency range of interest, it is important to increase the radiation resistance and/or reduce the reactance of the antenna. Hence, it is necessary to modify the antenna to reduce the reactance of the antenna and/or add impedance matching networks (MNs) to maximize the transfer of power from a resistive source to the highly reactive antenna.


title: Simulation model of a grid-connected single-phase photovoltaic system in PSCAD/EMTDC

abstract: In this paper, a complete simulation model of a grid-connected single-phase two-stage photovoltaic (PV) system with associated controllers is presented. The simulation model is developed in PSCAD/EMTDC simulation program. The component models of the grid-connected PV system include a PV array, a dc-dc boost converter, a voltage source converter (VSC) and an LCL filter. Components of the LCL filter, the dc-link capacitor of the VSC and the inductor of the dc-dc boost converter are established theoretically and that are used in modelling the grid-connected PV system. The control architecture of the presented system incorporates a synchronous reference frame phase-locked-loop (s-PLL), a stationary frame current controller, a dc-link voltage controller, a dc-dc boost converter controller and a maximum power point tracking (MPPT) algorithm. Control design methodologies are described in detail. Simulation studies confirm that the modelling and control design approaches taken are robust and lead to a system with acceptable performance.


title: A Theory-Driven Design Framework for Social Recommender Systems

abstract: Social recommender systems utilize data regarding users’ social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data – beyond consumption profile similarity – is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.’s (1992) IS Design Theory by introducing the notion of “applied behavioral theory,” as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient’s advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems.


title: A cloud-based openflow firewall for mitigation against DDoS attacks in smart grid AMI networks

abstract: Recent architectures for the advanced metering infrastructure (AMI) have incorporated several back-end systems that handle billing and other smart grid control operations. The non-availability of metering data when needed or the untimely delivery of data needed for control operations will undermine the activities of these back-end systems. Unfortunately, there are concerns that cyber attacks such as distributed denial of service (DDoS) will manifest in magnitude and complexity in a smart grid AMI network. Such attacks will range from a delay in the availability of end user's metering data to complete denial in the case of a grounded network. This paper proposes a cloud-based (IaaS) firewall for the mitigation of DDoS attacks in a smart grid AMI network. The proposed firewall has the ability of not only mitigating the effects of DDoS attack but can prevent the attack before they are launched. Our proposed firewall system leverages on cloud computing technology which has an added advantage of reducing the burden of data computations and storage for smart grid AMI back-end systems. The openflow firewall proposed in this study is a better security solution with regards to the traditional on-premises DoS solutions which cannot cope with the wide range of new attacks targeting the smart grid AMI network infrastructure. Simulation results generated from the study show that our model can guarantee the availability of metering/control data and could be used to improve the QoS of the smart grid AMI network under a DDoS attack scenario.


title: Continuous Learning in Single-Incremental-Task Scenarios

abstract: Abstract It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge. However, these strategies are still unsatisfactory if the tasks are not disjoint but constitute a single incremental task (e.g., class-incremental learning). In this paper we point out the differences between multi-task and single-incremental-task scenarios and show that well-known approaches such as LWF, EWC and SI are not ideal for incremental task scenarios. A new approach, denoted as AR1, combining architectural and regularization strategies is then specifically proposed. AR1 overhead (in terms of memory and computation) is very small thus making it suitable for online learning. When tested on CORe50 and iCIFAR-100, AR1 outperformed existing regularization strategies by a good margin.


title: Automatic image co-segmentation using geometric mean saliency

abstract: Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing singleimage saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.


title: Theoretical analysis of trend vanishing moments for directional orthogonal transforms

abstract: This work contributes to investigate theoretical properties of the trend vanishing moment (TVM) which the authors have defined in a previous work and applied to the directional design of 2-D nonsep-arable GenLOTs. The TVM condition can be regarded as a natural extention of 1-D VM to 2-D one and alternative of the conventional directional vanishing moment (DVM). Firstly, the definition of TVM is given to clarify what it is, and then the mathematical meaning is discussed. While the conventional DVM condition requires for the moments to vanish along lines in the frequency domain and restricts the direction to a rational factor, the TVM condition imposes the moments only point-wisely and the direction can be steered flexibly. Some significant properties of TVMs are also shown and a simulation result of zonal coding for an artifical picture is given to verify the trend surface annihilation property.


title: Detecting predatory conversations in social media by deep Convolutional Neural Networks

abstract: Automatic identification of predatory conversations in chat logs helps the law enforcement agencies act proactively through early detection of predatory acts in cyberspace. In this paper, we describe the novel application of a deep learning method to the automatic identification of predatory chat conversations in large volumes of chat logs. We present a classifier based on Convolutional Neural Network (CNN) to address this problem domain. The proposed CNN architecture outperforms other classification techniques that are common in this domain including Support Vector Machine (SVM) and regular Neural Network (NN) in terms of classification performance, which is measured by F1-score. In addition, our experiments show that using existing pre-trained word vectors are not suitable for this specific domain. Furthermore, since the learning algorithm runs in a massively parallel environment (i.e., general-purpose GPU), the approach can benefit a large number of computation units (neurons) compared to when CPU is used. To the best of our knowledge, this is the first time that CNNs are adapted and applied to this application domain.


title: Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews

abstract: Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product.


title: Temporal construal

abstract: Construal level theory proposes that temporal distance changes people's responses to future events by changing the way people mentally represent those events. The greater the temporal distance, the more likely are events to be represented in terms of a few abstract features that convey the perceived essence of the events (high-level construals) rather than in terms of more concrete and incidental details of the events (low-level construals). The informational and evaluative implications of high-level construals, compared with those of low-level construals, should therefore have more impact on responses to distant-future events than near-future events. This article explores the implications of construal level theory for temporal changes in evaluation, prediction, and choice. The authors suggest that construal level underlies a broad range of evaluative and behavioral consequences of psychological distance from events.


title: Microcontroller Based Substation Monitoring and Control System with Gsm Modem

abstract: The purpose of this project is to acquire the remote electrical parameters like Voltage, Current and Frequency and send these real time values over GSM network using GSM Modem/phone along with temperature at power station. This project is also designed to protect the electrical circuitry by operating an Electromagnetic Relay. This Relay gets activated whenever the electrical parameters exceed the predefined values. The Relay can be used to operate a Circuit Breaker to switch off the main electrical supply. User can send commands in the form of SMS messages to read the remote electrical parameters. This system also can automatically send the real time electrical parameters periodically (based on time settings) in the form of SMS. This system can be designed to send SMS alerts whenever the Circuit Breaker trips or whenever the Voltage or Current exceeds the predefined limits. This project makes use of an onboard computer which is commonly termed as microcontroller. This onboard computer can efficiently communicate with the different sensors being used. The controller is provided with some internal memory to hold the code. This memory is used to dump some set of assembly instructions into the controller. And the functioning of the controller is dependent on these assembly instructions. The controller is programmed using Embedded C language. Keyworld: GSM Modem, Initialization of ADC module of microcontroller, PIC-C compiler for Embedded C programming, PIC kit 2 programmer for dumping code into Micro controller, Express SCH for Circuit design, Proteus for hardware simulation.


title: Requirements Engineering Challenges in Large-Scale Agile System Development

abstract: Motivated by their success in software development, companies implement agile methods and their practices increasingly for software-intense, large products, such as cars, telecommunication infrastructure, and embedded systems. Such systems are usually subject to safety and regulative concerns as well as different development cycles of hardware and software. Consequently, requirements engineering involves upfront and detailed analysis, which can be at odds with agile (software) development. In this paper, we present results from a multiple case study with two car manufacturers, a telecommunications company, and a technology company that are on the journey to introduce organization wide continuous integration and continuous delivery to customers. Based on 20 qualitative interviews, 5 focus groups, and 2 cross-company workshops, we discuss possible scopes of agile methods within system development, the consequences this has on the role of requirements, and the challenges that arise from the interplay of requirements engineering and agile methods in large-scale system development. These relate in particular to communicating and managing knowledge about a) customer value and b) the system under development. We conclude that better alignment of a holistic requirements model with agile development practices promises rich gains in development speed, flexibility, and overall quality of software and systems.


title: A Survey to Predict the Trend of AI-able Server Evolution in the Cloud

abstract: About a decade ago, people concerned about the risks of adopting cloud computing. It was an unproven new thing that raised more questions than it answered. Nowadays, we hear more about the risks of not adopting the cloud. Three of the leading cloud players, Amazon Web Services, Microsoft Azure, and Google Cloud Platform, and other participants have developed complex cloud platforms that are driving the cloud agenda and launching innovative new products to meet the needs of modern businesses. When looking at processors, core components of the cloud, there is a trend for hyperscale data centers is to move beyond the CPUs and turn to dedicated chips, such as graphics processing units, field programmable gating arrays, and application specific integrated circuits. We think it is an artificial intelligence (AI) realization process and provide a detailed survey about hardware server design in this process. After discussing and summarizing various disclosed techniques and platforms, we conceived a hybrid hardware structure for efficient AI applications.