title: A digital LDO with transient enhancement and limit cycle oscillation reduction

abstract: A digital low dropout regulator (D-LDO) manages to operate at low voltage and scale with process. But, the tradeoff between current efficiency and transient response speed limits its applications. In this work, a coarse-fine-tuning technique with burst-mode operation is employed to advance this trade-off. Once the output voltage under/overshoot is detected, the power MOS array changes with x16 unit size at 500 MHz sampling frequency for a fixed time, comparing with x1 size and 50 MHz in steady state. The limit cycle oscillation (LCO) in steady state is reduced by a feed-forward compensation zero with negligible power and area overheads. The proposed D-LDO is simulated in a 65 nm CMOS process, achieving a 60 mV voltage undershoot and 0.36 ps FOM of speed with a 60 μA quiescent current, and mode-1 LCO in steady state.


title: Motion detection based on the combining of the background subtraction and spatial color information

abstract: To detect the moving objects in a video sequence based on background subtraction approaches, a background model should be generated at the first time before subtract it from each image of the sequence and then segmenting the moving objects. But this detection can be difficult when the environment is influenced by illumination and weather changes. In The goal to solve the problem of environmental illumination changes in the background model and to classify pixels of the current image as foreground or background, a new method of background subtraction is presented in this paper. Firstly, the spatial color information is used to generate the background of each color space (R, G, and B) of the sequence. The absolute difference is computed to subtracting the background before compute the binary image of the moving objects using a threshold. This threshold is also used to update the background at each new image. The experimental results demonstrate that our approach is effective and accurate for moving objects detection and the use of spatial color information was robust to environmental illumination change. The experimental results are also compared with the results of the background estimation algorithm with Σ-Δ (SD) and Motion detection with pyramid structure of background model (MDPS).


title: The Faces of Facebookers: Investigating Social Enhancement and Social Compensation Hypotheses; Predicting Facebook™ and Offline Popularity from Sociability and Self-Esteem, and Mapping the Meanings of Popularity with Semantic Networks

abstract: This research investigates two competing hypotheses from the literature: 1) the Social Enhancement (“Rich Get Richer”) hypothesis that those more popular offline augment their popularity by increasing it on Facebook™, and 2) the “Social Compensation” (“Poor Get Richer”) hypothesis that users attempt to increase their Facebook™ popularity to compensate for inadequate offline popularity. Participants (n= 614) at a large, urban university in the Midwestern United States completed an online survey. Results are that a subset of users, those more extroverted and with higher self-esteem, support the Social Enhancement hypothesis, being more popular both offline and on Facebook™. Another subset of users, those less popular offline, support the Social Compensation hypotheses because they are more introverted, have lower self-esteem and strive more to look popular on Facebook™. Semantic network analysis of open-ended responses reveals that these two user subsets also have different meanings for offline and online popularity. Furthermore, regression explains nearly twice the variance in offline popularity as in Facebook™ popularity, indicating the latter is not as socially grounded or defined as offline popularity.


title: Association between Gamma-Glutamyltransferase Level and Risk of Stroke: A Systematic Review and Meta-analysis of Prospective Studies.

abstract: BACKGROUND ::: Stroke is often regulated by a number of modifiable and nonmodifiable risk factors. Recently, studies suggested high gamma-glutamyltransferase (GGT) level may be associated with stroke, but drew inconsistent conclusions. So, we conducted a meta-analysis to evaluate the relationship between GGT level and risk of stroke. ::: ::: ::: METHODS ::: We systematically searched PubMed, Embase, and Cochrane Library (updated to January 2015) for prospective cohort studies. Then, relative risk (RR) with 95% confidence interval (CI) was used to assess the association. Regression analyses, subgroup analyses, and sensitivity analyses were also performed. The Begg test, Egger test, and the trim-and-fill method were used to assess potential publication bias. ::: ::: ::: RESULTS ::: A total of 5707 cases and 926,497 participants in 10 prospective studies were included. Overall, high GGT level has a positive association with increased risk of stroke (RR = 1.28; 95% CI, 1.16-1.43). In the subgroup analyses, a positive association was consistently observed in each subgroup except in the women subgroup (RR = 1.45; 95% CI, .9-2.34) and a large number of stroke events subgroups (≥ 500) (RR = 1.25; 95% CI, .85-1.84). Heterogeneity was significantly reduced in the subgroup analysis by population characteristics. In the publication bias test, the resulting adjusted RR remained significant (RR = 1.10; 95% CI, 1.00-1.21) after using the trim-and-fill method. ::: ::: ::: CONCLUSIONS ::: Our meta-analysis provides evidence that a high level of GGT is significantly associated with increased risk of stroke independently of alcohol intake. Gender and ethnicity variations may exist in the relationship between high GGT level and risk of stroke.


title: DISFA: A Spontaneous Facial Action Intensity Database

abstract: Access to well-labeled recordings of facial expression is critical to progress in automated facial expression recognition. With few exceptions, publicly available databases are limited to posed facial behavior that can differ markedly in conformation, intensity, and timing from what occurs spontaneously. To meet the need for publicly available corpora of well-labeled video, we collected, ground-truthed, and prepared for distribution the Denver intensity of spontaneous facial action database. Twenty-seven young adults were video recorded by a stereo camera while they viewed video clips intended to elicit spontaneous emotion expression. Each video frame was manually coded for presence, absence, and intensity of facial action units according to the facial action unit coding system. Action units are the smallest visibly discriminable changes in facial action; they may occur individually and in combinations to comprise more molar facial expressions. To provide a baseline for use in future research, protocols and benchmarks for automated action unit intensity measurement are reported. Details are given for accessing the database for research in computer vision, machine learning, and affective and behavioral science.


title: A service-oriented middleware framework for manufacturing industry 4.0

abstract: The advantages of the Internet of things (IoT) initiated the vision of Industry 4.0 in Europe and smart manufacturing in USA. Both visions aim to implement the smart factory to achieve similar objectives by utilizing new technologies. These technologies include cloud computing, fog computing, cyber-physical systems (CPS), and data analytics. Together they help automate and autonomize the manufacturing processes and controls to optimize the productivity, reliability, quality, cost-effeteness, and safety of these processes. While both visions are promising, developing and operating Industry 4.0 applications are extremely challenging. This is due to the complexity of the manufacturing processes as well as their management, controls, and integration dynamics. This paper introduces Man4Ware, a service-oriented middleware for Industry 4.0. Man4Ware can help facilitate the development and operations of cloud and fog-integrated smart manufacturing applications. Man4Ware offers many advantages through service level interfaces to enable easy utilization of new technologies and integration of different services to relax many of the challenges facing the development and operations of such applications1.


title: Hybrid Rigid-Soft Hand Exoskeleton to Assist Functional Dexterity

abstract: A hybrid hand exoskeleton, leveraging rigid and soft elements, has been designed to serve as an assistive device to return the ability to perform activities of daily living (ADL) and improve quality of life for a broad population with hand impairment. This glove-like exoskeleton, the SeptaPose Assistive and Rehabilitative (SPAR) Glove, is underactuated, enabling seven hand poses which support most ADLs. The device resides on the spectrum between traditional rigid devices and the latest soft robotic designs. It includes novel ergonomic elements for power transmission and additional features to enable self-donning and doffing. Embedded sensors enable pose estimation and intent detection for intuitive control of the glove. In this paper, we summarize the overall design of the glove, and present details of the novel rigid palm bar and hyperextension prevention elements. We characterize the grasp force and range of motion (ROM) of the glove, and present initial feedback from an end user. The SPAR Glove meets or exceeds the functional requirements of ADLs for both ROM and grasp force. Additionally, the glove exceeds the grasp force capabilities of comparable devices, while simultaneously offering the highest number of poses. In addition to its role as an assistive device, the SPAR Glove exoskeleton has the potential to provide “hands-in” rehabilitation, centered on performing functional tasks. In the near term, the glove is a highly capable prototype for exploring hybrid assistive device design, intent detection, and user interface research.


title: Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading

abstract: Click signal has been widely used for designing and evaluating interactive information systems, which is taken as the indicator of user preference. However, click signal does not capture post-click user experience. Very commonly, the user first clicked an item and then found it is not what he wanted after reading its content, which shows there is a gap between user click and user actual preference. Previous studies on web search have incorporated other user behaviors, such as dwell time, to reduce the gap. Unfortunately, for other scenarios such as recommendation and online news reading, there still lacks a thorough understanding of the relationship between click and user preference, and the corresponding reasons which are the focus of this work. Based on an in-depth laboratory user study of online news reading scenario in the mobile environment, we show that click signal does not align with user preference. Besides, we find that user preference changes frequently, hence preferences in three phases are proposed: Before-Read Preference, After-Read Preference and Post-task Preference. In addition, the statistic analysis shows that the changes are highly related to news quality and the context of user interactions. Meanwhile, many other user behaviors, like viewport time, dwell time, and read speed, are found reflecting user preference in different phases. Furthermore, with the help of various kinds of user behaviors, news quality, and interaction context, we build an effective model to predict whether the user actually likes the clicked news. Finally, we replace binary click signals of traditional click-based evaluation metrics, like Click-Through Rate, with the predicted item-level preference, and significant improvements are achieved in estimating the user's list-level satisfaction. Our work sheds light on the understanding of user click behaviors and provides a method for better estimating user interest and satisfaction. The proposed model could also be helpful to various recommendation tasks in mobile scenarios.


title: Towards retro-projected robot faces: An alternative to mechatronic and android faces

abstract: This paper presents a new implementation of a robot face using retro-projection of a video stream onto a semitransparent facial mask. The technology is contrasted against mechatronic robot faces, of which Kismet is a typical example, and android robot faces, as used on the Ishiguro robots. The paper highlights the strengths of Retro-projected Animated Faces (RAF) technology (with cost, flexibility and robustness being notably strong) and discusses potential developments.


title: Enhancing Wikipedia search results using Text Mining

abstract: Wikipedia is an online encyclopedia which contains millions of articles related to different subject domains. Wikipedia also has a search page itself to display the links corresponding to Wikipedia articles for a given user query input. This search result page displays the search results according to the relevance order, without any content based grouping. This paper presents an experimental deduction of a search result clustering methodology to group the links, returned by the search result page for a particular keyword, based on the contents of the HTML documents, represented by the links and label these resulted groups meaningfully. The proposed methodology is based on the concepts and theories of Text Mining. Grouping of search results makes easy and efficient for the user in finding the desired Wikipedia document. It is also possible to view the different applications and usages of a given keyword very quickly. This work identifies the best clustering algorithm for document clustering and investigates the ways to determine optimum number of clusters to have a better grouping and label the groups. We evaluate our proposed method by conducting several experiments and the results indicate that our method has a higher precision and recall.


title: McRT-STM: a high performance software transactional memory system for a multi-core runtime

abstract: Applications need to become more concurrent to take advantage of the increased computational power provided by chip level multiprocessing. Programmers have traditionally managed this concurrency using locks (mutex based synchronization). Unfortunately, lock based synchronization often leads to deadlocks, makes fine-grained synchronization difficult, hinders composition of atomic primitives, and provides no support for error recovery. Transactions avoid many of these problems, and therefore, promise to ease concurrent programming.We describe a software transactional memory (STM) system that is part of McRT, an experimental Multi-Core RunTime. The McRT-STM implementation uses a number of novel algorithms, and supports advanced features such as nested transactions with partial aborts, conditional signaling within a transaction, and object based conflict detection for C/C++ applications. The McRT-STM exports interfaces that can be used from C/C++ programs directly or as a target for compilers translating higher level linguistic constructs.We present a detailed performance analysis of various STM design tradeoffs such as pessimistic versus optimistic concurrency, undo logging versus write buffering, and cache line based versus object based conflict detection. We also show a MCAS implementation that works on arbitrary values, coexists with the STM, and can be used as a more efficient form of transactional memory. To provide a baseline we compare the performance of the STM with that of fine-grained and coarse-grained locking using a number of concurrent data structures on a 16-processor SMP system. We also show our STM performance on a non-synthetic workload -- the Linux sendmail application.


title: Improving MapReduce Performance in Heterogeneous Network Environments and Resource Utilization

abstract: MapReduce is a widely-used model for data parallel applications. We found its resource utilization is inefficient when there are not enough tasks to fill all task slots as the resources "reserved" for idle slots are just wasted. We propose resource stealing which enables running tasks to steal the unutilized resources and return them when new tasks are assigned. It exploits the opportunistic use of the otherwise wasted resources to improve overall resource utilization and reduce job execution time. Besides, our practical use of Hadoop shows the current mechanism adopted to trigger speculative execution creates many unnecessary speculative tasks that are killed soon after creation as the original tasks complete earlier. To alleviate the issue, we propose Benefit Aware Speculative Execution which predicts the benefit of running new speculative tasks and greatly eliminates unnecessary runs. Finally, MapReduce is mainly optimized for homogeneous environments and its inefficiency in heterogeneous network environments has been observed in our experiments. We investigate network heterogeneity aware scheduling of both map and reduce tasks. Overall, our goal is to enhance Hadoop to cope with significant network heterogeneity and improve resource utilization.


title: The Internet of Things in an Enterprise Context

abstract: This paper puts the Internet of Things in a wider context: How it relates to the Future Internet overall, and where the business value lies so that it will become interesting for enterprises to invest in it. Real-World Awareness and Business Process Decomposition are the two major paradigms regarding future business value. The major application domains where the Internet of Things will play an important role and where there are concrete business opportunities are highlighted. But there are also many technical challenges that need to be addressed. These are listed and it is shown how they are tackled by existing research projects with industrial participation.


title: Thermal-Visible Registration of Human Silhouettes: a Similarity Measure Performance Evaluation

abstract: Abstract When dealing with the registration of information from different image sources, the de facto similarity measure used is Mutual Information (MI). Although MI gives good performance in many image registration applications, recent works in thermal–visible registration have shown that other similarity measures can give results that are as accurate, if not more than MI. Furthermore, some of these measures also have the advantage of being calculated independently from each image to register, which allows them to be integrated more easily in energy minimization frameworks. In this article, we investigate the accuracy of similarity measures for thermal–visible image registration of human silhouettes, including MI, Sum of Squared Differences (SSD), Normalized Cross-Correlation (NCC), Histograms of Oriented Gradients (HOG), Local Self-Similarity (LSS), Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Census, Fast Retina Keypoint (FREAK), and Binary Robust Independent Elementary Feature (BRIEF). We tested the various similarity measures in dense stereo matching tasks over 25,000 windows to have statistically significant results. To do so, we created a new dataset in which one to five humans are walking in a scene in various depth planes. Results show that even if MI is a very strong performer, particularly for large regions of interest (ROI), LSS gives better accuracies when ROI are small or segmented into small fragments because of its ability to capture shape. The other tested similarity measures did not give consistently accurate results.


title: Comparative Effects of Alcohol and Marijuana on Mood, Memory, and Performance

abstract: Abstract This study compared subjective and behavioral effect profiles of alcohol and smoked marijuana using technology that controlled puffing and inhalation parameters. Male volunteers ( n = 5) with histories of moderate alcohol and marijuana use were administered three doses of alcohol (0.25, 0.5, or 1.0 g/kg), three doses of marijuana [4, 8, or 16 puffs of 3.55% Δ 9 -tetrahydrocannabinol (THC)], and placebo in random order under double blind conditions in seven separate sessions. Blood alcohol concentration (10–90 mg/dl) and THC levels (63–188 ng/ml) indicated that active drug was delivered to subjects dose dependently. Alcohol and marijuana produced dose-related changes in subjective measures of drug effect. Ratings of perceived impairment were identical for the high doses of alcohol and marijuana. Both drugs produced comparable impairment in digit–symbol substitution and word recall tests, but had no effect in time perception and reaction time tests. Alcohol, but not marijuana, slightly impaired performance in a number recognition test. These data are useful for understanding the relative performance impairment produced by alcohol and marijuana at the delivered doses and the relationship between their subjective and behavioral effects.


title: On Controllable Sparse Alternatives to Softmax

abstract: Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature such as softmax, sum-normalization, spherical softmax, and sparsemax, but there is very little understanding in terms how they relate with each other. Further, none of the above formulations offer an explicit control over the degree of sparsity. To address this, we develop a unified framework that encompasses all these formulations as special cases. This framework ensures simple closed-form solutions and existence of sub-gradients suitable for learning via backpropagation. Within this framework, we propose two novel sparse formulations, sparsegen-lin and sparsehourglass, that seek to provide a control over the degree of desired sparsity. We further develop novel convex loss functions that help induce the behavior of aforementioned formulations in the multilabel classification setting, showing improved performance. We also demonstrate empirically that the proposed formulations, when used to compute attention weights, achieve better or comparable performance on standard seq2seq tasks like neural machine translation and abstractive summarization.


title: Learning to rank for recommender systems

abstract: Recommender system aim at providing a personalized list of items ranked according to the preferences of the user, as such ranking methods are at the core of many recommendation algorithms. The topic of this tutorial focuses on the cutting-edge algorithmic development in the area of recommender systems. This tutorial will provide an in depth picture of the progress of ranking models in the field, summarizing the strengths and weaknesses of existing methods, and discussing open issues that could be promising for future research in the community. A qualitative and quantitative comparison between different models will be provided while we will also highlight recent developments in the areas of Reinforcement Learning.


title: LorSLIM: Low Rank Sparse Linear Methods for Top-N Recommendations

abstract: In this paper, we notice that sparse and low-rank structures arise in the context of many collaborative filtering applications where the underlying graphs have block-diagonal adjacency matrices. Therefore, we propose a novel Sparse and Low-Rank Linear Method (Lor SLIM) to capture such structures and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a sparse and low-rank aggregation coefficient matrix W is learned from Lor SLIM by solving an l1-norm and nuclear norm regularized optimization problem. We also develop an efficient alternating augmented Lagrangian method (ADMM) to solve the optimization problem. A comprehensive set of experiments is conducted to evaluate the performance of Lor SLIM. The experimental results demonstrate the superior recommendation quality of the proposed algorithm in comparison with current state-of-the-art methods.


title: High-Speed Medical Imaging in 3D Ultrasound Computer Tomography

abstract: A promising candidate for sensitive imaging of breast cancer is 3D Ultrasound Computer Tomography (3D USCT). So far its clinical applicability for diagnosis has been limited by the duration of the demanding image reconstruction. In this paper we investigate how signal processing and image reconstruction can be accelerated for diagnosis by using heterogeneous hardware. Additionally, the time and costs for real-time system for a future diagnosis and therapy device is estimated. Reusing the device's built-in FPGA-based data acquisition system (DAQ) through reconfiguration results in a speed-up by a factor of 7 for signal processing and by a factor of 2 for image reconstruction. Applying cutting-edge single FPGAs and GPUs, speed-ups by a factor of 10 (FPGA) and 6 (GPU) for signal processing and 15 (FPGA) and 37 (GPU) for image reconstruction were achieved compared to a recent quad-core Intel Core-i7 CPU. Using quad-core CPUs and a cluster of eight GPUs allowed us for the first time to calculate volumes in less than 30 min with an overall speed-up by a factor of 47, enabling a first clinical study. Based on these results we extrapolated that real-time reconstruction for a therapeutic 3D USCT will be possible in the year 2020 if the trend in density follows the ITRS roadmap.


title: Making data structures confluently persistent

abstract: We address a longstanding open problem of [8, 7], and present a general transformation that takes any data structure and transforms it to a confluently persistent data structure. We model this general problem using the concepts of a version DAG (Directed Acyclic Graph) and an instantiation of a version DAG. We introduce the concept of the effective depth of a vertex in the version DAG and use it to derive information theoretic lower bounds on the space expansion of any such transformation for this DAG. We then give a confluently persistent data structure, such that for any version DAG, the time slowdown and space expansion match the information theoretic lower bounds to within a factor of O(log2(¦V¦)).


title: Big Data Transformation Testing Based on Data Reverse Engineering

abstract: During the transformation of huge volume of data, there might exist data mismatch, miscalculation and/or loss of useful data that leads to an unsuccessful data transformation. To check out the occurrence of such possible errors, testing is a crucial requirement. The existing quality testing methods are either unreliable, return biased results, fail to provide answers for data differences or have several limitations which does not treat each and every part of the data into the process. We propose an approach of big data transformation testing based on the concept of data reverse engineering. It is a comprehensive approach that reverse the whole transformation process and does a comparison testing on each and every entry of the data if the original source data can be constructed back from the target data, once successful ETL process is done.


title: What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing

abstract: Driven by new software development processes and testing in clouds, system and integration testing nowadays tends to produce enormous number of alarms. Such test alarms lay an almost unbearable burden on software testing engineers who have to manually analyze the causes of these alarms. The causes are critical because they decide which stakeholders are responsible to fix the bugs detected during the testing. In this paper, we present a novel approach that aims to relieve the burden by automating the procedure. Our approach, called Cause Analysis Model, exploits information retrieval techniques to efficiently infer test alarm causes based on test logs. We have developed a prototype and evaluated our tool on two industrial datasets with more than 14,000 test alarms. Experiments on the two datasets show that our tool achieves an accuracy of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per cause analysis. Due to the attractive experimental results, our industrial partner, a leading information and communication technology company in the world, has deployed the tool and it achieves an average accuracy of 72% after two months of running, nearly three times more accurate than a previous strategy based on regular expressions.


title: Robust fitting of multiple structures: The statistical learning approach

abstract: We propose an unconventional but highly effective approach to robust fitting of multiple structures by using statistical learning concepts. We design a novel Mercer kernel for the robust estimation problem which elicits the potential of two points to have emerged from the same underlying structure. The Mercer kernel permits the application of well-grounded statistical learning methods, among which nonlinear dimensionality reduction, principal component analysis and spectral clustering are applied for robust fitting. Our method can remove gross outliers and in parallel discover the multiple structures present. It functions well under severe outliers (more than 90% of the data) and considerable inlier noise without requiring elaborate manual tuning or unrealistic prior information. Experiments on synthetic and real problems illustrate the superiority of the proposed idea over previous methods.


title: Impact factor: a valid measure of journal quality?

abstract: OBJECTIVES ::: Impact factor, an index based on the frequency with which a journal's articles are cited in scientific publications, is a putative marker of journal quality. However, empiric studies on impact factor's validity as an indicator of quality are lacking. The authors assessed the validity of impact factor as a measure of quality for general medical journals by testing its association with journal quality as rated by clinical practitioners and researchers. ::: ::: ::: METHODS ::: We surveyed physicians specializing in internal medicine in the United States, randomly sampled from the American Medical Association's Physician Masterfile (practitioner group, n = 113) and from a list of graduates from a national postdoctoral training program in clinical and health services research (research group, n = 151). Respondents rated the quality of nine general medical journals, and we assessed the correlation between these ratings and the journals' impact factors. ::: ::: ::: RESULTS ::: The correlation between impact factor and physicians' ratings of journal quality was strong (r2 = 0.82, P = 0.001). The correlation was higher for the research group (r2 = 0.83, P = 0.001) than for the practitioner group (r2 = 0.62, P = 0.01). ::: ::: ::: CONCLUSIONS ::: Impact factor may be a reasonable indicator of quality for general medical journals.


title: Mechatronic design of a variable stiffness robotic arm

abstract: This paper presents the mechatronic design of a robotic arm that is mounted on a ground rover and used to deploy and recover small-scale unmanned aerial vehicles. The arm and rover are part of a network of collaborative robotic agents aiming to enhance current rescue operations by supporting human operators without burdening them with servicing tasks. The robust autonomy of the system, guaranteed in part through the addition of this robotic arm, is a main contributing factor. Design requirements are derived from the context of the rescue mission and a kinematic analysis is provided that leads to a customized design, including variable stiffness joints for compliant interaction with the environment. Experiments demonstrate the system's ability to perform the required Cartesian trajectory control and manipulation tasks, and to achieve a desired variable end-effector compliance.


title: Adaptive Selective Verification

abstract: We consider Denial of Service (DoS) attacks within the province of a shared channel model in which attack rates may be large but are bounded and client request rates vary within fixed bounds. In this setting it is shown that the clients can respond effectively to an attack by using bandwidth as a payment scheme and time-out windows to adaptively boost request rates. The server will be able to process client requests with high probability while pruning out most of the attack by selective random sampling. Our protocol, which we call Adaptive Selective Verification (ASV) is shown to be efficient in terms of bandwidth consumption using both a theoretical model and network simulations. It differs from previously-investigated adaptive mechanisms for bandwidth-based payment by requiring very limited state on the server.


title: A Coarse-Fine Network for Keypoint Localization

abstract: We propose a coarse-fine network (CFN) that exploits multi-level supervisions for keypoint localization. Recently, convolutional neural networks (CNNs)-based methods have achieved great success due to the powerful hierarchical features in CNNs. These methods typically use confidence maps generated from ground-truth keypoint locations as supervisory signals. However, while some keypoints can be easily located with high accuracy, many of them are hard to localize due to appearance ambiguity. Thus, using strict supervision often fails to detect keypoints that are difficult to locate accurately To target this problem, we develop a keypoint localization network composed of several coarse detector branches, each of which is built on top of a feature layer in a CNN, and a fine detector branch built on top of multiple feature layers. We supervise each branch by a specified label map to explicate a certain supervision strictness level. All the branches are unified principally to produce the final accurate keypoint locations. We demonstrate the efficacy, efficiency, and generality of our method on several benchmarks for multiple tasks including bird part localization and human body pose estimation. Especially, our method achieves 72.2% AP on the 2016 COCO Keypoints Challenge dataset, which is an 18% improvement over the winning entry.


title: Design and Analysis of the DC–DC Converter With a Frequency Hopping Technique for EMI Reduction

abstract: As more circuit functions are integrated within a single die or small integrated package, the number of electromagnetic interference (EMI) problems caused by dc–dc converters is growing. In this paper, the dominant electromagnetic emission source on the dc–dc converter is identified by analyzing the power spectrum of the nodes. The noise scanner method is applied to a packaged single-chip prototype dc–dc converter. The results show that the dominant source of electromagnetic emission is the switching node of the converter, not the output node. The frequency hopping technique (FHT) is applied to the dc–dc converter to reduce the emission at the switching node, and its effectiveness is analyzed mathematically and experimentally. The mathematical model of FHTs is proposed to analyze its effectiveness in reducing EMI, and the optimal design using the FHT and dead-time control is fabricated with 0.18- $\mu \text{m}$ CMOS technology. The measured power spectrum reduction by using the optimal FHT design and dead-time control at the switching node is 16 dB. The EMI reduction amount of the proposed design measured by the IC-stripline method is 12.6 dB at the fundamental switching frequency.


title: Comparison of linear voice coil and reluctance actuators for high-precision applications

abstract: This paper concerns the comparison of three linear reluctance actuators and four voice coil actuators for high-precision applications. The short-stroke actuators with reasonably low variation of the force are optimized on actuator mass and power dissipation. For actuators with a significantly high force variation, the configuration is modified with the objective to improve the actuator properties for high-precision applications. Finally, the comparison is made on performance regarding, force, mass, losses and stiffness.


title: A Robot Hand with T-MPSOM Neural Networks in a Model of the Human Haptic System

abstract: We have developed an 8 d.o.f. robot hand, which has been tested with two computational models of haptic perception. Each model uses a variant of the novel self-organizing neural network, the Tensor-Multiple Peak Self-Organizing Map (T-MPSOM). One of the models uses a variant of the T-MPSOM that multiplies the activity corresponding to each of the two input vectors, while the other uses a variant that sums them. The computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. Both models were capable of shape categorization, and in addition, one of the models was able to discriminate individual objects.


title: Cerebral Function of the Guinea Pig Neonate after Chronic Intrauterine Exposure to Khat (Catha edulis Forsk.)

abstract: Cerebral function in normoxia and its reactions to standard periods of hypoxia of increasing severity were studied in 30 newborn guinea pigs less than 3 days old. Intrauterine growth retardation was i


INTELLIGENT VIRTUAL ASSISTANT FOR GAMIFIED ENVIRONMENTS


title: Composite distance metric integration by leveraging multiple experts' inputs and its application in patient similarity assessment

abstract: In the real world, it is common that different experts have different opinions on the same problem due to their different experience. How to come up with a consistent decision becomes a critical issue. As an example, patient similarity assessment is an important task in the context of patient cohort identification for comparative effectiveness studies and clinical decision-support applications. The goal is to derive clinically meaningful distance metric to measure the similarity between patients represented by their key clinical indicators. It is desirable to learn the distance metric based on experts' knowledge of clinical similarity among subjects. However, often different physicians have different understandings of patient similarity based on the specifics of the cases. The distance metric learned for each individual physician often leads to a limited view of the true underlying distance metric. The key challenge will be how to integrate the individual distance metrics obtained for a group of physicians into a globally consistent unified metric. ::: ::: To achieve this goal, we propose the composite distance integration (Comdi) approach in this paper. Comdi first constructs discriminative neighborhoods from each individual metrics, then it combines all discriminative information in those neighborhoods to learn a single optimal distance metric. We formulate Comdi as a quadratic optimization problem and propose an efficient alternating strategy to find the solution. Besides learning a globally consistent metric, Comdi provides an elegant way to share knowledge across multiple experts without sharing the underlying data, which lowers the risk of disclosing private data. Our experiments on several benchmark data sets show approximately 10% improvement in classification accuracy over baseline methods, which suggests that Comdi is an effective and general metric learning approach. We also demonstrate two case studies on applying Comdi to real-world clinic data sets. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 54–69, 2012 © 2012 Wiley Periodicals, Inc.


title: A measurement study on the application-level performance of LTE

abstract: Many of today's Internet applications such as mobile web browsing and live video streaming are delay and throughput sensitive. In face of the great success of cellular networking, especially with the advent of the high-speed LTE access tech-nology, it is noteworthy that there is little consensus on the performance experienced by applications running over cellular networks. In this paper we present application-level performance results measured in a major commercial LTE network. We replicate measurements in a wired access network to provide a reference for the wireless results. We investigate the performance of common web application scenarios over LTE. In addition, we deploy controlled measurement nodes to discover transparent middleboxes in the LTE network. The introduction of middle-boxes to LTE results in a faster connection establishment on the client side and notable performance gain for HTTP. However, this improvement comes at the price of ambiguity as some middlebox operations may introduce unnecessary timeouts. Further, we pinpoint LTE specific delays that arise from network signalling, energy saving algorithms and Hybrid Automatic Repeat reQuest (HARQ). Our analysis provides insights into the interaction between transport protocols and LTE.


title: Digital Signal Processing Methodologies for Conventional Digital Medical Ultrasound Imaging System

abstract: Ultrasound imaging is an efficient, noninvasive, method for med ical diagnosis. A commonly used approach to image acquisition in ultrasound system is digital beamforming. Digital beamforming, as applied to the medical ultrasound, is defined as phase align ment and summation of signals that are generated fro m a co mmon source, by received at different times by a mu lti-elements ultrasound transducer. In this paper first: we tested all signal processing methodologies for digital beamforming which included: the effect of over samp ling techniques, single trans mit focusing and their limitations, the apodization technique and its effect to reduce the sidelobes, the analytical envelope detection using digital finite impulse response (FIR) filter appro ximations for the Hilbert transformat ion and how to co mpress the dynamic range to achieve the desired dynamic range for display (8 bits). Here the image was reconstructed using physical array elements and virtual array elements for linear and phase array probe. The results shown that virtual array elements were given well results in linear array image reconstruction than physical array elements, because it provides additional number of lines. Ho wever, physical array elements shown a good results in linear phase array reconstruction (steering) than virtual array elements, because the active elements number (Aperture) is less than in physical array elements. We checked the quality of the image using quantitative entropy. Second: a modular FPGA-based 16 channel digital u ltrasound beamforming with embedded DSP for ultrasound imaging is presented. The system is imp lemented in Virtex-5 FPGA (Xilin x, Inc.). The system consists of: tw o 8 channels block, the DSP wh ich co mposed of the FIR Hilbert filter bl ock to obtain the quadrature components, the fractional delay filter block (in-phase filter) to co mpensate the delay when we were used a high FIR order, and the envelope detection block to compute the envelope of the in-phase and quadrature components. The Hilbert filter is imp lemented in the form whereby the zero tap coefficients were not computed and therefore an order L filter used only L/2 mu ltiplications. This reduced the computational time by a half. Fro m the implementation result the total estimated power consumption equals 4732.87 mW and the device utilization was acceptable. It is possible for the system to accept other devices for further processing. Also it is possible to build 16-,32-, and 64-channel beamformer. The hardware architecture of the design provided flexib ility for beamforming.


title: Micro strip Patch Antenna for 2.4 GHZ Wireless Applications

abstract: A small Microstrip patch antenna is introduced .the antenna is designed to function in 2.4 GHz wireless radio band .it achieves return loss -38 dB by the using fr4 substrate under the patch .the antenna has many practical applications like in WLAN, WIFI, etc as illustrated in detail herein .the patch design is simulated in An Soft HFSS Vs 10 software .the result showed satisfactory performance.


title: Faster Transit Routing by Hyper Partitioning

abstract: We present a preprocessing-based acceleration technique for computing bi-criteria Pareto-optimal journeys in public transit networks, based on the well-known RAPTOR algorithm [Delling et al 2015]. Our key idea is to first partition a hypergraph into cells, in which vertices correspond to routes (e.g., bus lines) and hyperedges to stops, and to then mark routes sufficient for optimal travel across cells. The query can then be restricted to marked routes and those in the source and target cells. This results in a practical approach, suitable for networks that are too large to be efficiently handled by the basic RAPTOR algorithm.


title: Automated and safe vulnerability assessment

abstract: As the number of system vulnerabilities multiplies in recent years, vulnerability assessment has emerged as a powerful system security administration tool that can identify vulnerabilities in existing systems before they are exploited. Although there are many commercial vulnerability assessment tools in the market, none of them can formally guarantee that the assessment process never compromises the computer systems being tested. This paper proposes a featherweight virtual machine (FVM) technology to address the safety issue associated with vulnerability testing. Compared with other virtual machine technologies, FVM is designed to facilitate sharing between virtual machines but still provides strong protection between them. The FVM technology allows a vulnerability assessment tool to test an exact replica of a production-mode network service, including both hardware and system software components, while guaranteeing that the production-mode network service is fully isolated from the testing process. In addition to safety, the vulnerability assessment support system described in this paper can also automate the entire process of vulnerability testing and thus for the first time makes it feasible to run vulnerability testing autonomously and frequently. Experiments on a Windows-based prototype show that Nessus assessment results against an FVM virtual machine are identical to those against a real machine. Furthermore, modifications to the file system and registry state made by vulnerability assessment runs are completely isolated from the host machine. Finally, the performance impact of vulnerability assessment runs on production network services is as low as 3%


title: Intrusion Detection System for IEC 60870-5-104 based SCADA networks

abstract: Increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in Smart Grids potentially means greater susceptibility to malicious attackers. SCADA systems with legacy communication infrastructure have inherent cyber-security vulnerabilities as these systems were originally designed with little consideration of cyber threats. In order to improve cyber-security of SCADA networks, this paper presents a rule-based Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method, which includes signature-based and model-based approaches tailored for SCADA systems. The proposed signature-based rules can accurately detect several known suspicious or malicious attacks. In addition, model-based detection is proposed as a complementary method to detect unknown attacks. Finally, proposed intrusion detection approaches for SCADA networks are implemented and verified via Snort rules.


title: Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information

abstract: Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.


title: State of the Art Authentication, Access Control, and Secure Integration in Smart Grid

abstract: The smart grid (SG) is a promising platform for providing more reliable, efficient, and cost effective electricity to the consumers in a secure manner. Numerous initiatives across the globe are taken by both industry and academia in order to compile various security issues in the smart grid network. Unfortunately, there is no impactful survey paper available in the literature on authentications in the smart grid network. Therefore, this paper addresses the required objectives of an authentication protocol in the smart grid network along with the focus on mutual authentication, access control, and secure integration among different SG components. We review the existing authentication protocols, and analyze mutual authentication, privacy, trust, integrity, and confidentiality of communicating information in the smart grid network. We review authentications between the communicated entities in the smart grid, such as smart appliance, smart meter, energy provider, control center (CC), and home/building/neighborhood area network gateways (GW). We also review the existing authentication schemes for the vehicle-to-grid (V2G) communication network along with various available secure integration and access control schemes. We also discuss the importance of the mutual authentication among SG entities while providing confidentiality and privacy preservation, seamless integration, and required access control with lower overhead, cost, and delay. This paper will help to provide a better understanding of current authentication, authorization, and secure integration issues in the smart grid network and directions to create interest among researchers to further explore these promising areas.


title: Adaptive term frequency normalization for BM25

abstract: A key component of BM25 contributing to its success is its sub linear term frequency (TF) normalization formula. The scale and shape of this TF normalization component is controlled by a parameter k1, which is generally set to a term-independent constant. We hypothesize and show empirically that in order to optimize retrieval performance, this parameter should be set in a term-specific way. Following this intuition, we propose an information gain measure to directly estimate the contributions of repeated term occurrences, which is then exploited to fit the BM25 function to predict a term-specific k1. Our experiment results show that the proposed approach, without needing any training data, can efficiently and automatically estimate a term-specific k1, and is more effective and robust than the standard BM25.


Distributional Clustering of English Words


title: A 3-D printed lightweight cavity-backed monocone antenna

abstract: A lightweight exponentially tapered cavity-backed mono-cone antenna made by 3-D printing and conductive paint coating is proposed. To make the 3-D printed structure be metalized, three layers of conductive paint are used. To verify the proposed fabrication method, the 3-D printed antenna was fabricated and its return loss and far-field radiation characteristics were analyzed.


RFID and Wireless Sensor Networks


title: Fraud Analysis Approaches in the Age of Big Data - A Review of State of the Art

abstract: Fraud is a criminal practice for illegitimate gain of wealth or tampering information. Fraudulent activities are of critical concern because of their severe impact on organizations, communities as well as individuals. Over the last few years, various techniques from different areas such as data mining, machine learning, and statistics have been proposed to deal with fraudulent activities. Unfortunately, the conventional approaches display several limitations, which were addressed largely by advanced solutions proposed in the advent of Big Data. In this paper, we present fraud analysis approaches in the context of Big Data. Then, we study the approaches rigorously and identify their limits by exploiting Big Data analytics.


title: Robust Online Time Series Prediction with Recurrent Neural Networks

abstract: Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare. However the real data is often complicated with anomalies and change points, which can lead the learned models deviating from the underlying patterns of the time series, especially in the context of online learning mode. In this paper we present an adaptive gradient learning method for recurrent neural networks (RNN) to forecast streaming time series in the presence of anomalies and change points. We explore the local features of time series to automatically weight the gradients of the loss of the newly available observations with distributional properties of the data in real time. We perform extensive experimental analysis on both synthetic and real datasets to evaluate the performance of the proposed method.


title: Implementation and verification of a generic universal memory controller based on UVM

abstract: This paper presents a coverage driven constraint random based functional verification method based on the Universal Verification Methodology (UVM) using System Verilog for generic universal memory controller architecture. This universal memory controller is looking forward to improving the performance of the existing memory controllers through a complete integration of the existing memory controllers features in addition of providing novel features. It also reduces the consumed power through providing high power consumption control due to its proposed different power levels supported to fit all power scenarios. While implementing a worthy architecture like the proposed generic universal memory controller, UVM is the best choice to build well-constructed, high controlled and reusable verification environment to efficiently verify it. More than 200 coverage points have been covered to verify the validation of the integrated features which makes the proposed universal memory controller replaces the existing controllers on the scene as it provides all of their powerful features in addition of novel features to control two of the most dominated types of memory; FLASH and DRAM through one memory controller.


title: Making the most of information technology & systems usage

abstract: Literature review on information technology & systems (IT/S) from January 2000 to December 2014.Total 152 relevant articles were found and classified into four main domains.Literature on IT/S continuous usage lacks a clear roadmap and agenda.Most research conducted in East Asia and North America.Technology acceptance model (TAM) the most used theory. This detailed literature review has considered a relatively large quantity (152 total) of scholarly empirical publications, conference proceedings, books and popular market reports published over the last 15years, i.e., from January 2000 to December 2014, in the field of human continuous usage behavior and in the context of information technology/systems. Based on the search results, the literature was synthesized, segregated into four major domains according to the purpose, nature and usage of the information technology/systems. The authors believe that this segregation within the information technology & systems continuous usage literature provides greater scalability, flexibility and space for future research. Moreover, this proposed segregation allows for future research to include more 'systems' in each category depending on the usage, relevance and nature of the 'systems' that will evolve over the period of time. Scalability will provide more insights and ideas that will help future research investigate and propose domain-specific conceptual or business models that will help facilitate an understanding of information technology/systems continuous usage according to the nature of the 'system.' Conclusions and recommendations are drawn and priorities are proposed for continuing research.


title: Concentrations of Anthocyanins in Common Foods in the United States and Estimation of Normal Consumption

abstract: Anthocyanins (ACNs) are water-soluble plant pigments that have important functions in plant physiology as well as possible health effects. Over 100 common foods were screened for ACNs, and 24 of them were found to contain ACNs. Concentrations of total ACNs varied considerably from 0.7 to 1480 mg/100 g of fresh weight in gooseberry ('Careless' variety) and chokeberry, respectively. Not only does the concentration vary, but the specific anthocyanins present in foods are also quite different. Only six common aglycones, delphinidin, cyanidin, petunidin, pelargonidin, peonidin, and malvidin, were found in all of these foods. However, their sugar moieties and acylation patterns varied from food to food. Results from this study will add to the available data for the USDA Nutrient Database of flavonoids. On the basis of the concentration data and updated food intake data from NHANES 2001-2002, the daily intake of ACNs is estimated to be 12.5 mg/day/person in the United States. Of the different aglycones, cyanidin, delphinidin, and malvidin were estimated to contribute 45, 21, and 15%, respectively, of the total ACN intake. Nonacylated contributed 77% compared to 23% from acylated ACNs.


title: A High-Power Low-Distortion GaAs HBT Power Amplifier for Mobile Terminals Used in Broadband Wireless Applications

abstract: This paper describes technologies of miniaturized high-power low-distortion GaAs HBT power amplifiers with a low-voltage operation for mobile terminals used in 5-6 GHz broadband wireless applications. In conjunction with diode-based linearizing techniques, wideband matching network techniques including trap circuits for second harmonics allow us to obtain a compact broadband power amplifier module with harmonic filtering, achieving the high linear output power at a low supply voltage together with the low distortion and the low second-harmonic spurious outputs in a wide frequency range. The fabricated power amplifier exhibited linear output power levels of 21 and 22 dBm at EVM values of 2.0 and 3.0%, respectively, measured with 54 Mb/s 64-QAM-OFDM signals at a supply voltage of 3.3 V in a frequency range of 5-6 GHz. Second harmonic spurious outputs below -35 dBc were also attained.


Flexible Security Policy Architecture for Trusted Operating Systems


title: Simple Folded Monopole Slot Antenna for Penta-Band Clamshell Mobile Phone Application

abstract: An internal clamshell mobile phone antenna using a simple dual-band folded monopole slot for covering penta bands of WWAN operation is presented. The folded monopole slot has a length of about 60 mm only and is easily printed on the top portion (10 times 40 mm2) of the main circuit board and suitable to be placed at the hinge of the clamshell mobile phone. By further placing the connecting strip between the two ground planes to be in the vicinity of the open end of the monopole slot, the two (main and upper) ground planes of the clamshell mobile phone can also be excited to be efficient radiators (dipole-like chassis or groundplane modes excited through the strong electric field occurred at the slot's open end). By incorporating the resonant modes contributed from both the monopole slot and the two ground planes, two wide bands at about 900 and 1900 MHz are formed, which easily cover GSM850/900/1800/1900/UMTS operation. Owing to the additional resonant modes contributed by the two ground planes, the use of a simple folded monopole slot is sufficient to cover penta-band WWAN operation. This leads to compact size for the proposed clamshell mobile phone antenna. Details of the proposed antenna, including its SAR results, are presented.


A Comparison of Variant Calling Pipelines Using Genome in a Bottle as a Reference


Counselor Educators' Preparation in Ethics


title: Holographic Projection Technology: The World is Changing

abstract: This research papers examines the new technology of Holographic Projections. It highlights the importance and need of this technology and how it represents the new wave in the future of technology and communications, the different application of the technology, the fields of life it will dramatically affect including business, education, telecommunication and healthcare. The paper also discusses the future of holographic technology and how it will prevail in the coming years highlighting how it will also affect and reshape many other fields of life, technologies and businesses.


title: Detailed 3D Model Driven Single View Scene Understanding

abstract: We present a data driven approach to holistic scene understanding. From a single image of an indoor scene, our approach estimates its detailed 3D geometry, i.e. The location of its walls and floor, and the 3D appearance of its containing objects, as well as its semantic meaning, i.e. A prediction of what objects it contains. This is made possible by using large datasets of detailed 3D models alongside appearance based detectors. We first estimate the 3D layout of a room, and extrapolate 2D object detection hypotheses to three dimensions to form bounding cuboids. Cuboids are converted to detailed 3D models of the predicted semantic category. Combinations of 3D models are used to create a large list of layout hypotheses for each image -- where each layout hypothesis is semantically meaningful and geometrically plausible. The likelihood of each layout hypothesis is ranked using a learned linear model -- and the hypothesis with the highest predicted likelihood is the final predicted 3D layout. Our approach is able to recover the detailed geometry of scenes, provide precise segmentation of objects in the image plane, and estimate objects' pose in 3D.


title: iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach

abstract: Before becoming the native proteins during the biosynthesis, their polypeptide chains created by ribosome's translating mRNA will undergo a series of “product-forming” steps, such as cutting, folding, and posttranslational modification (PTM). Knowledge of PTMs in proteins is crucial for dynamic proteome analysis of various human diseases and epigenetic inheritance. One of the most important PTMs is the Arg- or Lys-methylation that occurs on arginine or lysine, respectively. Given a protein, which site of its Arg (or Lys) can be methylated, and which site cannot? This is the first important problem for understanding the methylation mechanism and drug development in depth. With the avalanche of protein sequences generated in the postgenomic age, its urgency has become self-evident. To address this problem, we proposed a new predictor, called iMethyl-PseAAC. In the prediction system, a peptide sample was formulated by a 346-dimensional vector, formed by incorporating its physicochemical, sequence evolution, biochemical, and structural disorder information into the general form of pseudo amino acid composition. It was observed by the rigorous jackknife test and independent dataset test that iMethyl-PseAAC was superior to any of the existing predictors in this area.


title: GTS: A Fast and Scalable Graph Processing Method based on Streaming Topology to GPUs

abstract: A fast and scalable graph processing method becomes increasingly important as graphs become popular in a wide range of applications and their sizes are growing rapidly. Most of distributed graph processing methods require a lot of machines equipped with a total of thousands of CPU cores and a few terabyte main memory for handling billion-scale graphs. Meanwhile, GPUs could be a promising direction toward fast processing of large-scale graphs by exploiting thousands of GPU cores. All of the existing methods using GPUs, however, fail to process large-scale graphs that do not fit in main memory of a single machine. Here, we propose a fast and scalable graph processing method GTS that handles even RMAT32 (64 billion edges) very efficiently only by using a single machine. The proposed method stores graphs in PCI-E SSDs and executes a graph algorithm using thousands of GPU cores while streaming topology data of graphs to GPUs via PCI-E interface. GTS is fast due to no communication overhead and scalable due to no data duplication from graph partitioning among machines. Through extensive experiments, we show that GTS consistently and significantly outperforms the major distributed graph processing methods, GraphX, Giraph, and PowerGraph, and the state-of-the-art GPU-based method TOTEM.


Joint Image Sharpening and Denoising by 3D Transform-Domain Collaborative Filtering


title: Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret

abstract: We present an efficient second-order algorithm with $\tilde{O}(\frac{1}{\eta}\sqrt{T})$ regret for the bandit online multiclass problem. The regret bound holds simultaneously with respect to a family of loss functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm of the competitor. The family of loss functions ranges from hinge loss ($\eta=0$) to squared hinge loss ($\eta=1$). This provides a solution to the open problem of (J. Abernethy and A. Rakhlin. An efficient bandit algorithm for $\sqrt{T}$-regret in online multiclass prediction? In COLT, 2009). We test our algorithm experimentally, showing that it also performs favorably against earlier algorithms.


title: Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges

abstract: Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI’s, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard.


title: Manual Therapy, Exercise, and Traction for Patients With Cervical Radiculopathy: A Randomized Clinical Trial

abstract: BACKGROUND ::: To date, optimal strategies for the management of patients with cervical radiculopathy remain elusive. Preliminary evidence suggests that a multimodal treatment program consisting of manual therapy, exercise, and cervical traction may result in positive outcomes for patients with cervical radiculopathy. However, limited evidence exists to support the use of mechanical cervical traction in patients with cervical radiculopathy. ::: ::: ::: OBJECTIVE ::: The purpose of this study was to examine the effects of manual therapy and exercise, with or without the addition of cervical traction, on pain, function, and disability in patients with cervical radiculopathy. ::: ::: ::: DESIGN ::: This study was a multicenter randomized clinical trial. ::: ::: ::: SETTING ::: The study was conducted in orthopedic physical therapy clinics. ::: ::: ::: PATIENTS ::: Patients diagnosed with cervical radiculopathy (N=81) were randomly assigned to 1 of 2 groups: a group that received manual therapy, exercise, and intermittent cervical traction (MTEXTraction group) and a group that received manual therapy, exercise, and sham intermittent cervical traction (MTEX group). ::: ::: ::: INTERVENTION ::: Patients were treated, on average, 2 times per week for an average of 4.2 weeks. ::: ::: ::: MEASUREMENTS ::: Outcome measurements were collected at baseline and at 2 weeks and 4 weeks using the Numeric Pain Rating Scale (NPRS), the Patient-Specific Functional Scale (PSFS), and the Neck Disability Index (NDI). ::: ::: ::: RESULTS ::: There were no significant differences between the groups for any of the primary or secondary outcome measures at 2 weeks or 4 weeks. The effect size between groups for each of the primary outcomes was small (NDI=1.5, 95% confidence interval [CI]=-6.8 to 3.8; PSFS=0.29, 95% CI=-1.8 to 1.2; and NPRS=0.52, 95% CI=-1.8 to 1.2). ::: ::: ::: LIMITATIONS ::: The use of a nonvalidated clinical prediction rule to diagnose cervical radiculopathy and the lack of a control group without treatment were limitations of this study. ::: ::: ::: CONCLUSIONS ::: The results suggest that the addition of mechanical cervical traction to a multimodal treatment program of manual therapy and exercise yields no significant additional benefit to pain, function, or disability in patients with cervical radiculopathy.


title: A road to independent living with smart homes for people with disabilities

abstract: Nearly one billion people are suffering from various disabilities around the world. People in developed countries have resources, facilities and technologies to cope up while in developing countries, they suffer more due to lack of facilities and assistive technologies. Many technologies have been considered as cost effective solution to overcome the limitations in developing countries and Smart Home (SH) is one them. SH is a vision of home automation system that interacts with the users to provide comfort and ease of access. SH embedded with assistive technologies can provide better living facilities for disabled people. As a result they can control home appliances without movement and interact better with their surroundings. It could help improving their confidence and self-esteem thus, enhancing the quality of life. In this study, we have presented SH system for disabled people that can improve their lives, focusing on the key features, major challenges and potentials of SHs.


title: Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation

abstract: Sarcasm is a form of speech in which speakers say the opposite of what they truly mean in order to convey a strong sentiment. In other words,"Sarcasm is the giant chasm between what I say, and the person who doesn't get it.". In this paper we present the novel task of sarcasm interpretation, defined as the generation of a non-sarcastic utterance conveying the same message as the original sarcastic one. We introduce a novel dataset of 3000 sarcastic tweets, each interpreted by five human judges. Addressing the task as monolingual machine translation (MT), we experiment with MT algorithms and evaluation measures. We then present SIGN: an MT based sarcasm interpretation algorithm that targets sentiment words, a defining element of textual sarcasm. We show that while the scores of n-gram based automatic measures are similar for all interpretation models, SIGN's interpretations are scored higher by humans for adequacy and sentiment polarity. We conclude with a discussion on future research directions for our new task.


title: Feature-preserving T-mesh construction using skeleton-based polycubes

abstract: This paper presents a novel algorithm which uses skeleton-based polycube generation to construct feature-preserving T-meshes. From the skeleton of the input model, we first construct initial cubes in the interior. By projecting corners of interior cubes onto the surface and generating a new layer of boundary cubes, we split the entire interior domain into different cubic regions. With the splitting result, we perform octree subdivision to obtain T-spline control mesh or T-mesh. Surface features are classified into three groups: open curves, closed curves and singularity features. For features without introducing new singularities like open or closed curves, we preserve them by aligning to the parametric lines during subdivision, performing volumetric parameterization from frame field, or modifying the skeleton. For features introducing new singularities, we design templates to handle them. With a valid T-mesh, we calculate rational trivariate T-splines and extract Bezier elements for isogeometric analysis. We present a novel algorithm which uses skeleton-based polycube generation to construct T-meshes.Three kinds of features are preserved: open curves, closed curves, singularity features.With a valid T-mesh, we calculate trivariate T-splines for isogeometric analysis.


title: A real-time face tracker

abstract: The authors present a real-time face tracker. The system has achieved a rate of 30+ frames/second using an HP-9000 workstation with a frame grabber and a Canon VC-Cl camera. It can track a person's face while the person moves freely (e.g., walks, jumps, sits down and stands up) in a room. Three types of models have been employed in developing the system. First, they present a stochastic model to characterize skin color distributions of human faces. The information provided by the model is sufficient for tracking a human face in various poses and views. This model is adaptable to different people and different lighting conditions in real-time. Second, a motion model is used to estimate image motion and to predict the search window. Third, a camera model is used to predict and compensate for camera motion. The system can be applied to teleconferencing and many HCI applications including lip reading and gaze tracking. The principle in developing this system can be extended to other tracking problems such as tracking the human hand.


title: Mining gene expression data by interpreting principal components

abstract: BackgroundThere are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.ResultsWe present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation.ConclusionWe provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10's to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.


title: The streamlined cognitive walkthrough method, working around social constraints encountered in a software development company

abstract: The cognitive walkthrough method described by Wharton et al. may be difficult to apply in a large software development company because of social constraints that exist in such companies. Managers, developers, and other team members are pressured for time, tend to lapse into lengthy design discussions, and are sometimes defensive about their user-interface designs. By enforcing four ground rules, explicitly defusing defensiveness, and streamlining the cognitive walkthrough method and data collection procedures, these social constraints can be overcome, and useful, valid data can be obtained. This paper describes a modified cognitive walkthrough process that accomplishes these goals, and has been applied in a large software development company.


title: A wearable haptic game controller

abstract: This paper outlines the development of a wearable game controller incorporating vibrotacticle haptic feedback that provides a low cost, versatile and intuitive interface for controlling digital games. The device differs from many traditional haptic feedback implementation in that it combines vibrotactile based haptic feedback with gesture based input, thus becoming a two way conduit between the user and the virtual environment. The device is intended to challenge what is considered an “interface” and draws on work in the area of Actor-Network theory to purposefully blur the boundary between man and machine. This allows for a more immersive experience, so rather than making the user feel like they are controlling an aircraft the intuitive interface allows the user to become the aircraft that is controlled by the movements of the user's hand. This device invites playful action and thrill. It bridges new territory on portable and low cost solutions for haptic controllers in a gaming context.


Reconstruction of 3D virtual buildings from 2D architectural floor plans


title: Web scraping for automated water quality monitoring system: A case study of PDAM Surabaya

abstract: The need for a better online water quality monitoring system to control and provide drinking water treatment processes is proven. Water utility company in Surabaya that called as PDAM Surabaya has few reservoirs in their water supply system which are being monitored by WTW IQ SensorNet 2020 XT. However, on that sensor devices while it can provide some of the water quality parameters value information but the sensor is passive and the internal data is still stored in the sensor itself. To solve the problem, we proposed an application of data logger to manage data collections online water quality monitoring system by using web scraping. This application built by using Python language, the application is able to collect data from the sensor by using web scraping. We have utilized Beautiful Soap library and store the data in SQL. In these research, our experiment shows the performance of the application by measuring the accuracy of data that has been scraped from sensors are around 99%, the error rate less than 1%, and MSE (Mean Square Error) around 0.35%. We also calculated the growth of the data size that is gradually increasing and also measured the correlation between the data size with the delay which shows the value of our data is around 0.0000002739. Its means that our application is able to work in real-time and the delay is not affected by the size of data.


Learning User Behaviors for Advertisements Click Prediction


title: Auto-Join: Joining Tables by Leveraging Transformations

abstract: Traditional equi-join relies solely on string equality comparisons to perform joins. However, in scenarios such as ad-hoc data analysis in spreadsheets, users increasingly need to join tables whose join-columns are from the same semantic domain but use different textual representations, for which transformations are needed before equi-join can be performed. We developed Auto-Join, a system that can automatically search over a rich space of operators to compose a transformation program, whose execution makes input tables equi-join-able. We developed an optimal sampling strategy that allows Auto-Join to scale to large datasets efficiently, while ensuring joins succeed with high probability. Our evaluation using real test cases collected from both public web tables and proprietary enterprise tables shows that the proposed system performs the desired transformation joins efficiently and with high quality.


title: Customer churn prediction in telecommunications

abstract: This paper presents a new set of features for land-line customer churn prediction, including 2 six-month Henley segmentation, precise 4-month call details, line information, bill and payment information, account information, demographic profiles, service orders, complain information, etc. Then the seven prediction techniques (Logistic Regressions, Linear Classifications, Naive Bayes, Decision Trees, Multilayer Perceptron Neural Networks, Support Vector Machines and the Evolutionary Data Mining Algorithm) are applied in customer churn as predictors, based on the new features. Finally, the comparative experiments were carried out to evaluate the new feature set and the seven modelling techniques for customer churn prediction. The experimental results show that the new features with the six modelling techniques are more effective than the existing ones for customer churn prediction in the telecommunication service field.


title: A new hybrid algorithm for finding the lowest minima of potential surfaces: Approach and application to peptides

abstract: A new algorithm is presented for finding the global minimum, and other low-lying minima, of a potential energy surface (PES) of biological molecules. The algorithm synergetically combines three well-known global optimization methods: the diffusion equation method (DEM), which involves smoothing the PES; a simulated annealing (SA) algorithm; and evolutionary programming (EP), whose population-oriented approach allows for a parallel search over different regions of the PES. Tests on five peptides having between 6 and 9 residues show that the code implementing the new combined algorithm is efficient and is found to outperform the constituent methods, DEM and SA. Results of the algorithm, in the gas phase and with the GBSA implicit solvent model, are compared with crystallographic data for the test peptides; good accord is found in all cases. Also, for all but one of the examples, our hybrid algorithm finds a minimum deeper than those obtained by a very extensive scan. TINKERs implementation of the OPLS-AA force field is employed for the structure prediction. The results show that the new algorithm is a powerful structure predictor, when a reliable potential function is available. Our implementation of the algorithm is time-efficient, and requires only modest computational resources. Work is underway on applications of the new algorithm to structural prediction of proteins and other biological macro-molecules. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011


title: Modification of radiation hardened triggers for special receivers

abstract: In this paper typical approaches to mitigate single event effects using spatial redundancy were reviewed. The specificity of receivers input tracts were described. The low power techniques as the clock gating have a significant impact on the work conditions of a digital receiver. A novel modification was proposed for storage cells which are insensitive to radiation-induced single event upsets such as TMR and DICE flip-flop triggers. This new technique is aimed for implementation in high density ASICs of receivers. It's shown that the new flip-flops have quite similar failure tolerance with 20% percent area reduction. The proposed DICE flip-flop was used for the logical synthesis of the GNSS input channel. The received channel has about 10% percent less area.


title: A sentiment-enhanced personalized location recommendation system

abstract: Although online recommendation systems such as recommendation of movies or music have been systematically studied in the past decade, location recommendation in Location Based Social Networks (LBSNs) is not well investigated yet. In LBSNs, users can check in and leave tips commenting on a venue. These two heterogeneous data sources both describe users' preference of venues. However, in current research work, only users' check-in behavior is considered in users' location preference model, users' tips on venues are seldom investigated yet. Moreover, while existing work mainly considers social influence in recommendation, we argue that considering venue similarity can further improve the recommendation performance. In this research, we ameliorate location recommendation by enhancing not only the user location preference model but also recommendation algorithm. First, we propose a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which are processed using sentiment analysis techniques. Second, we develop a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation. Using two datasets extracted from the location based social networks Foursquare, experiment results demonstrate that the proposed hybrid preference model can better characterize user preference by maintaining the preference consistency, and the proposed algorithm outperforms the state-of-the-art methods.


title: Big Data Infrastructure for Aviation Data Analytics

abstract: This paper describes our approach towards developing and using a big data infrastructure for analyzing aviation data. In this paper, we briefly introduce our data sources, nature of data collected, cluster design, data loading and storage strategy and our language and library of choice for analytics and visualization. We present some of the analytics we have implemented for health monitoring of auxiliary power units (APUs) using our big data infrastructure. Best practices to implement big data analytics and pitfalls are discussed and substantiated with our experiences.


title: 30 Years of Adaptive Neural Networks : Perceptron , Madaline , and Backpropagation

abstract: This chapter contains sections titled: Introduction Fundamental Concepts Adaptation ??????-?????? The Minimal Disturbance Principle Error Correction Rules ??????-?????? Single Threshold Element Error Correction Rules ??????-?????? Multi-Element Networks Steepest-Descent Rules ??????-?????? Single Threshold Element Steepest-Descent Rules ??????-?????? Multi-Element Networks Summary Acknowledgments Bibliography


title: Active Learning in Collaborative Filtering Recommender Systems

abstract: In Collaborative Filtering Recommender Systems user’s preferences are expressed in terms of rated items and each rating allows to improve system prediction accuracy. However, not all of the ratings bring the same amount of information about the user’s tastes. Active Learning aims at identifying rating data that better reflects users’ preferences. Active learning Strategies are used to selectively choose the items to present to the user in order to acquire her ratings and ultimately improve the recommendation accuracy. In this survey article, we review recent active learning techniques for collaborative filtering along two dimensions: (a) whether the system requested ratings are personalised or not, and, (b) whether active learning is guided by one criterion (heuristic) or multiple criteria.


title: Psychotherapy for Substance Use Disorders – the importance of affects

abstract: Substance use disorder (SUD) is a serious disorder with severe consequences for the individual, the family and for society. Comorbidity is common in the SUD population and the diversity of the diso ...


title: Constructing a multi-class classifier using one-against-one approach with different binary classifiers

abstract: For the one-against-one approach, all the binary classifiers that form a one-against-one classifier should be sufficiently competent. If some of the classifiers are not competent, the consequences might be invalid classification results. To address the problem, we propose diversified one-against-one (DOAO) method that seeks to find the best classification algorithm for each class pair when applying the one-against-one approach to multi-class classification problems. Applying the proposed method makes various classification algorithms to complement each other. Since the best classification algorithm for each class pair is different, the proposed method can obtain improved classification results. Experimental results show that the proposed method outperforms other one-against-one based methods. HighlightsWe propose diversified one-against-one (DOAO) method for multi-class classification problems.DOAO seeks to find the best classification algorithm for each class pair when applying the one-against-one approach.DOAO outperforms other one-against-one based methods.


title: Educational intelligence: Applying cloud-based big data analytics to the Indian education sector

abstract: Big Data technology is a generic technology, which can be applied to any real-world problem that involves a lot of data. Moreover, the use of cloud-based infrastructure to implement the big data technology makes it a cost-effective solution to the big data problem. One of the fundamental sectors that can benefit from this technology is education and research. The education system can use big data analytics to provide better education and administer the institutional operations. Research, which is an extension of education, can use analytics of big scholar data, for diverse applications, to facilitate research at the individual, team and organization level. With that said, the practical implementation and adoption of big data for education and research, collectively referred to as ‘Educational Intelligence’, faces several challenges, particularly in a developing country like India. This research paper explores how cloud-based big data analytics can be applied to Indian education and research and reviews the challenges that need to be addressed before the true benefits of this technology can be obtained.


Prediction of consumer credit risk


title: QuaSE: Sequence Editing under Quantifiable Guidance

abstract: We propose the task of Quantifiable Sequence Editing (QuaSE): editing an input sequence to generate an output sequence that satisfies a given numerical outcome value measuring a certain property of the sequence, with the requirement of keeping the main content of the input sequence. For example, an input sequence could be a word sequence, such as review sentence and advertisement text. For a review sentence, the outcome could be the review rating; for an advertisement, the outcome could be the click-through rate. The major challenge in performing QuaSE is how to perceive the outcome-related wordings, and only edit them to change the outcome. In this paper, the proposed framework contains two latent factors, namely, outcome factor and content factor, disentangled from the input sentence to allow convenient editing to change the outcome and keep the content. Our framework explores the pseudo-parallel sentences by modeling their content similarity and outcome differences to enable a better disentanglement of the latent factors, which allows generating an output to better satisfy the desired outcome and keep the content. The dual reconstruction structure further enhances the capability of generating expected output by exploiting the couplings of latent factors of pseudo-parallel sentences. For evaluation, we prepared a dataset of Yelp review sentences with the ratings as outcome. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.


title: A Biocompatible Near-Infrared 3D Tracking System*

abstract: A fundamental challenge in soft-tissue surgery is that target tissue moves and deforms, becomes occluded by blood or other tissue, and is difficult to differentiate from surrounding tissue. We developed small biocompatible near-infrared fluorescent (NIRF) markers with a novel fused plenoptic and NIR camera tracking system, enabling three-dimensional tracking of tools and target tissue while overcoming blood and tissue occlusion in the uncontrolled, rapidly changing surgical environment. In this work, we present the tracking system and marker design and compare tracking accuracies to standard optical tracking methods using robotic experiments. At speeds of 1 mm/s, we observe tracking accuracies of 1.61 mm, degrading only to 1.71 mm when the markers are covered in blood and tissue.


title: A stereo video segmentation algorithm combining disparity map and frame difference

abstract: In this paper, an algorithm to segment object from stereo video is presented. The algorithm combines the strong points of disparity map with frame difference. First, segment disparity maps to get the object segmentation template in different disparity lays. Then make frame difference extraction in the template regions to modify the object edge. After that the precise motion object has been gotten. The algorithm is low complexity and easy to realize. From the simulation results, we can conclude that the algorithm is efficient in segmentation when there is overlapping in different disparity motion objects or the background is moving.


title: Personalizing a Dialogue System with Transfer Learning.

abstract: It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain. By following this idea, we propose "PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework based on POMDP to learn a personalized dialogue system. The system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target user. This framework can avoid the negative transfer problem by considering differences between source and target users. The policy in the personalized POMDP can learn to choose different actions appropriately for different users. Experimental results on a real-world coffee-shopping data and simulation data show that our personalized dialogue system can choose different optimal actions for different users, and thus effectively improve the dialogue quality under the personalized setting.


title: Effects of sleep deprivation on performance: A meta-analysis.

abstract: To quantitatively describe the effects of sleep loss, we used meta-analysis, a technique relatively new to the sleep research field, to mathematically summarize data from 19 original research studies. Results of our analysis of 143 study coefficients and a total sample size of 1,932 suggest that overall sleep deprivation strongly impairs human functioning. Moreover, we found that mood is more affected by sleep deprivation than either cognitive or motor performance and that partial sleep deprivation has a more profound effect on functioning than either long-term or short-term sleep deprivation. In general, these results indicate that the effects of sleep deprivation may be underestimated in some narrative reviews, particularly those concerning the effects of partial sleep deprivation.


title: Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks

abstract: Wireless networks employing millimeter-wave (mmWave) and Massive Multiple-Input Multiple-Output (MIMO) technologies are a key approach to boost network capacity, coverage, and quality of service (QoS) for future communications. They deploy symmetric antennas on a large scale in order to enhance the system throughput and data rate. However, increasing the number of antennas and Radio Frequency (RF) chains results in high computational complexity and more energy requirements. Therefore, to solve these problems, this paper proposes a low-complexity hybrid beamforming scheme for mmWave Massive-MIMO 5G wireless networks. The proposed algorithm is on the basis of alternating the minimum mean square error (Alt-MMSE) hybrid beamforming technique in which the orthogonal properties of the digital matrix were designed, and then the MSE of the transmitted and received signal was reduced. The phase of the analog matrix was obtained from the updated digital matrix. Simulation results showed that the proposed hybrid beamforming algorithm had better performance than existing state-of-the-art algorithms, and similar performance with the optimal digital precoding algorithm.