title: A layered approach to simplified access control in virtualized systems

abstract: In this work, we show how the abstraction layer created by a hypervisor, or virtual machine monitor, can be leveraged to reduce the complexity of mandatory access control policies throughout the system. Policies governing access control decisions in today's systems are complex and monolithic. Achieving strong security guarantees often means restricting usability across the entire system, which is a primary reason why mandatory access controls are rarely deployed. Our architecture uses a hypervisor and multiple virtual machines to decompose policies into multiple layers. This simplifies the policies and their enforcement, while minimizing the overall impact of security on the system. We show that the overhead of decomposing system policies into distinct policies for each layer can be negligible. Our initial implementation confirms that such layering leads to simpler security policies and enforcement mechanisms as well as a more robust layered trusted computing base. We hope that this work serves to start a dialog regarding the use of mandatory access controls within a hypervisor for both increasing security and improving manageability.


title: Discriminative Training Techniques for Acoustic Language Identification

abstract: This paper presents comparison of Maximum Likelihood (ML) and discriminative Maximum Mutual Information (MMI) training for acoustic modeling in language identification (LID). Both approaches are compared on state-of- the-art shifted delta-cepstra features, the results are reported on data from NIST 2003 evaluations. Clear advantage of MMI over ML training is shown. Further improvements of acoustic LID are discussed: Heteroscedastic Linear Discriminant Analysis (HLDA) for feature de-correlation and dimensionality reduction and Ergodic Hidden Markov models (EHMM) for better modeling of dynamics in the acoustic space. The final error rate compares favorably to other results published on NIST 2003 data.


title: Broadband design of the non-symmetric ground /spl lambda//4 open slot antenna with small size

abstract: In this article, a novel design of non-symmetric ground /spl lambda//4 open slot antenna with microstrip-line is proposed. Choosing the proper dimension of the rectangular conducting plate which connected to the open end of the 50-/spl Omega/ microstrip-line with printed on the opposite side of the FR4 substrate and open slot, one can obtain wide impedance bandwidth and miniaturized antenna area. The proposed antenna printed on FR4 and fed by 50-/spl Omega/ microstrip-line is investigated. The frequency range of the proposed antenna is about 2.2 /spl sim/ 6.1 GHz (94%), which can be applied to mobile wireless communication products on WLAN, Bluetooth and UWB.


title: Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

abstract: Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self-supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.


A Triple Obstetric Challenge of Thoracopagus-Type Conjoined Twins, Eclampsia, and Obstructed Labor: A Case Report from Sub-Saharan Africa


title: COMPLEX HORMONAL REGULATION OF MAMMARY GLAND MILK PROTEIN GENE EXPRESSION

abstract: ABSTRACT The control of milk protein gene expression by both peptide and steroid hormones is a complex interactive process involving regulation at both the transcriptional and post-transcriptional levels. Both the rapidity and the large magnitude of induction observed in response to hormones in serum-free mammary explant cultures make this an ideal system for investigating the factors controlling specific mRNA accumulation in mammalian cells. The predominant milk proteins, the caseins, are a family of phosphoproteins, which appear to have evolved from a common ancestral gene. Considerable sequence divergence has been observed among members of this gene family in both intra- and interspecies comparisons, with the exception of two conserved regions, the signal peptide sequences and the casein phosphorylation sites. Members of the casein gene family are located on a single mouse chromosome, #5, while a fourth, abundant milk protein gene encoding a novel cysteinerich, acidic whey protein is on a different chromosome. Analysis of the structure of genomic casein clones has revealed that the casein genes are large and complex, containing multiple intervening sequences resulting in a length of approximately 17 Kb for the γ-, and 25 Kb for the s-casein gene. These genes are, therefore, approximately 18 and 22 times larger than their respective mRNAs. Cloned cDNAs have been used to measure the levels of these mRNAs during both normal mammary development and in cultures exposed to the hormones, prolactin and hydrocortisone. In organ culture both hormones were found to be essential for the maximal induction of all four mRNAs, with whey protein mRNA levels being particularly sensitive to the inductive effects of the steroid hormone. The relative proportions of the α- and s-compared to γ-casein mRNA changed from 300:35:1 in the uninduced to 4:7.7:1 in the induced state, primarily reflecting the very low basal levels of γ-casein mRNA. Recent sequence analysis of γ-casein mRNA has revealed the presence of an unusual poly(A) addition sequence, AUUAAA, and a significantly greater proportion of γ-casein mRNA was also observed in nuclear poly(A)-minus RNA isolated from lactating glands. Changes in milk protein mRNA concentrations may be explained by differences in both the half-lives of these mRNAs, as well as their rates of transcription in the presence and absence of hormones.


Combining Lexical, Syntactic, And Semantic Features With Maximum Entropy Models For Information Extraction


title: A statistical semantic language model for source code

abstract: Recent research has successfully applied the statistical n-gram language model to show that source code exhibits a good level of repetition. The n-gram model is shown to have good predictability in supporting code suggestion and completion. However, the state-of-the-art n-gram approach to capture source code regularities/patterns is based only on the lexical information in a local context of the code units. To improve predictability, we introduce SLAMC, a novel statistical semantic language model for source code. It incorporates semantic information into code tokens and models the regularities/patterns of such semantic annotations, called sememes, rather than their lexemes. It combines the local context in semantic n-grams with the global technical concerns/functionality into an n-gram topic model, together with pairwise associations of program elements. Based on SLAMC, we developed a new code suggestion method, which is empirically evaluated on several projects to have relatively 18-68% higher accuracy than the state-of-the-art approach.


title: Evaluation of salient point methods

abstract: Processing visual content in images and videos is a challenging task associated with the development of modern computer vision. Because salient point approaches can represent distinctive and affine invariant points in images, many approaches have been proposed over the past decade. Each method has particular advantages and limitations and may be appropriate in different contexts. In this paper we evaluate the performance of a wide set of salient point detectors and descriptors. We begin by comparing diverse salient point algorithms (SIFT, SURF, BRIEF, ORB, FREAK, BRISK, STAR, GFTT and FAST) with regard to repeatability, recall and precision and then move to accuracy and stability in real-time video tracking.


title: Retrieval-induced forgetting in recall: competitor interference revisited.

abstract: Participants studied category–exemplar pairs (FRUIT Cherry, FRUIT Grape) and then practiced some of the items (Cherry). In Experiment 1, practice that involved retrieving the item from memory suppressed recall of related items (Grape), a finding known as the retrieval-induced forgetting (RIF) effect. In Experiment 2, practice that involved studying the item without retrieval produced no RIF effect. Both retrieval and nonretrieval practice facilitated the subsequent recall of practiced items (Cherry). The dissociation between “strengthening” of practiced items and forgetting of related items is thought to be evidence that RIF is the result of inhibition during earlier retrieval attempts rather than interference from competing memories at retrieval. However, simulations of the SAM-REM model show that competitor interference can account for this dissociation. Experiments 3–6 supported the predictions of the model by demonstrating that nonretrieval practice can produce the RIF effect under conditions that emphasize context encoding or increase the number of competitors.


Digital Disruption


A New Glottal Neural Vocoder for Speech Synthesis


title: A method to convert mRNA into a gRNA library for CRISPR/Cas9 editing of any organism

abstract: The clustered regularly interspersed palindromic repeats (CRISPR)/Cas9 (CRISPR-associated protein 9) system is a powerful tool for genome editing that can be used to construct a guide RNA (gRNA) library for genetic screening. For gRNA design, one must know the sequence of the 20-mer flanking the protospacer adjacent motif (PAM), which seriously impedes experimentally making gRNA. I describe a method to construct a gRNA library via molecular biology techniques without relying on bioinformatics. Briefly, one synthesizes complementary DNA from the mRNA sequence using a semi-random primer containing a PAM complementary sequence and then cuts out the 20-mer adjacent to the PAM using type IIS and type III restriction enzymes to create a gRNA library. The described approach does not require prior knowledge about the target DNA sequences, making it applicable to any species.


title: Rotated QAM Constellations to Improve BICM Performance for DVB-T2

abstract: A technique intended to increase the diversity order of Bit-Interleaved Coded Modulations (BICM) over non Gaussian channels is presented. It introduces simple modifications to the mapper and to the corresponding demapper. They consist of a constellation rotation coupled with signal space component interleaving. Iterative processing at the receiver side can provide additional improvement to the BICM performance. This method has been shown to perform well over fading channels with or without erasures. It has been adopted for the 4-, 16-, 64- and 256-QAM constellations considered in the DVB-T2 standard. Resulting gains can vary from 0.2 dB to several dBs depending on the order of the constellation, the coding rate and the channel model.


title: Designing Digital Mindfulness: Presence-In and Presence-With versus Presence-Through

abstract: The digital health and wellbeing movement has led to development of digital mindfulness applications that aim to help people to become mindful. In this paper we suggest a broad scheme for classifying and ordering apps intended to support mindfulness. This scheme consists of four levels of what we here term digital mindfulness. One crucial aspect of the fourth level is that artifacts at this level allow for what we term as presence-with and presence-in as opposed to presence-through, which occurs at the first three levels. We articulate our four levels along with specific design qualities through concrete examples of existing mindfulness apps and through research through design (RtD) work conducted with design fiction examples. We then use a working design case prototype to further illustrate the possibilities of presence-with and presence-in. We hope our four levels of digital mindfulness framework will be found useful by other researchers in discussing and planning the design of their own mindfulness apps and digital artifacts.


title: Learning Tractable Probabilistic Models for Fault Localization

abstract: In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize across multiple programs. In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and probabilistically infer the location of the bug. While most previous statistical debugging methods generalize over many executions of a single program, TFLMs are trained on a corpus of previously seen buggy programs, and learn to identify recurring patterns of bugs. Widely-used fault localization techniques such as TARANTULA evaluate the suspiciousness of each line in isolation; in contrast, a TFLM defines a joint probability distribution over buggy indicator variables for each line. Joint distributions with rich dependency structure are often computationally intractable; TFLMs avoid this by exploiting recent developments in tractable probabilistic models (specifically, Relational SPNs). Further, TFLMs can incorporate additional sources of information, including coverage-based features such as TARANTULA. We evaluate the fault localization performance of TFLMs that include TARANTULA scores as features in the probabilistic model. Our study shows that the learned TFLMs isolate bugs more effectively than previous statistical methods or using TARANTULA directly.


title: A Secure Image Steganography Based on RSA Algorithm and Hash-LSB Technique

abstract: Steganography is a method of hiding secret messages in a cover object while communication takes place between sender and receiver. Security of confidential information has always been a major issue from the past times to the present time. It has always been the interested topic for researchers to develop secure techniques to send data without revealing it to anyone other than the receiver. Therefore from time to time researchers have developed many techniques to fulfill secure transfer of data and steganography is one of them. In this paper we have proposed a new technique of image steganography i.e. Hash-LSB with RSA algorithm for providing more security to data as well as our data hiding method. The proposed technique uses a hash function to generate a pattern for hiding data bits into LSB of RGB pixel values of the cover image. This technique makes sure that the message has been encrypted before hiding it into a cover image. If in any case the cipher text got revealed from the cover image, the intermediate person other than receiver can't access the message as it is in encrypted form.


title: Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas

abstract: A cellular base station serves a multiplicity of single-antenna terminals over the same time-frequency interval. Time-division duplex operation combined with reverse-link pilots enables the base station to estimate the reciprocal forward- and reverse-link channels. The conjugate-transpose of the channel estimates are used as a linear precoder and combiner respectively on the forward and reverse links. Propagation, unknown to both terminals and base station, comprises fast fading, log-normal shadow fading, and geometric attenuation. In the limit of an infinite number of antennas a complete multi-cellular analysis, which accounts for inter-cellular interference and the overhead and errors associated with channel-state information, yields a number of mathematically exact conclusions and points to a desirable direction towards which cellular wireless could evolve. In particular the effects of uncorrelated noise and fast fading vanish, throughput and the number of terminals are independent of the size of the cells, spectral efficiency is independent of bandwidth, and the required transmitted energy per bit vanishes. The only remaining impairment is inter-cellular interference caused by re-use of the pilot sequences in other cells (pilot contamination) which does not vanish with unlimited number of antennas.


title: Analyzing Graphs with Node Differential Privacy

abstract: We develop algorithms for the private analysis of network data that provide accurate analysis of realistic networks while satisfying stronger privacy guarantees than those of previous work. We present several techniques for designing node differentially private algorithms, that is, algorithms whose output distribution does not change significantly when a node and all its adjacent edges are added to a graph. We also develop methodology for analyzing the accuracy of such algorithms on realistic networks. ::: ::: The main idea behind our techniques is to 'project' (in one of several senses) the input graph onto the set of graphs with maximum degree below a certain threshold. We design projection operators, tailored to specific statistics that have low sensitivity and preserve information about the original statistic. These operators can be viewed as giving a fractional (low-degree) graph that is a solution to an optimization problem described as a maximum flow instance, linear program, or convex program. In addition, we derive a generic, efficient reduction that allows us to apply any differentially private algorithm for bounded-degree graphs to an arbitrary graph. This reduction is based on analyzing the smooth sensitivity of the 'naive' truncation that simply discards nodes of high degree.


title: Low power dual matchline ternary content addressable memory

abstract: Content addressable memories (CAMs) are very attractive for high-speed table lookups in modern network systems. This paper presents a low-power dual match line (ML) ternary CAM (TCAM) to address the power consumption issue of CAMs. The highly capacitive ML is divided into two segments to reduce the active capacitance and hence the power. We analyze possible cases of mismatches and demonstrate a significant reduction in power (up to 43%) for a small penalty in search speed (4%).


title: Controllability of complex networks

abstract: The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes. Control theory can be used to steer engineered and natural systems towards a desired state, but a framework to control complex self-organized systems is lacking. Can such networks be controlled? Albert-Laszlo Barabasi and colleagues tackle this question and arrive at precise mathematical answers that amount to 'yes, up to a point'. They develop analytical tools to study the controllability of an arbitrary complex directed network using both model and real systems, ranging from regulatory, neural and metabolic pathways in living organisms to food webs, cell-phone movements and social interactions. They identify the minimum set of driver nodes whose time-dependent control can guide the system's entire dynamics ( ::: http://go.nature.com/wd9Ek2 ::: ). Surprisingly, these are not usually located at the network hubs.


title: Exploring web scale language models for search query processing

abstract: It has been widely observed that search queries are composed in a very different style from that of the body or the title of a document. Many techniques explicitly accounting for this language style discrepancy have shown promising results for information retrieval, yet a large scale analysis on the extent of the language differences has been lacking. In this paper, we present an extensive study on this issue by examining the language model properties of search queries and the three text streams associated with each web document: the body, the title, and the anchor text. Our information theoretical analysis shows that queries seem to be composed in a way most similar to how authors summarize documents in anchor texts or titles, offering a quantitative explanation to the observations in past work. We apply these web scale n-gram language models to three search query processing (SQP) tasks: query spelling correction, query bracketing and long query segmentation. By controlling the size and the order of different language models, we find that the perplexity metric to be a good accuracy indicator for these query processing tasks. We show that using smoothed language models yields significant accuracy gains for query bracketing for instance, compared to using web counts as in the literature. We also demonstrate that applying web-scale language models can have marked accuracy advantage over smaller ones.


Formalization and Algebraic Verification of Legal Requirements


title: Chip-level and board-level CDM ESD tests on IC products

abstract: The electrostatic discharge (ESD) transient currents and failure analysis (FA) between chip-level and board-level charged-device-model (CDM) ESD tests are investigated in this work. The discharging current waveforms of three different printed circuit boards (PCBs) are characterized first. Then, the chip-level and board-level CDM ESD tests are performed to an ESD-protected dummy NMOS and a high-speed receiver front-end circuit, respectively. Scanning electron microscope (SEM) failure pictures show that the board-level CDM ESD test causes much severer failure than that caused by the chip-level CDM ESD test.


title: Some Economics of Private Digital Currency

abstract: Recent innovations have made it feasible to transfer private digital currency without the intervention of an institution. A digital currency must prevent users from spending their balances more than once, which is easier said than done with purely digital currencies. Current digital currencies such as Bitcoin use peer-to-peer networks and open-source software to stop double spending and create finality of transactions. This paper explains how the use of these technologies and limitation of the quantity produced can be equilibrium strategies in which a digital currency has a positive value. This paper also summarizes the rise of 24/7 trading on a computerized market in Bitcoin, a remarkable innovation in financial markets. I conclude that exchanges of foreign currency may be the obvious way in which use of digital currencies can become widespread.


title: The Driving Forces of Customer Loyalty: A Study of Internet Service Providers in Hong Kong

abstract: In this study we examine the driving forces of customer loyalty in the broadband market in Hong Kong. We developed and empirically tested a model to examine the antecedents of customer loyalty towards Internet service providers (ISPs) in Hong Kong. Structural equation modeling (SEM) was used to evaluate the proposed model. A total of 737 valid returns were obtained through a questionnaire survey. The results show that customer satisfaction, switching cost, and price perception are antecedents that lead directly to customer loyalty, with customer satisfaction exerting the greatest influence. Although we found that service quality significantly influences customer satisfaction, which in turn leads to customer loyalty, we did not find a direct relationship between service quality and customer loyalty. Our results also reveal that corporate image is not related to customer loyalty. Our empirical investigation suggests that investing huge resources in building corporate image can indeed be a risky strategy for ISPs.


Machine Learning Methods for Histopathological Image Analysis


title: Apportionment and uncertainty analysis of nitrate sources based on the dual isotope approach and a Bayesian isotope mixing model at the watershed scale

abstract: Abstract Identifying and eliminating pollutant sources of water bodies is critical for drinking water safety. In this research, river water, reservoir water and groundwater samples (n = 259) were collected from November 2015 to January 2017. Spatial Analysis was made of the isotopic compositions of potential nitrate sources (i.e., manure, sewage, chemical nitrogen fertilizer, soil organic nitrogen and rainfall) so as to obtain the site source isotopic signatures. Different sources pools and fractionation factors were loaded to a Bayesian isotope mixing model to ensure posterior estimates with less uncertainty. Results showed that the total nitrogen (TN) concentrations in Hexi Reservoir watershed were higher than the Environmental Quality Standards for Surface Water of China (GB 3838-2002), and NO3−-N was the dominant form of TN (accounting for 68.63% on average). There are significant spatio-temporal variations in the isotope data (δ15N-NO3− and δ18O-NO3−) and the dominant nitrate sources, which were related to the land use types. Loading the site source isotopic signatures to the Bayesian isotope mixing model effectively improved the accuracy and precision of nitrate source apportionment. Chemical nitrogen fertilizer (NF) was the foremost largest contributor of NO3−-N (38.82%), especially for Hexi North Stream (34.19%) and Yangmei Stream (44.39%), while atmospheric deposition (AD) contributed the least to NO3−-N (0.47%) of river water in the watershed; soil organic nitrogen (NS) contributed more to NO3−-N in the dry season than in the wet season; and manure and sewage (M&S) contributed approximately 30.22% in the whole study period, 53.60% in September 2016 and 41.33% in Hexi South Stream. This research suggests that combination of Spatial Analysis and the Bayesian isotope mixing model with the measured isotopic signatures of potential nitrate sources accurately apportion the nitrate source contributions.


The Catholic Priest in the United States: Sociological Investigations


title: Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.


title: A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks

abstract: To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA), and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy and crosstalk noise limits the retrieval accuracy and information capacity. A novel theory to optimize memory performance in such networks is presented and compared with simulation experiments. The theory describes linear readout of analog data, and readout with winner-take-all error correction of symbolic data as proposed in VSA models. We find that diverse VSA models from the literature have universal performance properties, which are superior to what previous analyses predicted. Further, we propose novel VSA models with the statistically optimal Wiener filter in the readout that exhibit much higher information capacity, in particular for storing analog data. The presented theory also applies to memory buffers, networks with gradual forgetting, which can operate on infinite data streams without memory overflow. Interestingly, we find that different forgetting mechanisms, such as attenuating recurrent weights or neural nonlinearities, produce very similar behavior if the forgetting time constants are aligned. Such models exhibit extensive capacity when their forgetting time constant is optimized for given noise conditions and network size. These results enable the design of new types of VSA models for the online processing of data streams.


Caterpillar: A Blockchain-Based Business Process Management System


title: Outer-loop vectorization: revisited for short SIMD architectures

abstract: Vectorization has been an important method of using data-level parallelism to accelerate scientific workloads on vector machines such as Cray for the past three decades. In the last decade it has also proven useful for accelerating multi-media and embedded applications on short SIMD architectures such as MMX, SSE and AltiVec. Most of the focus has been directed at innermost loops, effectively executing their iterations concurrently as much as possible. Outer loop vectorization refers to vectorizing a level of a loop nest other than the innermost, which can be beneficial if the outer loop exhibits greater data-level parallelism and locality than the innermost loop. Outer loop vectorization has traditionally been performed by interchanging an outer-loop with the innermost loop, followed by vectorizing it at the innermost position. A more direct unroll-and-jam approach can be used to vectorize an outer-loop without involving loop interchange, which can be especially suitable for short SIMD architectures. In this paper we revisit the method of outer loop vectorization, paying special attention to properties of modern short SIMD architectures. We show that even though current optimizing compilers for such targets do not apply outer-loop vectorization in general, it can provide significant performance improvements over innermost loop vectorization. Our implementation of direct outer-loop vectorization, available in GCC 4.3, achieves speedup factors of 3.13 and 2.77 on average across a set of benchmarks, compared to 1.53 and 1.39 achieved by innermost loop vectorization, when running on a Cell BE SPU and PowerPC970 processors respectively. Moreover, outer-loop vectorization provides new reuse opportunities that can be vital for such short SIMD architectures, including efficient handling of alignment. We present an optimization tapping such opportunities, capable of further boosting the performance obtained by outer-loop vectorization to achieve average speedup factors of 5.26 and 3.64.


title: Ergonomics of laparoscopic graspers and the importance of haptic feedback: the surgeons’ perspective

abstract: Haptic feedback is drastically reduced in laparoscopic surgery compared to open surgery. Introducing enhanced haptic feedback in laparoscopic instruments might well improve surgical safety and efficiency. In the design process of a laparoscopic grasper with enhanced haptic feedback, handle design should be addressed to strive for optimal usability and comfort. Additionally, the surgeons’ perspective on the potential benefits of haptic feedback should be assessed to ascertain the clinical interest of enhanced haptic feedback. A questionnaire was designed to determine surgeons’ use and preferences for laparoscopic instruments and expectations about enhanced haptic feedback. Surgeons were also asked whether they experience physical complaints related to laparoscopic instruments. The questionnaire was distributed to a group of laparoscopic surgeons based in Europe. From the 279 contacted subjects, 98 completed the questionnaire (response rate 35 %). Of all respondents, 77 % reported physical complaints directly attributable to the use of laparoscopic instruments. No evident similarity in the main preference for graspers was found, either with or without haptic feedback. According to respondents, the added value of haptic feedback could be of particular use in feeling differences in tissue consistencies, feeling the applied pressure, locating a tumor or enlarged lymph node, feeling arterial pulse, and limiting strain in the surgeon’s hand. This study stresses that the high prevalence of physical complaints directly related to laparoscopic instruments among laparoscopic surgeons is still relevant. Furthermore, the potential benefits of enhanced haptic feedback in laparoscopic surgery are recognized by laparoscopic specialists. Therefore, haptic feedback is considered an unmet need in laparoscopy.


title: Overhead line fault detection using GSM technology

abstract: In this paper, a novel technique for the fault detection, classification & protection of transmission lines is proposed. The proposed system uses different protective equipment's, voltage sense section, microcontroller section, LED display section & GSM (global system for mobile communication) module. The faults like all series (1LO, 2LO, 3LO etc.) & shunt faults (L-G, L-L, L-L-G, L-L-L, L-L-L-G) get detected & classified according to characteristics condition of current & voltage at the occurrence of fault in the three phase overhead lines. The sensed signals are given to microcontroller for detection & classification of faults. Also wireless mobile communication technique i.e. GSM is used simultaneously to send message to responsible person on mobile. Type of fault gets displayed on fault display section. Simultaneously fault gets interrupted using protective devices. (fuse, circuit breakers etc.)


Engineering sustainable blockchain applications


title: Four Degrees of Freedom SCARA Robot Modeling and Simulation

abstract: SCARA robot for multivariable, nonlinear, it Abstract is difficult to verify the correctness of the model question paper for four degrees of freedom SCARA robot kinematics modeling, and then in the MATLAB environment, using Robotics Toolbox forward kinematics of the robot inverse kinematics simulated by the simulation, the observed motion of each joint SCARA robot status, verify the proposed model is correct, it is possible to achieve the desired goals.


title: Segmentation of moving objects in image sequence: A review

abstract: Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In second-generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation, and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized.


title: ParaPIM: a parallel processing-in-memory accelerator for binary-weight deep neural networks

abstract: Recent algorithmic progression has brought competitive classification accuracy despite constraining neural networks to binary weights (+1/-1). These findings show remarkable optimization opportunities to eliminate the need for computationally-intensive multiplications, reducing memory access and storage. In this paper, we present ParaPIM architecture, which transforms current Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) sub-arrays to massively parallel computational units capable of running inferences for Binary-Weight Deep Neural Networks (BWNNs). ParaPIM's in-situ computing architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers, accelerate BWNNs inference, eliminate unnecessary off-chip accesses and provide ultra-high internal bandwidth. The device-to-architecture co-simulation results indicate ~4x higher energy efficiency and 7.3x speedup over recent processing-in-DRAM acceleration, or roughly 5x higher energy-efficiency and 20.5x speedup over recent ASIC approaches, while maintaining inference accuracy comparable to baseline designs.


title: Evaluation of Bit Error Probability for CPM MIMO Systems in Rayleigh Channel

abstract: Space time codes (STC) design employing continuous phase modulation (CPM) is well suitable for a multi-antenna system with low power and compact spectrum. Although STC design criteria for CPM MIMO system have been developed to date, the evaluation of performances with CPM MIMO system requires more general results like linear modulations such as PSK, QAM and so on. In this paper, Laurent decomposition is first applied and the conclusions of PAM signals in MIMO systems are extended to CPM MIMO systems. Thereby the average exact bound of bit error probability is derived. Further, space time block codes with CPM MIMO are introduced and its decoding is simplified for Monte-Carlo simulation. Finally, analytical and simulated results have confirmed the performances of CPM MIMO system.


title: Quadtree Convolutional Neural Networks

abstract: This paper presents a Quadtree Convolutional Neural Network (QCNN) for efficiently learning from image datasets representing sparse data such as handwriting, pen strokes, freehand sketches, etc. Instead of storing the sparse sketches in regular dense tensors, our method decomposes and represents the image as a linear quadtree that is only refined in the non-empty portions of the image. The actual image data corresponding to non-zero pixels is stored in the finest nodes of the quadtree. Convolution and pooling operations are restricted to the sparse pixels, leading to better efficiency in computation time as well as memory usage. Specifically, the computational and memory costs in QCNN grow linearly in the number of non-zero pixels, as opposed to traditional CNNs where the costs are quadratic in the number of pixels. This enables QCNN to learn from sparse images much faster and process high resolution images without the memory constraints faced by traditional CNNs. We study QCNN on four sparse image datasets for sketch classification and simplification tasks. The results show that QCNN can obtain comparable accuracy with large reduction in computational and memory costs.


title: Review: RFID-enabled Applications to Improve the Delivery of Healthcare Services: A Typology and Supporting Technologies

abstract: The adoption of novel technologies such as radio-frequency identification (RFID) along the healthcare value chain are transforming the sector by enabling automatic identification and tracking of products, people, and assets, resulting in real-time visibility and improved efficiency in the delivery of healthcare services. Although a lot of information on RFID applications within the healthcare sector is already available, there is a need to clarify the role of RFID-specific technologies and systems in supporting such innovative healthcare applications. This paper provides a background for healthcare practitioners and researchers to clarify the role of RFID technologies in the healthcare sector. It then proposes a typology of the main RFID-enabled application domains and identifies specific technological designs that support such innovative healthcare applications. The living laboratory approach is used to assess and validate various technological designs that can be selected when envisioning the implementation of such applications.


MFCC Based Speaker Recognition using Matlab


title: The Addenbrooke's Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening.

abstract: There is a clear need for brief, but sensitive and specific, cognitive screening instruments as evidenced by the popularity of the Addenbrooke's Cognitive Examination (ACE). ::: ::: Objectives ::: We aimed to validate an improved revision (the ACE-R) which incorporates five sub-domain scores (orientation/attention, memory, verbal fluency, language and visuo-spatial). ::: ::: Methods ::: Standard tests for evaluating dementia screening tests were applied. A total of 241 subjects participated in this study (Alzheimer's disease = 67, frontotemporal dementia = 55, dementia of Lewy Bodies = 20; mild cognitive impairment–MCI = 36; controls = 63). ::: ::: Results ::: Reliability of the ACE-R was very good (alpha coefficient = 0.8). Correlation with the Clinical Dementia Scale was significant (r = −0.321, p < 0.001). Two cut-offs were defined (88: sensitivity = 0.94, specificity = 0.89; 82: sensitivity = 0.84, specificity = 1.0). Likelihood ratios of dementia were generated for scores between 88 and 82: at a cut-off of 82 the likelihood of dementia is 100:1. A comparison of individual age and education matched groups of MCI, AD and controls placed the MCI group performance between controls and AD and revealed MCI patients to be impaired in areas other than memory (attention/orientation, verbal fluency and language). ::: ::: Conclusions ::: The ACE-R accomplishes standards of a valid dementia screening test, sensitive to early cognitive dysfunction. Copyright © 2006 John Wiley & Sons, Ltd.


title: The design of AC permanent magnet motors for electric vehicles: A design methodology

abstract: This paper presents a complete methodology for the design of AC permanent magnet motors for electric vehicle traction. Electromagnetic, thermal and mechanical performance aspects are considered and modern CAD tools are utilised throughout the methodology. A 36 slot 10 pole interior permanent magnet design example is used throughout the analysis.


title: A benchmark comparison of deterministic and probabilistic methods for defining manual review datasets in duplicate records reconciliation

abstract: Introduction Clinical databases require accurate entity resolution (ER). One approach is to use algorithms that assign questionable cases to manual review. Few studies have compared the performance of common algorithms for such a task. Furthermore, previous work has been limited by a lack of objective methods for setting algorithm parameters. We compared the performance of common ER algorithms: using algorithmic optimization, rather than manual parameter tuning, and on two-threshold classification (match/manual review/non-match) as well as single-threshold (match/non-match). ::: ::: Methods We manually reviewed 20 000 randomly selected, potential duplicate record-pairs to identify matches (10 000 training set, 10 000 test set). We evaluated the probabilistic expectation maximization, simple deterministic and fuzzy inference engine (FIE) algorithms. We used particle swarm to optimize algorithm parameters for a single and for two thresholds. We ran 10 iterations of optimization using the training set and report averaged performance against the test set. ::: ::: Results The overall estimated duplicate rate was 6%. FIE and simple deterministic algorithms allowed a lower manual review set compared to the probabilistic method (FIE 1.9%, simple deterministic 2.5%, probabilistic 3.6%; p<0.001). For a single threshold, the simple deterministic algorithm performed better than the probabilistic method (positive predictive value 0.956 vs 0.887, sensitivity 0.985 vs 0.887, p<0.001). ER with FIE classifies 98.1% of record-pairs correctly (1/10 000 error rate), assigning the remainder to manual review. ::: ::: Conclusions Optimized deterministic algorithms outperform the probabilistic method. There is a strong case for considering optimized deterministic methods for ER.


title: Automated seed sowing agribot using arduino

abstract: The Discovery of Agriculture is the first big step towards civilized life, advancement of agricultural tools is the basic trend of agricultural improvement. Now the qualitative approach of this project is to develop a system which minimizes the working cost and also reduces the time for digging operation and seed sowing operation by utilizing solar energy to run the agribot. In this machine, solar panel is used to capture solar energy and then it is converted into electrical energy which is used to charge battery, which then gives the necessary power to a shunt wound DC motor. Ultrasonic Sensor and Digital Compass Sensor are used with the help of Wi-Fi interface operated on Android Application to manoeuvre robot in the field. This brings down labour dependency. Seed sowing and digging robot will move on various ground contours and performs digging, sowing the seed and covers the ground by closing it. The paper spells out the complete installation of the agribot including hardware and software facet.


title: Human Intention Inference Using Expectation-Maximization Algorithm With Online Model Learning

abstract: An algorithm called adaptive-neural-intention estimator (ANIE) is presented to infer the intent of a human operator’s arm movements based on the observations from a 3-D camera sensor (Microsoft Kinect). Intentions are modeled as the goal locations of reaching motions in 3-D space. Human arm’s nonlinear motion dynamics are modeled using an unknown nonlinear function with intentions represented as parameters. The unknown model is learned by using a neural network. Based on the learned model, an approximate expectation-maximization algorithm is developed to infer human intentions. Furthermore, an identifier-based online model learning algorithm is developed to adapt to the variations in the arm motion dynamics, the motion trajectory, the goal locations, and the initial conditions of different human subjects. The results of experiments conducted on data obtained from different users performing a variety of reaching motions are presented. The ANIE algorithm is compared with an unsupervised Gaussian mixture model algorithm and an Euclidean distance-based approach by using Cornell’s CAD-120 data set and data collected in the Robotics and Controls Laboratoy at UConn. The ANIE algorithm is compared with the inverse LQR and ATCRF algorithms using a labeling task carried out on the CAD-120 data set.


title: Personal knowledge questions for fallback authentication: security questions in the era of Facebook

abstract: Security questions (or challenge questions) are commonly used to authenticate users who have lost their passwords. We examined the password retrieval mechanisms for a number of personal banking websites, and found that many of them rely in part on security questions with serious usability and security weaknesses. We discuss patterns in the security questions we observed. We argue that today's personal security questions owe their strength to the hardness of an information-retrieval problem. However, as personal information becomes ubiquitously available online, the hardness of this problem, and security provided by such questions, will likely diminish over time. We supplement our survey of bank security questions with a small user study that supplies some context for how such questions are used in practice.


title: Open-Domain Non-factoid Question Answering

abstract: We present an end-to-end system for open-domain non-factoid question answering. We leverage the information on the ever-growing World Wide Web, and the capabilities of modern search engines to find the relevant information. Our QA system is composed of three components: (i) query formulation module (QFM) (ii) candidate answer generation module (CAGM) and (iii) answer selection module (ASM). A thorough empirical evaluation using two datasets demonstrates that the proposed approach is highly competitive.


title: Fast Incremental Method for Nonconvex Optimization

abstract: We analyze a fast incremental aggregated gradient method for optimizing nonconvex problems of the form $\min_x \sum_i f_i(x)$. Specifically, we analyze the SAGA algorithm within an Incremental First-order Oracle framework, and show that it converges to a stationary point provably faster than both gradient descent and stochastic gradient descent. We also discuss a Polyak's special class of nonconvex problems for which SAGA converges at a linear rate to the global optimum. Finally, we analyze the practically valuable regularized and minibatch variants of SAGA. To our knowledge, this paper presents the first analysis of fast convergence for an incremental aggregated gradient method for nonconvex problems.


title: Thompson Sampling for Dynamic Pricing

abstract: In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.


title: Paradigm Shifts - E-Business and Business/Systems Integration

abstract: The last decade or so spawned a host of business and technology innovations. On the business side, we saw business process reengineering, the management philosophies of customer relationship management and supply chain management, virtual organizations, electronic commerce, and business-tobusiness trading exchanges. On the technology side, we saw client-server computing, enterprise resource planning systems, the widespread adoption of Internet protocols, intranets and enterprise information portals, software package support for customer relationship management, supply chain management and other activities related to electronic business, and applications service providers. This tutorial puts put these business and technology innovations into historical context and relates them to one another through the unifying concepts of business integration and systems integration. One theme of the tutorial is the incomplete linkage between business integration and systems integration. Another is the imperfect relationship between the management philosophies of customer relationship management, supply chain management and electronic


title: Suicide by electrocution with low-voltage current.

abstract: Three cases of suicide by electrocution with low-voltage current were observed in five years (1994-1998) by medical clinical forensic examiners of an Emergency Forensic Unit of the Paris suburb among 2,000 external death examinations. The cases involved one woman, aged 72 and two men, aged 38 and 41. In the last two cases, electric burns were retrieved under bared electric wires, placed on the arms or fingers in order to realize a hand-to-hand electric circuit involving the heart muscle. In the other case, the electric circuit between mouth and foot also involved the heart muscle. Household low-voltage current delivered (220 V in France) had a sufficient strength to induce local muscular paralysis and heart fibrillation. In the three cases, blood samples taken have retrieved very high levels of muscular enzymes (CPK, LDH) correlated to the mechanism of electric death. The rareness of suicide by electrocution and its forensic characteristics are detailed in order to help the clinical forensic examiners, prosecutors, and police officers concerned by such death examinations.


title: An evidential model of distributed reputation management

abstract: For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy correspondents is by collaborating with others to identify those whose past behavior has been untrustworthy. In other words, finding trustworthy correspondents reduces to the problem of distributed reputation management.Our approach adapts the mathematical theory of evidence to represent and propagate the ratings that agents give to their correspondents. When evaluating the trustworthiness of a correspondent, an agent combines its local evidence (based on direct prior interactions with the correspondent) with the testimonies of other agents regarding the same correspondent. We experimentally studied this approach to establish that some important properties of trust are captured by it.


Acceptor and Connector: Design Patterns for Initializing Communication Services


title: Optimal PID control of pH neutralization plant

abstract: pH control plays an important role in a wide range of industrial applications for waste management. The high non-linearity in pH neutralization processes and the uncertainty of the plant dynamics are key challenges that cannot be effectively addressed using PID controllers tuned with conventional methods. Some of the available and quite often tuning methods used for designing PID controllers are Cohen-Coon, Ziegler-Nichols (ZN), and Tyreus-Luyben. To address these problems, particle swarm optimization (PSO) is employed to tune the PID controller for a pH neutralization plant. The PSO algorithm is one of the most powerful methods for resolving non-smooth global optimization problems. In this paper, the mathematical model of pH process is developed taking into consideration of the overall plant's dynamics. The PSO based PID controller is then implemented and evaluated by comparing its performance with that of ZN-PID.


title: Adverse Childhood Experiences (ACEs), Stress and Mental Health in College Students

abstract: The goal of this short-term longitudinal study was to examine whether adverse childhood experiences (ACEs) could be used to identify college students at risk for mental health problems and whether current level of stress mediates the relationship between ACEs and mental health. Data on ACEs and mental health (depression, anxiety and suicidality) were collected at the beginning of the semester, and data on current stressors and mental health were collected toward the end of the semester (n = 239). Findings indicated that ACEs predicted worsening of mental health over the course of a semester and suggested current number of stressors as a mediator of the relationship between ACEs and mental health. Results suggest that screening for ACEs might be useful to identify students at high risk for deterioration in mental health. Results further suggest that stress-related interventions would be beneficial for students with high levels of ACEs and point to the need for more research and strategies to increase help-seeking in college students.


title: Methodologies for system-level remaining useful life prediction

abstract: While most prognostics approaches focus on accurate computation of the degradation rate and the remaining useful life (RUL) of individual components, it is the rate at which the performance of subsystems and systems degrade that is of greater interest to the operators and maintenance personnel of these systems. We develop a comprehensive methodology for system-level prognostics under different forms of uncertainty in this paper. Our approach combines an estimation scheme with a prediction scheme to compute the RUL as a stochastic distribution over the life of the system. We compare two prediction methods: (1) stochastic simulation and (2) the inverse first order reliability method (inverse-FORM). We compare the computational complexity and the accuracy of the two approaches using a case study of a system with several degrading components.


title: FVC2002: Second Fingerprint Verification Competition

abstract: Two years after the first edition, a new Fingerprint Verification Competition (FVC2002) was organized by the authors, with the aim of determining the state-of-the-art in this challenging pattern recognition application. The experience and the feedback received from FVC2000 allowed the authors to improve the organization of FVC2002 and to capture the attention of a significantly higher number of academic and commercial organizations (33 algorithms were submitted). This paper discusses the FVC2002 database, the test protocol and the main differences between FVC2000 and FVC2002. The algorithm performance evaluation will be presented at the 16/sup th/ ICPR.


title: Yoga for control of epilepsy

abstract: Yoga is an age-old traditional Indian psycho‐philosophical‐cultural method of leading one’s life, that alleviates stress, induces relaxation and provides multiple health benefits to the person following its system. It is a method of controlling the mind through the union of an individual’s dormant energy with the universal energy. Commonly practiced yoga methods are ‘Pranayama’ (controlled deep breathing), ‘Asanas’ (physical postures) and ‘Dhyana’ (meditation) admixed in varying proportions with differing philosophic ideas. A review of yoga in relation to epilepsy encompasses not only seizure control but also many factors dealing with overall quality-of-life issues (QOL). This paper reviews articles related to yoga and epilepsy, seizures, EEG, autonomic changes, neuro-psychology, limbic system, arousal, sleep, brain plasticity, motor performance, brain imaging studies, and rehabilitation. There is a dearth of randomized, blinded, controlled studies related to yoga and seizure control. A multi-centre, cross-cultural, preferably blinded (difficult for yoga), well-randomized controlled trial, especially using a single yogic technique in a homogeneous population such as Juvenile myoclonic epilepsy is justified to find out how yoga affects seizure control and QOL of the person with epilepsy. c 2001 BEA Trading Ltd


title: Characterisation of hemp (Cannabis sativa L.) roots under different growing conditions

abstract: Hemp (Cannabis sativa L.) is mainly grown for its fibre and is considered a desirable crop for sustainable production systems. In a field trial carried out over two years in Northern Italy the root system of a hemp crop, cultivated at contrasting plant densities, was sampled and analysed with an image analysis software. Root length density (RLD) was highest in the first 10 cm of soil, almost 5 cm cm−3; it decreased progressively until the depth of 130 cm, a part from a peak at 90–100 cm in response to a perched water table. Roots were found to 130 cm of depth in one year and to 200 cm in the other. Root diameter was finer (190 μm) in the upper soil layer, it increased with depth until 100 cm, and remained constant at 300 μm thereafter. Following the same trend of RLD, root biomass was highest in the first soil layer; 50% of the root biomass was found in the first 20 cm or 50 cm when taproot biomass was considered or not. Total root biomass was 3.21 t ha−1 and 2.41 t ha−1 in the two years of trial, but the ratio between aboveground and below ground biomass was constant at 5.46. None of the root parameters were significantly affected by plant population, which seems to confirm the plastic behaviour that hemp shows for aboveground development. The high root biomass production measured in this study, especially in deeper soil layers, provides additional evidence of the positive role that hemp can play in sustainable cropping systems.


title: Sequential Dialogue Context Modeling for Spoken Language Understanding

abstract: Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.


title: Human-in-the-loop Data Integration

abstract: Data integration aims to integrate data in different sources and provide users with a unified view. However, data integration cannot be completely addressed by purely automated methods. We propose a hybrid human-machine data integration framework that harnesses human ability to address this problem, and apply it initially to the problem of entity matching. The framework first uses rule-based algorithms to identify possible matching pairs and then utilizes the crowd to refine these candidate pairs in order to compute actual matching pairs. In the first step, we propose similarity-based rules and knowledge-based rules to obtain some candidate matching pairs, and develop effective algorithms to learn these rules based on some given positive and negative examples. We build a distributed in-memory system DIMA to efficiently apply these rules. In the second step, we propose a selection-inference-refine framework that uses the crowd to verify the candidate pairs. We first select some "beneficial" tasks to ask the crowd and then use transitivity and partial order to infer the answers of unasked tasks based on the crowdsourcing results of the asked tasks. Next we refine the inferred answers with high uncertainty due to the disagreement from the crowd. We develop a crowd-powered database system CDB and deploy it on real crowdsourcing platforms. CDB allows users to utilize a SQL-like language for processing crowd-based queries. Lastly, we provide emerging challenges in human-in-the-loop data integration.


Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement


title: Image Completion Approaches Using the Statistics of Similar Patches

abstract: Image completion involves filling missing parts in images. In this paper we address this problem through novel statistics of similar patches. We observe that if we match similar patches in the image and obtain their offsets (relative positions), the statistics of these offsets are sparsely distributed. We further observe that a few dominant offsets provide reliable information for completing the image. Such statistics can be incorporated into both matching-based and graph-based methods for image completion. Experiments show that our method yields better results in various challenging cases, and is faster than existing state-of-the-art methods.


title: Clinical performance of glass-ionomers.

abstract: Glass-ionomers were introduced to the profession 20 years ago and have been shown to be a very useful adjunct to restorative dentistry. Their major advantages include the ion exchange adhesion to both enamel and dentine and a continuing fluoride release throughout the life of the restoration. The chemistry of the setting reaction is essentially an acid/base reaction but recent research has introduced variations in an effort to improve clinical handling. Small additions of resin increase the physical properties to a degree and allow for a light-initiated setting mechanism. However, it is essential that the acid/base reaction remains dominant if the full advantages of the glass-ionomer are to be retained. The material has multiple uses in dentistry and it shows a high degree of biocompatibility to the pulp and surrounding soft tissues. The main limitation at this time is a relatively low fracture resistance but it is possible to protect it through lamination with stronger materials if the need arises. This paper examines the essential parameters for successful clinical placement and comments on its longevity.


title: Introduction to Judea Pearl's Do-Calculus

abstract: This is a purely pedagogical paper with no new results. The goal of the paper is to give a fairly self-contained introduction to Judea Pearl's do-calculus, including proofs of his 3 rules.


title: Provisioning servers in the application tier for e-commerce systems

abstract: Server providers that support e-commerce applications as a service for multiple e-commerce Web sites traditionally use a tiered server architecture. This architecture includes an application tier to process requests for dynamically generated content. How this tier is provisioned can significantly impact a provider's profit margin. In this article we study methods to provision servers in the application serving tier that increase a server provider's profits. First, we examine actual traces of request arrivals to the application tier of an e-commerce site, and show that the arrival process is effectively Poisson. Next, we construct an optimization problem in the context of a set of application servers modeled as M/G/1/PS queueing systems, and derive three simple methods that approximate the allocation that maximizes profits. Simulation results demonstrate that our approximation methods achieve profits that are close to optimal, and are significantly higher than those achieved via simple heuristics.


title: Web user behavioral profiling for user identification

abstract: In this paper, we propose a simple, yet powerful approach to profile users' web browsing behavior for the purpose of user identification. The importance of being able to identify users can be significant given a wide variety of applications in electronic commerce, such as product recommendation, personalized advertising, etc. We create user profiles capturing the strength of users' behavioral patterns, which can be used to identify users. Our experiments indicate that these profiles can be more accurate at identifying users than decision trees when sufficient web activities are observed, and can achieve higher efficiency than Support Vector Machines. The comparisons demonstrate that profile-based methods for user identification provide a viable and simple alternative to this problem.


2002): An Overview of Model Based Reject Inference for Credit Scoring. Working paper. Institute for Information and Computing Sciences


title: Atari games and Intel processors

abstract: The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions.


title: Financial Sentiment Lexicon Analysis

abstract: The modern stock market is a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. With the availability of the Internet and its financial social networks, such as StockTwits and SeekingAlpha, investors around the world have new opportunities to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with reasonable accuracy, but how accurate is a large group of such experts in aggregate? One way to answer this question is by examining the sentiment of a massive group of these authors towards various stocks. By extracting the sentiment of the whole group, a collective prediction can be observed. Although sentiment extraction is a major technical challenge, the lexicon-based approach is an effective method of determining how positive or negative the content of a text document is. In this paper, we investigate if we can improve the performance of sentiment extraction from financial social media data by using lexicon-based approaches.


Automatic construction of decision trees from data: A multidisciplinary survey. Data Mining and Knowledge Discovery


title: Brief Report: An Evaluation of the AQ-10 as a Brief Screening Instrument for ASD in Adults

abstract: There is a need for brief screening instruments for autistic spectrum disorders (ASD) that can be used by frontline healthcare professionals to aid in the decision as to whether an individual should be referred for a full diagnostic assessment. In this study we evaluated the ability of a short form of the autism spectrum quotient (AQ) questionnaire, the 10 item AQ-10, to correctly classify individuals as having or not having ASD. In a sample of 149 individuals with ASD and 134 controls without an ASD diagnosis, we found that the full AQ (AQ-50) abridged AQ (AQ-S) and AQ-10 all performed well as a screen for ASD. ROC analysis indicated that sensitivity, specificity and area under the curve were very similar at suggested cut-off’s for ASD across measures, with little difference in performance between the AQ-10 and full AQ-50. Results indicate the potential usefulness of the AQ-10 as a brief screen for ASD.


Stable Signal Recovery from Incomplete and Inaccurate Measurements


title: New therapies for castration-resistant prostate cancer: efficacy and safety.

abstract: CONTEXT ::: Prostate cancer (PCa) is the most common noncutaneous malignancy and the second leading cause of cancer mortality amongst men in the Western world. Up to 40% of men diagnosed with PCa will eventually develop metastatic disease, and although most respond to initial medical or surgical castration, progression to castration resistance is universal. The average survival for patients with castration-resistant prostate cancer (CRPC) is 2-3 yr. ::: ::: ::: OBJECTIVE ::: To discuss the biologic rationale and evidence supporting current management of patients with CRPC and to review promising novel agents. ::: ::: ::: EVIDENCE ACQUISITION ::: Electronic databases (PubMed, ClinicalTrials.gov), relevant journals, and conference proceedings were searched manually for preclinical studies, clinical trials, and biomarker analyses focused on the treatment of CRPC. Keywords included castrate resistant prostate cancer and: targeted therapy, novel therapy, immunotherapy, androgen therapy, bone therapy, mechanisms, biomarkers, and trial endpoints; no time range was specified. Information pertaining to current studies was discussed with key opinion leaders. ::: ::: ::: EVIDENCE SYNTHESIS ::: We focus on the efficacy and safety of approved agents, promising therapies that have proceeded to phase 3 evaluation, and those that have enhanced our understanding of the biology of CRPC. Biomarkers are considered in the context of novel targeted agents and immunotherapy. ::: ::: ::: CONCLUSIONS ::: CRPC has many targets. Four new agents with different mechanisms of action have recently been shown to have positive results in large phase 3 randomized trials, and have already been approved in the United States for CRPC: cabazitaxel, sipuleucel-T, denosumab, and abiraterone acetate. With our improved understanding of tumor biology and the incorporation of new prognostic and molecular biomarkers into clinical trials, we are making progress in the management of patients with CRPC.


title: Fast, Scalable and Secure Onloading of Edge Functions Using AirBox

abstract: This paper argues for the utility of back-end driven onloading to the edge as a way to address bandwidth use and latency challenges for future device-cloud interactions. Supporting such edge functions (EFs) requires solutions that can provide (i) fast and scalable EF provisioning and (ii) strong guarantees for the integrity of the EF execution and confidentiality of the state stored at the edge. In response to these goals, we (i) present a detailed design space exploration of the current technologies that can be leveraged in the design of edge function platforms (EFPs), (ii) develop a solution to address security concerns of EFs that leverages emerging hardware support for OS agnostic trusted execution environments such as Intel SGX enclaves, and (iii) propose and evaluate AirBox, a platform for fast, scalable and secure onloading of edge functions.


title: Stray current control in DC mass transit systems

abstract: Stray current control is essential in direct current (DC) mass transit systems where the rail insulation is not of sufficient quality to prevent a corrosion risk to the rails, supporting and third-party infrastructure. This paper details the principles behind the need for stray current control and examines the relationship between the stray current collection system design and its efficiency. The use of floating return rails is shown to provide a reduction in stray current level in comparison to a grounded system, significantly reducing the corrosion level of the traction system running rails. An increase in conductivity of the stray current collection system or a reduction in the soil resistivity surrounding the traction system is shown to decrease the corrosion risk to the supporting and third party infrastructure.


title: Regulatory Structure for Financial Stability and Development

abstract: To understand the appropriate regulatory response to the crisis, we start from the basic market failures that justify regulation in financial markets. Neglecting these first principles contributed to the market and regulatory failures. Regulation that induces better outcomes through creating correct incentives for market participants is the key to reform. A combination of micro and macro prudential regulation can moderate procyclicality, information failure and market power. Better national and global coordination of regulators is also required. Global prudential standards can push financial firms to choose safe over risky strategies, by removing the moral hazard from bailouts, and assuring that a competitor is not adopting risky strategies either. Universal application of basic standards prevents regulatory arbitrage. A pure principles-based regulatory approach maybe too flexible, but principle-based rules retain sufficient operational flexibility and universality. This analysis is applied to regulation in emerging market economies (EMEs), where development of financial markets is a major regulatory goal along with stability.


title: An 80 mV Startup Dual-Mode Boost Converter by Charge-Pumped Pulse Generator and Threshold Voltage Tuned Oscillator With Hot Carrier Injection

abstract: This paper presents an 80 mV startup-voltage dual-mode boost converter for energy harvesting applications. The charge-pumped pulse generator enables a startup operation of the boost converter from the input voltage of 80 mV. The threshold voltage tuned oscillator for the clock generator of the boost converter compensates for the die-to-die process variation by a hot carrier injection (HCI), thereby reducing the minimum operation voltage (VDDMIN) of the clock generator by 45% with a trimming time of 10 minutes. The proposed step-up converter achieves the lowest startup voltage without using a mechanical switch or a large transformer.


title: Smart vehicle monitoring system for air pollution detection using Wsn

abstract: Wireless Sensor Network (WSN) plays an important role in the application of environmental monitoring. Mostly air pollution being major issues as cause many hazardous effects on ecological system of human being. Therefore the need of monitoring air pollution around the city and the public transport buses and cars are very important problem today. Basically, environmental monitoring methods have difficulties in wired sensor network but by using WSN it is possible to achieve the challenging issues by implementing internet/intranet. This proposed research concentrates on measuring the gas level of air contamination around the cities and reduces the man power and also increases the overall flexibility of sender and receiver. The main objective of the proposed system for the moving vehicles is monitor the NO2, Humidity, Temperature, CO levels of air contamination by using NO2 sensor, Humidity sensor, Temperature sensor, CO sensor. In our proposed work MANET(Mobile Ad Hoc Network) routing algorithm is used which has nearly 28 mobile nodes(Vehicles) provide a coverage area of 300meters around the city. The sensor data of the vehicles will be sent to the smart phones of the appropriate drivers to monitor effectively. The result of the proposed method includes the following parameters such as data type, speed of transmission, coverage of system, coverage area size, and No.of vehicles to closely monitor the proposed system.


title: Feature extraction for robust physical activity recognition

abstract: This paper presents the development of a Human Activity Recognition (HAR) system that uses a network of nine inertial measurement units situated in different body parts. Every unit provides 3D (3-dimension) acceleration, 3D angular velocity, 3D magnetic field orientation, and 4D quaternions. This system identifies 33 different physical activities (walking, running, cycling, lateral elevation of arms, etc.). The system is composed of two main modules: a feature extractor for obtaining the most relevant characteristics from the inertial signals every second, and a machine learning algorithm for classifying between the different activities. This paper focuses on the feature extractor module, evaluating several types of features and proposing different normalization approaches. This paper also analyses the performance of every sensor included in the inertial measurement units. The main experiments have been done using a public available dataset named REALDISP Activity Recognition dataset. This dataset includes recordings from 17 subjects performing 33 different activities in three different scenarios. Final results demonstrate that the proposed HAR system significantly improves the classification accuracy compared to previous works on this dataset. For the best configuration, the system accuracy is 99.1%. This system has been also evaluated with the OPPORTUNITY dataset obtaining competitive results.


title: Giving computers a nose for news: exploring the limits of story detection and verification

abstract: The use of social media as a source of news is entering a new phase as computer algorithms are developed and deployed to detect, rank, and verify news. The efficacy and ethics of such technology is the subject of this article, which examines the SocialSensor application, a tool developed by a multidisciplinary European Union research project. The results suggest that computer software can be used successfully to identify trending news stories, allow journalists to search within a social media corpus, and help verify social media contributors and content. However, such software also raises questions about accountability as social media is algorithmically filtered for use by journalists and others. Our analysis of the inputs SocialSensor relies on shows biases towards those who are vocal and have an audience, many of whom are men in the media. We also reveal some of the technology’s temporal and topic preferences. The conclusion discusses whether such biases are necessary for systems like SocialSensor to be...


title: Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis

abstract: We investigate the design of an entire mobile imaging system for early detection of melanoma. Different from previous work, we focus on smartphone-captured visible light images. Our design addresses two major challenges. First, images acquired using a smartphone under loosely-controlled environmental conditions may be subject to various distortions, and this makes melanoma detection more difficult. Second, processing performed on a smartphone is subject to stringent computation and memory constraints. In our work, we propose a detection system that is optimized to run entirely on the resource-constrained smartphone. Our system intends to localize the skin lesion by combining a lightweight method for skin detection with a hierarchical segmentation approach using two fast segmentation methods. Moreover, we study an extensive set of image features and propose new numerical features to characterize a skin lesion. Furthermore, we propose an improved feature selection algorithm to determine a small set of discriminative features used by the final lightweight system. In addition, we study the human-computer interface (HCI) design to understand the usability and acceptance issues of the proposed system.


title: A Reduced Reference Image Quality assessment for Multiply Distorted Images

abstract: In this paper, we propose a new Reduced Reference Image Quality Metric for multiply degraded images based on a features extraction step and its combination. The selected features are extracted from the original image and its degraded version. Some of them aim to quantify the level of the considered degradation types, while the others quantify its sharpness. These features are then combined to obtain a single value, which corresponds to the predicted subjective score. Our method has been evaluated and compared in terms of correlation with subjective judgments to some recent methods by using the LIVE Multiply Distorted Image Quality Database.


Contrastive Linguistics : Theories and Methods


title: Prominent Features of Rumor Propagation in Online Social Media

abstract: The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than the offline counterpart. In this paper, we identify characteristics of rumors by examining the following three aspects of diffusion: temporal, structural, and linguistic. For the temporal characteristics, we propose a new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have fluctuations over time. We also identify key structural and linguistic differences in the spread of rumors and non-rumors. Our selected features classify rumors with high precision and recall in the range of 87% to 92%, that is higher than other states of the arts on rumor classification.


title: Object-Proposal Evaluation Protocol is ‘Gameable’

abstract: Object proposals have quickly become the de-facto pre-processing step in a number of vision pipelines (for object detection, object discovery, and other tasks). Their performance is usually evaluated on partially annotated datasets. In this paper, we argue that the choice of using a partially annotated dataset for evaluation of object proposals is problematic -- as we demonstrate via a thought experiment, the evaluation protocol is 'gameable', in the sense that progress under this protocol does not necessarily correspond to a "better" category independent object proposal algorithm. ::: To alleviate this problem, we: (1) Introduce a nearly-fully annotated version of PASCAL VOC dataset, which serves as a test-bed to check if object proposal techniques are overfitting to a particular list of categories. (2) Perform an exhaustive evaluation of object proposal methods on our introduced nearly-fully annotated PASCAL dataset and perform cross-dataset generalization experiments; and (3) Introduce a diagnostic experiment to detect the bias capacity in an object proposal algorithm. This tool circumvents the need to collect a densely annotated dataset, which can be expensive and cumbersome to collect. Finally, we plan to release an easy-to-use toolbox which combines various publicly available implementations of object proposal algorithms which standardizes the proposal generation and evaluation so that new methods can be added and evaluated on different datasets. We hope that the results presented in the paper will motivate the community to test the category independence of various object proposal methods by carefully choosing the evaluation protocol.


OnUniformSemiglobalExponential Stability ( USGES ) of Proportional Line-of-SightGuidanceLaws


title: Measuring User Influence on Twitter Using Modified K-Shell Decomposition

abstract: Social influence can be described as power - the ability of a person to influence the thoughts or actions of others. Identifying influential users on online social networks such as Twitter has been actively studied recently. In this paper, we investigate a modified k-shell decomposition algorithm for computing user influence on Twitter. The input to this algorithm is the connection graph between users as defined by the follower relationship. User influence is measured by the k-shell level, which is the output of the k-shell decomposition algorithm. Our first insight is to modify this k-shell decomposition to assign logarithmic k-shell values to users, producing a measure of users that is surprisingly well distributed in a bell curve. Our second insight is to identify and remove peering relationships from the network to further differentiate users. In this paper, we include findings from our study.


title: Hierarchical geographical modeling of user locations from social media posts

abstract: With the availability of cheap location sensors, geotagging of messages in online social networks is proliferating. For instance, Twitter, Facebook, Foursquare, and Google+ provide these services both explicitly by letting users choose their location or implicitly via a sensor. This paper presents an integrated generative model of location and message content. That is, we provide a model for combining distributions over locations, topics, and over user characteristics, both in terms of location and in terms of their content preferences. Unlike previous work which modeled data in a flat pre-defined representation, our model automatically infers both the hierarchical structure over content and over the size and position of geographical locations. This affords significantly higher accuracy --- location uncertainty is reduced by 40% relative to the best previous results [21] achieved on location estimation from Tweets. We achieve this goal by proposing a new statistical model, the nested Chinese Restaurant Franchise (nCRF), a hierarchical model of tree distributions. Much statistical structure is shared between users. That said, each user has his own distribution over interests and places. The use of the nCRF allows us to capture the following effects: (1) We provide a topic model for Tweets; (2) We obtain location specific topics; (3) We infer a latent distribution of locations; (4) We provide a joint hierarchical model of topics and locations; (5) We infer personalized preferences over topics and locations within the above model. In doing so, we are both able to obtain accurate estimates of the location of a user based on his tweets and to obtain a detailed estimate of a geographical language model.


title: A Compact 27 GHz Antenna-in-Package (AiP) with RF Transmitter and Passive Phased Antenna Array

abstract: In this paper, based on rigid-flex printed circuit board (PCB), a compact solution of Antenna-in-Package (AiP) including RF transmitter and microstrip passive phased antenna array is demonstrated. Apart from the landing of two connectors, the size shrinks down to 35 mm × 32 mm. RF transmitter works at the band of 26.2 GHz to 27.2 GHz, with conversion gain of 13 dB and rejection ratio up to 17.5 dBc. The 1 × 4 microstrip passive phased antenna array integrating 4 × 4 Butler Matrix achieves four scanning angles which are -10°, 30°, -30° and 10° with 20° half power beam width (HPBW), and simulated absolute gain is 13.51 dBi, 12.31 dBi, 12.23 dBi and 13.26 dBi respectively. Minor deviation on scanning angles from simulation data is observed in the testing due to measurement and manufacturing tolerance. However, among operating bandwidth, HPBW, main-to-side lobe ratio and the difference between two scanning angles when port 1, 4 and port 2, 3 are excited respectively are in good agreement with simulation results


Performance evaluation of massively distributed microservices based applications


ALBAHA UNIVERSITY JOURNAL OF BASIC AND APPliED SCIENCES Automatic Classification of Welding Defects Using Neural Network and Image Processing Techniques


Efficient SIMD Vectorization for Hashing in OpenCL


title: Alternative futures for the DSM revision process: iteration v. paradigm shift

abstract: Two major approaches can be used for the up-coming revisions of DSM–V and ICD–10: an ‘iterative model’ in which incremental changes are made or a ‘paradigm shift model’ in which the existing approach is jettisoned in favour of a new nosological model. We explore each of these two approaches and conclude that although they both have strengths and limitations, our field is not currently ready for a paradigm shift.


title: Intentions to use social media in organizing and taking vacation trips

abstract: This work proposes a theoretical model to explain the factors determining the intentions to use social media when organizing and taking vacation trips. Understanding the antecedents of the tourists' use of these technologies is considered to be crucial for organization managers and destination policy makers. This use of social media technologies determines which elements of the trip might be used by the tourist thus having a great impact on the market. The model and its hypotheses have been tested by means of an approach based on structural equations with the PLS technique. The study was conducted on a sample of 404 individuals who normally use the Internet and had traveled on vacation in the previous 12months. The conclusions of the study reveal that the intentions to use social media are directly influenced by the perceived benefits of that use (functional, psychological and hedonic and social); however, the costs do not significantly affect the predisposition to use such technologies. It is also shown that there is a series of incentives such as altruism, availability, individual predisposition or trust in the contributions of others which facilitate and promote the use of this type of technology when organizing and taking tourist trips.


title: Stereotype Threat and Female Communication Styles

abstract: A large body of research has documented the performance-debilitating effects of stereotype threat for individuals, but there is a paucity of research exploring interpersonal consequences of stereotype threat. Two experiments tested the hypothesis that stereotype threat would change the style in which women communicate. Results indicate that women who experience stereotype threat regarding leadership abilities react against the stereotype by adopting a more masculine communication style. Study 2 provides evidence that self-affirmation eliminates this effect of stereotype threat on women’s communication styles. A third study demonstrates an ironic consequence of this effect of stereotype threat on women’s communication—when women under stereotype threat adopt a more masculine communication style, they are rated as less warm and likeable, and evaluators indicate less willingness to comply with their requests. Theoretical and practical implications of these findings are discussed.


title: Curved and straight crop row detection by accumulation of green pixels from images in maize fields

abstract: This study proposes a new method for detecting curved and straight crop rows in images captured in maize fields during the initial growth stages of crop and weed plants. The images were obtained under perspective projection with a camera installed onboard and conveniently arranged at the front of a tractor. The final goal was the identification of the crop rows which are crucial for precise autonomous guidance and site-specific treatments, including weed removal based on the identification of plants outside the crop rows. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments and by gaps in the crop rows (due to lack of germination or defects during planting). Also, different plants heights and volumes occur due to different growth stages affecting the crop row detection process. The proposed method was designed with the required robustness to cope with the above undesirable situations and it consists of three sequentially linked phases: (i) image segmentation, (ii) identification of starting points and (iii) crop row detection. The main contribution is the ability of the method to detect curved crop rows as well as straights rows even with irregular inter-row spaces. The method performance has been tested in terms of accuracy and time processing.


title: Ubiquitous Learning: A Systematic Review

abstract: Abstract Ubiquitous learning, labeled as u–learning, takes advantage of digital content, physical surroundings, mobile devices, pervasive components, and wireless communication to deliver teaching–learning experiences to users at anytime, anywhere, and anyway. U–learning represents an emergent paradigm that spreads education in diverse settings, where users are situated in authentic learning contexts to face immersive experiences in order to accomplish meaningful learning. With the aim at disseminating such a revolutionary arena, this systematic review analyzes its nature, application, and evolution throughout a longitudinal study, where 176 approaches built since 2010 up to the third quarter of 2017 date are gathered, classified, and characterized to disclose labor traits, outcome patterns, and field tendencies. These five results are grounded respectively in a representative collection, a proposed taxonomy, a suggested pattern, statistical interpretations, mining findings, and critical analysis. The conclusions reveal: u–learning is able to transform traditional education provided at classroom level and by e–learning. Principally, this is because students, pertaining to diverse academic levels experience real and authentic settings, are immersed in dual reality sceneries, benefit from context–aware support, learn diverse educational domains, follow suitable learning paradigms, deal with diverse effects, and interact with different devices and technologies in a blended fashion. All of this with the purpose of enhancing users’ apprenticeship.


title: Multidimensional Divide-and-Conquer and Weighted Digital Sums

abstract: This paper studies three types of functions arising separately in the analysis of algorithms that we analyze exactly using similar Mellin transform techniques. The first is the solution to a Multidimensional Divide-and-Conquer (MDC) recurrence that arises when solving problems on points in $d$-dimensional space. The second involves weighted digital sums. Write $n$ in its binary representation $n=(b_i b_{i-1}... b_1 b_0)_2$ and set $S_M(n) = \sum_{t=0}^i t^{\bar{M}} b_t 2^t$. We analyze the average $TS_M(n) = \frac{1}{n}\sum_{j<n} S_M(j)$. The third is a different variant of weighted digital sums. Write $n$ as $n=2^{i_1} + 2^{i_2} + ... + 2^{i_k}$ with $i_1>i_2>...>i_k\geq 0$ and set $W_M(n) = \sum_{t=1}^k t^M 2^{i_t}$. We analyze the average $TW_M(n) = \frac{1}{n}\sum_{j<n} W_M(j)$. We show that both the MDC functions and $TS_M(n)$ (with $d=M+1$) have solutions of the form $\lambda_d n \lg^{d-1}n + \sum_{m=0}^{d-2}(n\lg^m n)A_{d,m}(\lg n) + c_d,$ where $\lambda_d,c_d$ are constants and $A_{d,m}(u)$'s are periodic functions with period one (given by absolutely convergent Fourier series). We also show that $TW_M(n)$ has a solution of the form $n G_M(\lg n) + d_M \lg^M n + \sum_{d=0}^{M-1}(\lg^d n)G_{M,d}(\lg n),$ where $d_M$ is a constant, $G_M(u)$ and $G_{M,d}(u)$'s are again periodic functions with period one (given by absolutely convergent Fourier series).