title: A Community Based Social Recommender System for Individuals & Groups

abstract: Social networks provide rich new types of data that promise to personalize and improve Recommender Systems. One promising type of data extracted from social networks is relationships. While existing recommenders have capitalized on the use of explicit relationship data such as friendships, the use of implicit relationship data (that gathered from less obvious connections such as co-occurrence) offers great potential for diversifying recommendation for both individuals and groups. Our novel Community Based Social Recommender System (CBSRS) utilizes this new social data to provide personalized recommendations based on communities constructed from the users' social interaction history with the items in the target domain. It allows quick and efficient recommendations to groups as well as individuals. We propose and evaluate such approach using the Internet Movie database (IMDb). We use the underlying social network graph of the movies based on their common reviewers to model the generic network of interests. Communities are then discovered and used as a basis to provide extensive and diverse recommendation for one user, couples or group of users (friends, family, co-workers) offering them movies of their common interests. Finally we demonstrate that the proposed CBSRS increases the accuracy in the results whilst it addresses the cold sparsity of data and start problem for new users and items and provides recommendation for both individuals and groups.


title: Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities

abstract: Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this paper, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus, enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.


title: PRISM: an algorithm for inducing modular rules

abstract: Abstract The decision tree output of Quinlan's ID3 algorithm is one of its major weaknesses. Not only can it be incomprehensible and difficult to manipulate, but its use in expert systems frequently demands irrelevant information to be supplied. This report argues that the problem lies in the induction algorithm itself and can only be remedied by radically altering the underlying strategy. It describes a new algorithm, PRISM which, although based on ID3, uses a different induction strategy to induce rules which are modular, thus avoiding many of the problems associated with decision trees.


title: Study and Design of Intelligent Management System for Student’s Attendance

abstract: Aiming at the problems existing in the work of traditional student's attendance, using computer technology combined with the more advanced technology of face recognition, a real-time processing intelligent management system for student's attendance has been studied and designed. The system is mainly composed of image collector and attendance management computer. Its control core is the digital signal processor TMS320DM6437, and peripheral circuits of TMS320DM6437are given. In the image collector, the collection of student's face image is completed by the CMOS cam- era. After collection, the face image is two valued, noise filtered, and the face region is fixed roughly. And then through vertical and horizontal projection, eyes are positioned precisely, according to the geometric characteristics of human face. The face image is cut out, and then through the image normalization and histogram equalization, the standard face image of the student is obtained. Finally, the standard image is decomposed by two-layered wavelet, feature vectors of the stu- dent's image are extracted, and, in this way, student's attendance is completed. The attendance results are stored in the memory, at the same time, the results are sent to the attendance management computer through the network. When a stu- dent is absent, messages are sent to his mobile phone through the wireless communication module in image collector. In the image collector, the C language is used to program, and the control function of attendance management computer is achieved by Visual Basic.


title: A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior

abstract: The purpose of this study is to propose an extended model of Theory of Planned Behavior (TPB) by incorporating constructs drawn from the model of Expectation Disconfirmation Theory (EDT) and to examine the antecedents of users' intention to continue using online shopping (continuance intention). Prior research has demonstrated that TPB constructs, including attitude, subjective norm, and perceived behavioral control, are important factors in determining the acceptance and use of various information technologies. These factors, however, are insufficient to explain a user's continuance intention in the online shopping context. In this study we extended TPB with two EDT constructs-disconfirmation and satisfaction-for studying users' continuance intention in the online shopping context. By employing longitudinal method with two-stage survey, we empirically validated the proposed model and research hypotheses.


title: Network intrusion detection system model based on data mining

abstract: The paper's object is to develop a network intrusion detection model based on data mining technology, which can detect known intrusion effectively and has a good capacity to recognize unknown data schema which can't be detected effectively in traditional IDS. The paper mainly does the following work: by analyzing the intrusion deeply, extract the properties which can reflect intrusion characteristics effectively; combine misuse detection, anomaly detection and human intervention, establish rule library based on C.45 decision tree algorithm and use the optimal pattern matching so as to improve detection rate; the hosts are clustered to be IP group based on visit number by k-means clustering algorithm, the audit data are divided into parts under the IP group's direction, and the classifiers are built up by divided audit data respectively, then the detected Data apply different rules according to their own IP group, thereby reduce false positives. The experiments proved that the method is effective to detect intrusion such as scanning and Deny of Service.


title: Attitudes of mothers toward sex education.

abstract: Data are presented on the attitudes of mothers from the entire social class spectrum toward content and timing of sex education for children (CT-Attitudes), and also toward sex education in school (S-Attitudes) in two California communities in 1969. Findings underscore the necessity to consider these two attitudinal variables separately. As a result of their separation for study purposes, it was possible to arrive at a four-fold typology or grouping of mothers: 1) CT liberals in favor, and 2) CT liberals opposed to sex education in schools, 3) CT conservatives in favor, and 4) CT conservatives opposed to school programs of sex education. It is inferred that educational planners need to pay due regard to the sentiments of all four maternal groups and all social classes in developing sex education programs for families and for school children.


title: Flying object detection for automatic UAV recognition

abstract: With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object classification. In this work, we investigate the suitability of a number of flying object detection approaches for the task of UAV detection based on video data from static and moving cameras. We compare approaches based on image differencing with object proposal detectors which are learned from data. Finally, we classify each detection by a convolutional neural network (CNN) into the classes UAV or clutter. Our approach is evaluated on six sequences of challenging real world data which contain multiple UAVs, birds, and background motion.


title: I Forget What That's Called! Children's Online Processing of Disfluencies Depends on Speaker Knowledge

abstract: Speech disfluencies can convey information to listeners: Adults and children predict that filled pauses (e.g., uhh) will be followed by referents that are difficult to describe or are new to the discourse. In adults, this is driven partly by an understanding that disfluencies reflect processing difficulties. This experiment examined whether 3½-year-olds' use of disfluencies similarly involves inferences about processing difficulty. Forty children were introduced to either a knowledgeable or a forgetful speaker, who then produced fluent and disfluent utterances. Children exposed to the knowledgeable speaker looked preferentially at novel, discourse-new objects during disfluent utterances. However, children who heard the forgetful speaker did not. These results suggest that, like adults, children modify their expectations about the informativeness of disfluencies on a speaker-specific basis.


title: Bank Customer Credit Scoring by Using Fuzzy Expert System

abstract: Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.


Dataset shift in classification: terminology, benchmarks and methods


title: Iterative Concave Rank Approximation for Recovering Low-Rank Matrices

abstract: In this paper, we propose a new algorithm for recovery of low-rank matrices from compressed linear measurements. The underlying idea of this algorithm is to closely approximate the rank function with a smooth function of singular values, and then minimize the resulting approximation subject to the linear constraints. The accuracy of the approximation is controlled via a scaling parameter $\delta$, where a smaller $\delta$ corresponds to a more accurate fitting. The consequent optimization problem for any finite $\delta$ is nonconvex. Therefore, in order to decrease the risk of ending up in local minima, a series of optimizations is performed, starting with optimizing a rough approximation (a large $\delta$) and followed by successively optimizing finer approximations of the rank with smaller $\delta$'s. To solve the optimization problem for any $\delta>0$, it is converted to a new program in which the cost is a function of two auxiliary positive semidefinete variables. The paper shows that this new program is concave and applies a majorize-minimize technique to solve it which, in turn, leads to a few convex optimization iterations. This optimization scheme is also equivalent to a reweighted Nuclear Norm Minimization (NNM), where weighting update depends on the used approximating function. For any $\delta>0$, we derive a necessary and sufficient condition for the exact recovery which are weaker than those corresponding to NNM. On the numerical side, the proposed algorithm is compared to NNM and a reweighted NNM in solving affine rank minimization and matrix completion problems showing its considerable and consistent superiority in terms of success rate, especially, when the number of measurements decreases toward the lower-bound for the unique representation.


title: FCTH: Fuzzy Color and Texture Histogram - A Low Level Feature for Accurate Image Retrieval

abstract: This paper deals with the extraction of a new low level feature that combines, in one histogram, color and texture information. This feature is named FCTH - Fuzzy Color and Texture Histogram - and results from the combination of 3 fuzzy systems. FCTH size is limited to 72 bytes per image, rendering this descriptor suitable for use in large image databases. The proposed feature is appropriate for accurately retrieving images even in distortion cases such as deformations, noise and smoothing. It is tested on a large number of images selected from proprietary image databases or randomly retrieved from popular search engines. To evaluate the performance of the proposed feature, the averaged normalized modified retrieval rank was used. An online demo that implements the proposed feature in an image retrieval system is available at: http://orpheus.ee.duth.gr/image_retrieval.


title: Using Visual Analytics to Interpret Predictive Machine Learning Models

abstract: It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing predictive quality. We identify a space of possible solutions and provide two examples of where such techniques have been successfully used in practice.


title: A General Analysis of the Convergence of ADMM

abstract: We provide a new proof of the linear convergence of the alternating direction method of multipliers (ADMM) when one of the objective terms is strongly convex. Our proof is based on a framework for analyzing optimization algorithms introduced in Lessard et al. (2014), reducing algorithm convergence to verifying the stability of a dynamical system. This approach generalizes a number of existing results and obviates any assumptions about specific choices of algorithm parameters. On a numerical example, we demonstrate that minimizing the derived bound on the convergence rate provides a practical approach to selecting algorithm parameters for particular ADMM instances. We complement our upper bound by constructing a nearly-matching lower bound on the worst-case rate of convergence.


title: A Prototype Navigation System for Guiding Blind People Indoors using NXT Mindstorms

abstract: People with visual impairment face enormous difficulties in terms of their mobility as they do not have enough information about their location and orientation with respect to traffic and obstacles on their route. Visually impaired people can navigate unknown areas by relying on the assistance of canes, other people, or specially trained guide dogs. The traditional ways of using guide dogs and long-cane only help to avoid obstacles, not to know where they are. The research presented in this paper introduces a mobile assistant navigation prototype to locate and direct blind people indoors. Since most of the existing navigation systems developed so far for blind people employ a complex conjunction of positioning systems, video cameras, location-based and image processing algorithms, we designed an affordable low-cost prototype navigation system for orienting and tracking the position of blind people in complex environments. The prototype system is based on the inertial navigation system and experiments have been performed on NXT Mindstorms platform.


title: Entity Linking in Queries: Tasks and Evaluation

abstract: Annotating queries with entities is one of the core problem areas in query understanding. While seeming similar, the task of entity linking in queries is different from entity linking in documents and requires a methodological departure due to the inherent ambiguity of queries. We differentiate between two specific tasks, semantic mapping and interpretation finding, discuss current evaluation methodology, and propose refinements. We examine publicly available datasets for these tasks and introduce a new manually curated dataset for interpretation finding. To further deepen the understanding of task differences, we present a set of approaches for effectively addressing these tasks and report on experimental results.


title: The effect of preparation procedures on the interaction of melanin with ionizing radiation

abstract: Melanin, a pigmented biopolymer found in most vertebrates and invertebrates, has many unique properties, including radioprotection. Studies have shown that melanin-covered nanoparticles can be used to protect tissues against radiotoxicity during radioimmunotherapy. In this study we explore the radioprotective properties of melanin using different preparation procedures. Melanin samples from the ink sac of Sepia officinalis were purified using different centrifugation conditions. The pellet was then collected and its X-ray absorption was tested. Our experimental results show that the percentage of X-ray blocking by melanin depends upon the centrifugation force used during the sample preparation. The maximum amount of X-ray blocking occurs when the melanin sample is prepared at 2875g. We conclude that the preparation procedures significantly affect the radioprotective properties of melanin. This finding may be important for applications, such as the use of melanin as a lightweight radiation-protective material.


title: Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology

abstract: Abstract Background Research literature on problematic smartphone use, or smartphone addiction, has proliferated. However, relationships with existing categories of psychopathology are not well defined. We discuss the concept of problematic smartphone use, including possible causal pathways to such use. Method We conducted a systematic review of the relationship between problematic use with psychopathology. Using scholarly bibliographic databases, we screened 117 total citations, resulting in 23 peer-reviewer papers examining statistical relations between standardized measures of problematic smartphone use/use severity and the severity of psychopathology. Results Most papers examined problematic use in relation to depression, anxiety, chronic stress and/or low self-esteem. Across this literature, without statistically adjusting for other relevant variables, depression severity was consistently related to problematic smartphone use, demonstrating at least medium effect sizes. Anxiety was also consistently related to problem use, but with small effect sizes. Stress was somewhat consistently related, with small to medium effects. Self-esteem was inconsistently related, with small to medium effects when found. Statistically adjusting for other relevant variables yielded similar but somewhat smaller effects. Limitations We only included correlational studies in our systematic review, but address the few relevant experimental studies also. Conclusions We discuss causal explanations for relationships between problem smartphone use and psychopathology.


title: K-nearest neighbor search: Fast GPU-based implementations and application to high-dimensional feature matching

abstract: The k-nearest neighbor (kNN) search problem is widely used in domains and applications such as classification, statistics, and biology. In this paper, we propose two fast GPU-based implementations of the brute-force kNN search algorithm using the CUDA and CUBLAS APIs. We show that our CUDA and CUBLAS implementations are up to, respectively, 64X and 189X faster on synthetic data than the highly optimized ANN C++ library, and up to, respectively, 25X and 62X faster on high-dimensional SIFT matching.


title: The Pricing of Investment Grade Credit Risk during the Financial Crisis

abstract: This paper uses a structural model to investigate the pricing of investment grade credit risk during the …nancial crisis. Our analysis suggests that the dramatic recent widening of credit spreads is highly consistent with the decline in the equity market, the increase in its long-term volatility, and an improved investor appreciation of the risks embedded in structured products. In contrast to the main argument in favor of using government funds to help purchase structured credit securities, we …nd little evidence that suggests these markets are experiencing …re sales.


title: Malware detection with quantitative data flow graphs

abstract: We propose a novel behavioral malware detection approach based on a generic system-wide quantitative data flow model. We base our data flow analysis on the incremental construction of aggregated quantitative data flow graphs. These graphs represent communication between different system entities such as processes, sockets, files or system registries. We demonstrate the feasibility of our approach through a prototypical instantiation and implementation for the Windows operating system. Our experiments yield encouraging results: in our data set of samples from common malware families and popular non-malicious applications, our approach has a detection rate of 96% and a false positive rate of less than 1.6%. In comparison with closely related data flow based approaches, we achieve similar detection effectiveness with considerably better performance: an average full system analysis takes less than one second.


title: A comparative study of controllers used for Interleaved Double Dual Boost Converter

abstract: This paper intends to compare controllers used for non-isolated (transformer less) interleaved double dual boost (IDDB) converter which provide high voltage gain and can be used for high-power applications. A control subsystem is required to improve dynamic behaviour of converter and to have a better output response. In most cases small signal model of converter is obtained which are linearized at an operating point to derive a controller which cannot take uncertainties into consideration. Robust control and Nonlinear control methods are probably areas which can handle uncertainties and non-linearities effectively. Conventional linear quadratic regulator (LQR) controller cannot assure stability if system is highly uncertain. A convex model of converter dynamics is obtained taking uncertainties into account. To apply LQR control in uncertain converter case linear matrix inequalities(LMIs) are used to optimize the performance index. A comparative study of LMI-LQR control, LQR Controller and PID controller is done.


Childhood adversities and adult psychopathology in the National Comorbidity Survey Replication (NCS-R) I: Associations with first onset of DSM-IV disorders


title: Interactive Image Segmentation Using Constrained Dominant Sets

abstract: We propose a new approach to interactive image segmentation based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract groups of dominant-set clusters which are constrained to contain user-selected elements. The resulting algorithm can deal naturally with any type of input modality, including scribbles, sloppy contours, and bounding boxes, and is able to robustly handle noisy annotations on the part of the user. Experiments on standard benchmark datasets show the effectiveness of our approach as compared to state-of-the-art algorithms on a variety of natural images under several input conditions.


title: Chip package interaction induced ILD integrity issues in fine pitch flip chip packages

abstract: Thermal compression bonding is being widely used for fine pitch copper pillar assembly as it provides high die placement accuracy and bonding suitable for mass production. In the current work, chip package interaction induced ILD integrity failures under thermo-compression assembly induced stresses were investigated. A step by step EFA (Electrical Failure Analysis) plus PFA (Physical Failure Analysis) methodology was developed to isolate the failing location and confirm the fail mode. The key steps include initially dataloging the fails and running the data through ATPG (Automatic Test Pattern Generation) tools to localize the failing nets. The PFA was then focused on the ILD sections in the vicinity of the Cu pillar interconnect (as the pillar is the primary load transference link between the die and substrate). Specific fail modes were then identified using a combination of Scanning Optical Microscopy (SOM), Scanning Acoustic Microscopy (SAM), layer by layer de-processing and Focused Ion Beam (FIB) cross-sections. The paper summarizes the key findings, failure modes from the study — it was found that the assembly process could result in damage initiation in the ILD which results in functional failures during subsequent reliability testing. The scale of induced damage makes it nearly impossible to detect using either traditional eFA, PFA or a combination of the two. Additionally, key assembly and design parameters, to resolve the fails are also discussed.


title: VQM-based QoS/QoE mapping for streaming video

abstract: Numerous new broadband and multimedia applications have emerged in recent years, in which streaming video services are particularly of interest. Unlike data communication, streaming video requires a subjective measure of quality referred to as the Quality of Experience (QoE), but the subjective evaluation is very time consuming and complex. We are proposing an objective approach to provide mapping between QoE and Quality of Service (QoS), in which Video Quality Metric (VQM) is utilized to indicate QoE. We have analyzed the emulation results and derived a simple formula to estimate QoE from QoS parameters for streaming video under certain conditions. This method is non-intrusive and quick to use.


title: The Barriers to the Use of ICT in Teaching in Saudi Arabia: A Review of Literature.

abstract: The paper reports some of the reasons behind the low use of Information and Communication Technology (ICT) by teachers. The paper has reviewed a number or studies from different parts of the world and paid greater attention to Saudi Arabia. The literature reveals a number of factors that hinder teachers' use of ICT. This paper will focus on lack of access to technology, lack of training and lack of time.


title: Time-frequency analysis of the Arabian flute (Nay) tone applied to automatic music transcription

abstract: Automatic music transcription has gained momentum in years due to the several benefits it provides as educational resources to give budding musicians hints during composition and/or training exercises. Unlike western music, Arabian music has gained only limited attention in literature over the past years. Moreover, analysis of Arabian music tones is more challenging than western music tones since least tone-to-tone distance in Arabian music is half of that of the western music. This paper presents experimental tone investigation for the Arabian flute (also called Nay) using the discrete Fourier transform (DFT). Experimental results showed that the Nay pitches can be detected with improved accuracy when compared to the well-known occidental flute. Comparison results for the Nay and the flute tone features are presented and evaluated using the musical scale (Kurd) as a reference.


title: High speed operation of electrical machines, a review on technology, benefits and challenges

abstract: The High Speed (HS) technology, in electrical machine based drive systems, is reviewed in this paper by investigating around 200 different machine's data that are reported in the literature or open sources. This study includes information regarding clarifying the maximum power vs. speed capability, potential applications, different machine types, design limitation issues consisting of thermal, electro-magnetic, mechanical, control, inverter and power electronics. The limitations of electrical machines in HS technology are classified by introducing HS-index, which is the product of the machine nominal power and nominal speed (= n × P out ). It is shown that HS-index behavior changes by speed, because limitation factors for HS power capability take different physics/nature, as speed increases. It is found that in relatively low speeds, n 20 krpm, additional losses in the machine dictates the maximum power capability. The survey shows that Induction Machine (IM) and Permanent Magnet (PM) machine have relatively superior performance. The maximum rotor surface speed, vmax , is limited to 250-300 m/s (can reach 550 m/s) and is independent of n and output power. The HS-index is a function of v2 max and almost independent of n for n 20 krpm is proportional to v3max / n 2 due to thermal limitations. The maximum power is almost cubically (Pout∝ d2.6) proportional to the rotor diameter.


title: Visuospatial training improves elementary students’ mathematics performance

abstract: Background ::: Although spatial ability and mathematics performance are highly correlated, there is scant research on the extent to which spatial ability training can improve mathematics performance. ::: ::: Aims ::: This study evaluated the efficacy of a visuospatial intervention programme within classrooms to determine the effect on students’ (1) spatial reasoning and (2) mathematics performance as a result of the intervention. ::: ::: Sample ::: The study involved grade six students (ages 10–12) in eight classes. There were five intervention classes (n = 120) and three non-intervention control classes (n = 66). ::: ::: Methods ::: A specifically designed 10-week spatial reasoning programme was developed collaboratively with the participating teachers, with the intervention replacing the standard mathematics curriculum. The five classroom teachers in the intervention programme presented 20 hr of activities aimed at enhancing students’ spatial visualization, mental rotation, and spatial orientation skills. ::: ::: Results ::: The spatial reasoning programme led to improvements in both spatial ability and mathematics performance relative to the control group who received standard mathematics instruction. ::: ::: Conclusions ::: Our study is the first to show that a classroom-based spatial reasoning intervention improves elementary school students’ mathematics performance.


title: Modelling the leaching kinetics of cement-based materials––influence of materials and environment

abstract: The leaching of cement-based materials in aggressive water is studied according to several protocols of accelerated or non-accelerated tests. The paper analyses international experimental published data on leaching concerned with several parameters, like material parameters as water-to-binder ratio and silica fume content, and environmental parameters, like pH and temperature. Then, a simplified model based on this analysis is developed to predict the kinetics of leaching. This model assumes that the rate of leaching follows a square root of time. Each parameter is introduced as a weight function. Particularly, the paper introduces the coupling between the built database and the model.


title: Improving Text Segmentation Using Latent Semantic Analysis: A Reanalysis of Choi, Wiemer-Hastings, and Moore (2001)

abstract: Choi, Wiemer-Hastings, and Moore (2001) proposed to use Latent Semantic Analysis (LSA) to extract semantic knowledge from corpora in order to improve the accuracy of a text segmentation algorithm. By comparing the accuracy of the very same algorithm, depending on whether or not it takes into account complementary semantic knowledge, they were able to show the benefit derived from such knowledge. In their experiments, semantic knowledge was, however, acquired from a corpus containing the texts to be segmented in the test phase. If this hyper-specificity of the LSA corpus explains the largest part of the benefit, one may wonder if it is possible to use LSA to acquire generic semantic knowledge that can be used to segment new texts. The two experiments reported here show that the presence of the test materials in the LSA corpus has an important effect, but also that the generic semantic knowledge derived from large corpora clearly improves the segmentation accuracy.


title: Chapter 15: Advising the Behavioral Investor: Lessons from the Real World

abstract: According to behavioral finance theory, investors are not the rational actors that economic theory describes. Rather, they are human beings whose decision-making can be driven by cognitive and emotional factors. Research evidence shows innumerable examples of investors behaving in ways that are counter to their own best interests. But there is good news about behavioral investors. First, many ways are available in which financial advisors can help their clients stay rational when the markets are not, thus improving their chances of staying with a well devised long-term investment strategy and realizing its ultimate benefits. Second, investment strategies can be constructed that actually profit from the bias-driven decisions of other market participants. Thus, investors can learn and profit from others’ mistakes. The purpose of this chapter is to apply the theory in behavioral finance and economics by exploring the practical, observable manifestations of investor behavior and to quantify their impact on investment results.


title: Block-based Fast Compression for Compound Images

abstract: This paper presents a novel block-based fast compression (BFC) algorithm for compound images that contain graphics, text and natural images. The images are divided to blocks, which are classified into four different types - smooth blocks, text blocks, hybrid blocks and picture blocks with a fast and effective block-based classification algorithm. Four different coding algorithms are carefully designed for each block type according to their different statistical properties to maximize the compression performance. Simulations show that the BFC algorithm we propose has much lower complexity than DjVu with significant better visual quality at high bit rate, and it also outperforms the popular lossy image coding method JPEG.


Fiber-Optic Chemical Sensors and Biosensors


title: Credit scoring models for the microfinance industry using neural networks: Evidence from Peru

abstract: Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring with the microfinance industry is a relatively recent application, and no model which employs a non-parametric statistical technique has yet, to the best of our knowledge, been published. This lack is surprising since the implementation of credit scoring should contribute towards the efficiency of microfinance institutions, thereby improving their competitiveness in an increasingly constrained environment. This paper builds several non-parametric credit scoring models based on the multilayer perceptron approach (MLP) and benchmarks their performance against other models which employ the traditional linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR) techniques. Based on a sample of almost 5500 borrowers from a Peruvian microfinance institution, the results reveal that neural network models outperform the other three classic techniques both in terms of area under the receiver-operating characteristic curve (AUC) and as misclassification costs.


title: Communication Algorithms via Deep Learning

abstract: Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.


Churn Prediction on Huge Telecom Data Using Hybrid Firefly-Particle Swarm Optimization Algorithm Based Classification


title: An adaptable fine-grained sentiment analysis for summarization of multiple short online reviews

abstract: Abstract In this study, we present a novel method in generating summaries of multiple online reviews using a fine-grained sentiment extraction model for short texts, which is adaptable to different domains and languages. Adaptability of a model is defined as its ability to be easily modified and be usable on different domains and languages. This is important because of the diversity of domains and languages available. The fine-grained sentiment extraction model is divided into two methods: sentiment classification and aspect extraction. The sentiment classifier is built using a three-level classification approach, while the aspect extractor is built using extended biterm topic model (eBTM), an extension of LDA topic model for short texts. Overall, results show that the sentiment classifier outperforms baseline models and industry-standard classifiers while the aspect extractor outperforms other topic models in terms of aspect diversity and aspect extracting power. In addition, using the Naver movies dataset, we show that online review summarization can be effectively constructed using the proposed methods by comparing the results of our method and the results of a movie awards ceremony.


title: Submonolayer Growth with Anomalously High Island Density in Hyperthermal Deposition

abstract: We present a rate equation model for submonolayer island growth under conditions where hyperthermal deposition techniques such as low-energy ion deposition are employed to achieve smooth layer-by-layer growth. By asymptotic analysis, we demonstrate that the model exhibits stationary behavior with well-defined dynamic and growth exponents β and Χ, respectively, in the limit of small and high detachment rates. We verify these predictions by using the particle coalescence simulation method. The simulations reveal the existence of a relatively sharp transition regime with an increasing detachment rate of adatoms from high values of the growth exponent β 1 to much smaller values of β determined by detachment and island diffusion processes. Our numerical results for the island size distribution indicate an anomalously high number of small islands, in agreement with available experimental data.


title: Feature Analysis for Fake Review Detection through Supervised Classification

abstract: Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.


title: Look at the Driver, Look at the Road: No Distraction! No Accident!

abstract: The paper proposes an advanced driver-assistance system that correlates the driver's head pose to road hazards by analyzing both simultaneously. In particular, we aim at the prevention of rear-end crashes due to driver fatigue or distraction. We contribute by three novel ideas: Asymmetric appearance-modeling, 2D to 3D pose estimation enhanced by the introduced Fermat-point transform, and adaptation of Global Haar (GHaar) classifiers for vehicle detection under challenging lighting conditions. The system defines the driver's direction of attention (in 6 degrees of freedom), yawning and head-nodding detection, as well as vehicle detection, and distance estimation. Having both road and driver's behaviour information, and implementing a fuzzy fusion system, we develop an integrated framework to cover all of the above subjects. We provide real-time performance analysis for real-world driving scenarios.


title: Holistic Neural Network for CTR Prediction

abstract: This paper proposes HNN, a holistic neural network structure for click-through rate (CTR) prediction in recommender systems. Empirically, equipped with HNN, the performance of deep neural networks for CTR prediction are improved on Criteo and Huawei App Store datasets.


title: Multilevel converter with variable flying capacitor voltage used for virtual infinite capacitor

abstract: We evaluate the flying capacitor multilevel converter (FCMC) with one switched-capacitor cell under asymmetric working condition, as a highly efficient hardware realization for the virtual infinite capacitor (VIC). The VIC is a type of electronic circuit, recently introduced to replace large, expensive and unreliable electrolytic capacitors, mainly intended for smoothing random fluctuations of a DC bus voltage V. By the proposed novel control algorithm, the pulsating power that the VIC takes away from the DC bus is shared among the main buffering and the flying capacitors, with the proportion set by a prescribed value. This facilitates to reduce the physical size of the converter, and hence achieve higher power density. This flying capacitor converter operates in an unconventional way, as the flying capacitor voltage is highly variable and hence the converter works under an asymmetric scenario. An average model of this specific working mode is presented, with the design of a linearized current controller. To improve the capacitance reduction ratio, the average value the of flying capacitor voltage is regulated to a relatively high value, instead of V/2. This is achieved by adjusting the phase shift of the PWM carriers. Both continuous and discontinuous conduction modes are investigated, and verified by simulation.


title: Quantification of Pelvic Tilt in Total Hip Arthroplasty

abstract: In THA, anterior or posterior tilt of the pelvis changes the position of the acetabular component on the coronal plane of the body as compared with its anatomic position in the pelvic bone. To understand the occurrence and clinical importance for patients with pelvic tilt on an operating room table in the lateral decubitus position, we studied 436 patients (477 hips) undergoing primary THA using an imageless computer navigation system that measured tilt. We determined the distribution and magnitude of pelvic tilt, especially tilt of 10° or greater. The distribution of tilt had a range of 25° posterior to 20° anterior. Twenty-nine of 477 (6.1%) hips had zero tilt; 251 (52.6%) had tilt of 1° to 5°; 120 (25.2%) had tilt of 6° to 9°; and 77 (16.1%) had tilt of 10° or greater. The conversion factor for acetabular anteversion has been determined by a mathematical formula by Lembeck et al. and was confirmed by us in practice. Measurement of pelvic tilt during the performance of THA will improve the accuracy of cup position, especially allowing anteversion to be measured on the coronal plane.


title: Detection of IUPAC and IUPAC-like chemical names

abstract: Motivation: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. ::: ::: Results: We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. ::: ::: Availability: We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component. ::: ::: Contact: roman.klinger@scai.fraunhofer.de


Stochastic Road Shape Estimation


title: Freeway Travel Time Prediction Using Takagi-Sugeno-Kang Fuzzy Neural Network

abstract: This article presents a Takagi–Sugeno–Kang Fuzzy Neural Network (TSKFNN) approach to predict freeway corridor travel time with an online computing algorithm. TSKFNN, a combination of a Takagi–Sugeno–Kang (TSK) type fuzzy logic system and a neural network, produces strong prediction performance because of its high accuracy and quick convergence. Real world data collected from US-290 in Houston, Texas are used to train and validate the network. The prediction performance of the TSKFNN is investigated with different combinations of traffic count, occupancy, and speed as input options. The comparison between online TSKFNN, offline TSKFNN, the back propagation neural network (BPNN) and the time series model (ARIMA) is made to evaluate the performance of TSKFNN. The results show that using count, speed, and occupancy together as input produces the best TSKFNN predictions. The online TSKFNN outperforms other commonly used models and is a promising tool for reliable travel time prediction on a freeway corridor.


title: Evaluation of embedded heat extraction for high power density integrated electromagnetic power passives

abstract: The use of embedded solid-state heat extractors to increase the power densities in power electronic modules seems promising. Cross-sectional geometric optimisation was done on heat extraction insert and it was found that the most suitable heat extraction configuration for application in power electronics is the use of flat continuous heat extraction layers aiding in conducting heat to externally mounted heat sinks. Theoretic expected thermal enhancement factors were determined and experimentally verified. Interfacial thermal resistances adversely influence the effectiveness of heat extraction inserts and should be minimised.


title: Self-Powered Wireless Sensor Network for Environmental Monitoring

abstract: Environmental monitoring serves a vital scientific role by revealing long-term trends that can lead to new knowledge and better understanding of the environment. Wireless Sensor Networks (WSNs) are ideal systems for environmental monitoring. Inch scale sensors, with low cost can be deployed over large areas to collect data. However, the energy efficiency of these sensor nodes is limited. Energy harvesting techniques can extend the network lifetime. In this paper, we introduce a self-powered prototype for real-time temperature, humidity and soil moisture monitoring. The prototype uses solar panels to harvest energy from the environment.


title: The Reverse Nasolabial Flap with a Cartilage Graft for the Repair of a Full-Thickness Alar Defect: A Single-Stage Procedure

abstract: A 65-year-old woman with a deeply infiltrating basal cell carcinoma in the right ala underwent full-thickness excision of most of the ala, including the alar rim, crease, and the adjacent cheek, leaving a 'through-and-through' defect. Reconstruction was performed by using the reverse nasolabial flap and a cartilage graft across the alar defect, harvested from the concha, to prevent nostril collapse and to maintain the alar shape. The reverse or turnover nasolabial flap is a variant of the conventional nasolabial flap; however, it may be more suited for the repair of a full-thickness, lateral alar defect. The reverse nasolabial flap functions both as an inner liner and an outer cover and the repair is performed as a single-stage procedure. Furthermore, this flap can provide both excellent function and excellent cosmetic outcome.


title: Estrous synchronization strategies to optimize beef heifer reproductive performance after reproductive tract scoring

abstract: Abstract Three experiments comparing four estrous synchronization protocols were conducted to determine estrous expression rate and artificial insemination pregnancy rate (AI-PR) in heifers with a range (1–5) of reproductive tract scores (RTSs). At enrollment (Day 0), 1783 Angus cross beef heifers from six locations were given body condition score and RTS. The four protocols were: (1) HRTS-DPGF group—heifers with RTS 5 received prostaglandin F2α (PGF; Dinoprost 25 mg; im) on Days 0 and 14; (2) HRTS-CIDR-PGF group—heifers with RTS 5 received a CIDR (1.3-g progesterone) insert on Day 7, followed by CIDR removal and PGF on Day 14; (3) LRTS-CIDR-PGF group—heifers with RTS 4 or less received a CIDR insert on Day 7, followed by CIDR removal and PGF on Day 14; and (4) HRTS-Select-Synch group—heifers with RTS 5 received 100 μg of gonadorelin diacetate tetrahydrate (gonadotropin releasing homone; im) on Day 7 and PGF on Day 14. In all groups, heifers observed in estrus were artificially inseminated (within 120 hours after PGF) using the AM-PM rule. In Experiment 1, estrus expression rates were 82.2% (282/343) and 88.5% (184/208) for HRTS-DPGF and LRTS-CIDR-PGF, respectively (P 0.1) and AI-PR were 52.1 (110/211), 60.3 (129/214), and 58.4% (122/209; P > 0.05). In Experiment 3, estrus expression rates were 77.5 (131/169), 85.5 (142/166), and 83.3% (219/263) for HRTS-DPGF, HRTS-Select-Synch and LRTS-CIDR-PGF (P > 0.05) and AI-PR were 53.3 (90/169), 60.2 (100/166), and 58.6% (154/263; P > 0.1). Overall, estrus expression rates for HRTS-DPGF, HRTS-Select-Synch, LRTS-CIDR-PGF, and HRTS-CIDR-PGF groups were 80.4 (581/723), 85.5 (142/166), 85.1 (579/680), and 86.9% (186/214), respectively; higher for heifers in LRTS-CIDR-PGF and HRTS-CIDR-PGF groups compared to heifers in HRTS-DPGF group (P


On the development of early conditioned reflexes and differentiations of auditory stimuli in infants


title: Color Tunneling: Interactive Exploration and Selection in Volumetric Datasets

abstract: Interactive data exploration and manipulation are often hindered by dataset sizes. For 3D data, this is aggravated by occlusion, important adjacencies, and entangled patterns. Such challenges make visual interaction via common filtering techniques hard. We describe a set of realtime multi-dimensional data deformation techniques that aim to help users to easily select, analyze, and eliminate spatial-and-data patterns. Our techniques allow animation between view configurations, semantic filtering and view deformation. Any data subset can be selected at any step along the animation. Data can be filtered and deformed to reduce occlusion and ease complex data selections. Our techniques are simple to learn and implement, flexible, and real-time interactive with datasets of tens of millions of data points. We demonstrate our techniques on three domain areas: 2D image segmentation and manipulation, 3D medical volume exploration, and astrophysical exploration.


Silent speech recognition from articulatory movements using deep neural network


title: Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors

abstract: Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) to characterize the generation process of local features. This choice has shown to be sufficient for traditional low dimensional local features, e.g., SIFT; and typically, good performance can be achieved with only a few hundred Gaussian distributions. However, the same number of Gaussians is insufficient to model the feature space spanned by higher dimensional local features, which have become popular recently. In order to improve the modeling capacity for high dimensional features, it turns out to be inefficient and computationally impractical to simply increase the number of Gaussians. ::: ::: In this paper, we propose a model in which each local feature is drawn from a Gaussian distribution whose mean vector is sampled from a subspace. With certain approximation, this model can be converted to a sparse coding procedure and the learning/inference problems can be readily solved by standard sparse coding methods. By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC). Moreover, we adopt the recently developed Deep Convolutional Neural Network (CNN) descriptor as a high dimensional local feature and implement image classification with the proposed SCFVC. Our experimental evaluations demonstrate that our method not only significantly outperforms the traditional GMM based Fisher vector encoding but also achieves the state-of-the-art performance in generic object recognition, indoor scene, and fine-grained image classification problems.


title: The CAD Triad Hypothesis: A Mapping Between Three Moral Emotions (Contempt

abstract: It is proposed that 3 emotions-contempt, anger, and disgust-are typically elicited, across cultures, by violations of 3 moral codes proposed by R. A. Shweder and his colleagues (R. A. Shweder, N. C. Much, M. Mahapatra, & L. Park, 1997). The proposed alignment links anger to autonomy (individual rights violations), contempt to community (violation of communal codes, including hierarchy), and disgust to divinity (violations of purity-sanctity). This is the CAD triad hypothesis. Students in the United States and Japan were presented with descriptions of situations that involve I of the types of moral violations and asked to assign either an appropriate facial expression (from a set of 6) or an appropriate word (contempt, anger, disgust, or their translations). Results generally supported the CAD triad hypothesis. Results were further confirmed by analysis of facial expressions actually made by Americans to the descriptions of these situations.


title: Power efficient compressive sensing for continuous monitoring of ECG and PPG in a wearable system

abstract: We demonstrate an end-to-end system prototype using Compressive Sensing (CS) to power-efficiently compress continuous bio-signals in a wearable body sensor network (BSN). We use a variant of a Binary Permuted Block Diagonal (P-BPBD) matrix encoder and pad the input signal symmetrically to achieve high compression ratios for ECG and PPG signals. In our approach, the gateway dynamically tunes compression parameters to adapt to changing signal sparsity levels during continuous monitoring and transmits them to the sensor node. We show that the power consumed by our encoder on a 32MHz Intel® Curie™ compute module is much lower than that consumed by a standard Wavelet-based (DWT) encoder. We also show how CS can simultaneously de-noise ECG signals during reconstruction. After evaluating multiple reconstruction algorithms, we demonstrate real-time signal recovery via an implementation of the Alternating Direction algorithm on a 500MHz Intel® Edison board, used as a gateway.


title: High-Speed Tracking with Kernelized Correlation Filters

abstract: The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.


title: Network Modelling and Simulation Tools

abstract: Abstract Computer network technologies have been growing explosively and the study in computer networks is being a challenging task. To make this task easy, different users, researchers and companies have developed different network modelling and simulation (MS) tools. These network MS tools can be used in education and research as well as practical purposes. They vary with their characteristics. This paper reviews some of the most important network MS tools developed recently. This paper also shows a classification of the tools used in communications networks.


title: Resolving human object recognition in space and time

abstract: A comprehensive picture of object processing in the human brain requires combining both spatial and temporal information about brain activity. Here, we acquired human magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) responses to 92 object images. Multivariate pattern classification applied to MEG revealed the time course of object processing: whereas individual images were discriminated by visual representations early, ordinate and superordinate category levels emerged relatively later. Using representational similarity analysis, we combine human fMRI and MEG to show content-specific correspondence between early MEG responses and primary visual cortex (V1), and later MEG responses and inferior temporal (IT) cortex. We identified transient and persistent neural activities during object processing, with sources in V1 and IT., Finally, human MEG signals were correlated to single-unit responses in monkey IT. Together, our findings provide an integrated space- and time-resolved view of human object categorization during the first few hundred milliseconds of vision.


title: Discretized streams: fault-tolerant streaming computation at scale

abstract: Many "big data" applications must act on data in real time. Running these applications at ever-larger scales requires parallel platforms that automatically handle faults and stragglers. Unfortunately, current distributed stream processing models provide fault recovery in an expensive manner, requiring hot replication or long recovery times, and do not handle stragglers. We propose a new processing model, discretized streams (D-Streams), that overcomes these challenges. D-Streams enable a parallel recovery mechanism that improves efficiency over traditional replication and backup schemes, and tolerates stragglers. We show that they support a rich set of operators while attaining high per-node throughput similar to single-node systems, linear scaling to 100 nodes, sub-second latency, and sub-second fault recovery. Finally, D-Streams can easily be composed with batch and interactive query models like MapReduce, enabling rich applications that combine these modes. We implement D-Streams in a system called Spark Streaming.


title: Modeling and optimization of building mix and energy supply technology for urban districts

abstract: Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance trade-offs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system efficiency.


title: Rendezvous design algorithms for wireless sensor networks with a mobile base station

abstract: Recent research shows that significant energy saving can be achieved in wireless sensor networks with a mobile base station that collects data from sensor nodes via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. To address this issue, we propose a rendezvous-based data collection approach in which a subset of nodes serve as the rendezvous points that buffer and aggregate data originated from sources and transfer to the base station when it arrives. This approach combines the advantages of controlled mobility and in-network data caching and can achieve a desirable balance between network energy saving and data collection delay. We propose two efficient rendezvous design algorithms with provable performance bounds for mobile base stations with variable and fixed tracks, respectively. The effectiveness of our approach is validated through both theoretical analysis and extensive simulations.


abstract: In this paper, we establish a theoretical connection between the classical Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer Networks (STNs). STNs are of interest to the vision and learning communities due to their natural ability to combine alignment and classification within the same theoretical framework. Inspired by the Inverse Compositional (IC) variant of the LK algorithm, we present Inverse Compositional Spatial Transformer Networks (IC-STNs). We demonstrate that IC-STNs can achieve better performance than conventional STNs with less model capacity, in particular, we show superior performance in pure image alignment tasks as well as joint alignment/classification problems on real-world problems.


title: Introducing Noise in Decentralized Training of Neural Networks

abstract: It has been shown that injecting noise into the neural network weights during the training process leads to a better generalization of the resulting model. Noise injection in the distributed setup is a straightforward technique and it represents a promising approach to improve the locally trained models. We investigate the effects of noise injection into the neural networks during a decentralized training process. We show both theoretically and empirically that noise injection has no positive effect in expectation on linear models, though. However for non-linear neural networks we empirically show that noise injection substantially improves model quality helping to reach a generalization ability of a local model close to the serial baseline.


title: No more ready-made deals: constructive recommendation for telco service bundling

abstract: We propose a new recommendation system for service and product bundling in the domain of telecommunication and multimedia. Using this system, users can easily generate a combined service plan that best suits their needs within a vast range of candidates. The system exploits the recent constructive preference elicitation framework, which allows us to flexibly model the exponentially large domain of bundle offers as an implicitly defined set of variables and constraints. The user preferences are modeled by a utility function estimated via coactive learning interaction, while iteratively generating high-utility recommendations through constraint optimization. In this paper, we detail the structure of our system, as well as the methodology and results of an empirical validation study which involved more than 130 participants. The system turned out to be highly usable with respect to both time and number of interactions, and its outputs were found much more satisfactory than those obtained with standard techniques used in the market.


title: Categorical term frequency probability based feature selection for document categorization

abstract: Document categorization technology heavily relies on the categorical distribution of features. Those terms which occur unevenly in various categories have strong distinguishable information as to categorization. At first, we give the definition of CTFP (Categorical Term Frequency Probability), which will be used to accurately reflect the categorical characteristics of terms on each category. Then, the CTFP_VM (Variance-Mean based on CTFP) feature selection criterion is introduced to reveal the category distribution difference. After computing and ranking the variance mean based on CTFP distribution for each term, feature sets are obtained for document categorization. We perform the document categorization experiments on SVM classifiers with the well-known Reuters-21578 and 20 news-18828 corpuses as unbalanced and balanced corpus respectively. Experiments compare the novel methods with other conventional feature selection algorithms and the proposed method achieves the best feature set for document categorization The experimental results also demonstrate that the proposed variance mean feature selection method base on CTFP not only has better Fl-metric for document categorization but excellent corpus adaptability.


title: High-performance parallel frequent subgraph discovery

abstract: Discovery of frequent subgraphs of an input network is one of the most important facilities for mining and analyzing complex networks. The most accurate solution to frequent subgraph discovery is to enumerate all subgraphs of size k and then count the frequency of each isomorphic class. However, the process is much time consuming because the number of subgraphs grows exponentially with the growth of the input network, or by increasing the size of the subgraphs. Also, there is no known polynomial-time algorithm for subgraph isomorphism detection, and this issue makes the problem harder. Hence, the available solutions can just mine small input networks and small subgraph sizes. A parallel and load-balanced solution named Subdigger is proposed which is faster and more efficient compared to available solutions. Subdigger efficiently executes on current multicore and multiprocessor machines, and incorporates a fast heuristic with a high-performance concurrent data structure which significantly accelerates detection and counting of isomorphic subgraphs. Subdigger can also handle large networks and subgraph sizes using external memory and external sorting. We performed several experiments using real-world input networks. Compared to the available solutions, Subdigger can extract frequent subgraphs much faster and the performance scales almost linearly using additional processor cores. The experimental results show that Subdigger can be more than 100 times faster than other solutions on a 4-core Intel i7 machine. Besides performance, Subdigger can process larger subgraphs using external memory while other tools crash due to memory limitation.


title: Cloud forensics definitions and critical criteria for cloud forensic capability: An overview of survey results

abstract: With the rapid growth of global cloud adoption in private and public sectors, cloud computing environments is becoming a new battlefield for cyber crime. In this paper, the researcher presents the results and analysis of a survey that was widely circulated among digital forensic experts and practitioners internationally on cloud forensics and critical criteria for cloud forensic capability in order to better understand the key fundamental issues of cloud forensics such as its definition, scope, challenges, opportunities as well as missing capabilities based on the 257 collected responses.


title: Forecasting time series with complex seasonal patterns using exponential smoothing

abstract: An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects. The new framework incorporates Box–Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensive approach to forecasting complex seasonal time series. A key feature of the framework is that it relies on a new method that greatly reduces the computational burden in the maximum likelihood estimation. The modeling framework is useful for a broad range of applications, its versatility being illustrated in three empirical studies. In addition, the proposed trigonometric formulation is presented as a means of decomposing complex seasonal time series, and it is show...


title: GLTO: On the Adequacy of Lightweight Thread Approaches for OpenMP Implementations

abstract: OpenMP is the de facto standard application programming interface (API) for on-node parallelism. The most popular OpenMP runtimes rely on POSIX threads (pthreads) implementations that offer an excellent performance for coarse-grained parallelism and match perfectly with the current hardware. However, a recent trend in runtimes/applications points in the direction of leveraging massive on-node parallelism in conjunction with fine-grained and dynamic scheduling paradigms. It has been demonstrated that lightweight thread (LWT) solutions are more appropriate for these new parallel paradigms. We have developed GLTO, an OpenMP implementation over the recently-emerged Generic Lightweight Threads (GLT) API. GLT exports a common API for LWT libraries that offers the possibility of running the same application over different native LWT solutions. In this paper we use GLTO to analyze different scenarios where OpenMP implementations may benefit from the use of either LWT or pthreads. Our study reveals that none of the threading approaches obtains the best performance in all the scenarios, but that there are important gaps among them.


title: Psychedelic drugs should be legally reclassified so that researchers can investigate their therapeutic potential

abstract: Trials of physiologically safe and non-addictive drugs such as LSD are almost impossible, writes James J H Rucker , calling on the authorities to downgrade their unnecessarily restrictive class A, schedule 1 classification


Chalcopyrite Al(Ga, Mn)As2 and AlGa(As, P) Quaternary Semiconductor: A First-principle Study


title: Superwoman Schema: African American Women’s Views on Stress, Strength, and Health

abstract: Researchers have suggested that health disparities in African American women, including adverse birth outcomes, lupus, obesity, and untreated depression, can be explained by stress and coping. The Strong Black Woman/Superwoman role has been highlighted as a phenomenon influencing African American women’s experiences and reports of stress. The purpose of this study was to develop a preliminary conceptual framework for Superwoman Schema (SWS) by exploring women’s descriptions of the Superwoman role; perceptions of contextual factors, benefits, and liabilities; and beliefs regarding how it influences health. Analysis of eight focus group discussions with demographically diverse African American women yielded themes characterizing the Superwoman role and personal or sociohistorical contextual factors. Participants reported that the Superwoman role had benefits (preservation of self and family or community) and liabilities (relationship strain, stress-related health behaviors, and stress embodiment). The SWS f...


title: AMUSE (Advanced MUSic Explorer) - A Multitool Framework for Music Data Analysis

abstract: A large variety of research tools is available now for music information retrieval tasks. In this paper we present a further framework which aims to facilitate the interaction between these applications. Since the available tools are very different in target domain, range of available methods, learning efforts, installation and runtime characteristics etc., it is not easy to find software which is optimal for certain research goals. Another problematic issue is that many incompatible data formats exist, so it is not always possible to use output from one tool just as input for another one. At first we describe some of the available projects and outline our motivation starting the development of AMUSE framework for audio data analysis. Requirements and application purposes are given. The structure of our framework is introduced in detail and the information for efficient application is provided. Finally we discuss several ideas for further work.


title: A secure camouflaged threshold voltage defined logic family

abstract: A myriad of security vulnerabilites can be exposed via the reverse engineering of the integrated circuits contained in electronics systems. The goal of IC reverse engineering is to uncover the functionality and internal structure of the chip via techniques such as depackaging/delayering, high-resolution imaging, probing, and side-channel examination. With this knowledge, an attacker can more efficiently mount various attacks, clone/-counterfeit the design possibly with hardware Trojans inserted, and discover trade secrets. We propose a gate camouflaging technique that relies on the usage of different threshold voltage transistors, but with identical layouts, to determine the logic gate function. In our threshold voltage defined (TVD) camouflaging technique, every TVD logic gate has the same physical structure and is one time mask programmed with different threshold implants for different boolean functionality. We design and implement TVD logic gates in an industrial 65nm bulk CMOS process. Using post-layout extracted simulation, we evaluate the logic style for VLSI overheads (area, power, delay) versus conventional logic, for process variablity robustness, and for various security metrics. Further, we evaluate the macro block overheads for ISCAS benchmark designs under various levels of TVD gate replacement upto and including 100% replacement. TVD logic gates are found to be CMOS process compatible, low overhead, and to increase security against various forms of attacks.


title: Improving exploration capability by interacting with start-ups

abstract: This paper describes and analyses an exploration-capability model that is currently being introduced in an automotive OEM. An increasingly high environmental dynamism as well as a new level of comp ...


title: Layout-induced stress effects in 14nm & 10nm FinFETs and their impact on performance

abstract: The ever increasing stress engineering raises a major concern of strong layout-dependent effects (LDE) in the advanced technology nodes. We report on the dependency of SiGe S/D and STI induced stress on fin length, position of the gate along the fin and fin to fin distances. The efficiency of epitaxial S/D SiGe stressors is reduced when the fin length is decreased and strongly degraded for fins with a single gate regardless of the SiGe depth, resulting in up to 21% performance degradation at ring oscillator level. Although tensile STI improves the NFETs mobility, the use of compressive STI guarantees a constant mobility ratio and limits the performance variation with layout.


title: Using graph theory to analyze biological networks

abstract: Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.


Visual Social Media Use Moderates the Relationship between Initial Problematic Internet Use and Later Narcissism


ON MEANING-PRESERVING ADVERSARIAL PERTUR-


title: Plastic Pollution in the World's Oceans: More than 5 Trillion Plastic Pieces Weighing over 250,000 Tons Afloat at Sea

abstract: Plastic pollution is ubiquitous throughout the marine environment, yet estimates of the global abundance and weight of floating plastics have lacked data, particularly from the Southern Hemisphere and remote regions. Here we report an estimate of the total number of plastic particles and their weight floating in the world's oceans from 24 expeditions (2007-2013) across all five sub-tropical gyres, costal Australia, Bay of Bengal and the Mediterranean Sea conducting surface net tows (N = 680) and visual survey transects of large plastic debris (N = 891). Using an oceanographic model of floating debris dispersal calibrated by our data, and correcting for wind-driven vertical mixing, we estimate a minimum of 5.25 trillion particles weighing 268,940 tons. When comparing between four size classes, two microplastic <4.75 mm and meso- and macroplastic >4.75 mm, a tremendous loss of microplastics is observed from the sea surface compared to expected rates of fragmentation, suggesting there are mechanisms at play that remove <4.75 mm plastic particles from the ocean surface.


title: Validation of a Clinical Prediction Rule for Pediatric Abusive Head Trauma

abstract: BACKGROUND AND OBJECTIVE ::: To reduce missed cases of pediatric abusive head trauma (AHT), Pediatric Brain Injury Research Network investigators derived a 4-variable AHT clinical prediction rule (CPR) with sensitivity of .96. Our objective was to validate the screening performance of this AHT CPR in a new, equivalent patient population. ::: ::: ::: METHODS ::: We conducted a prospective, multicenter, observational, cross-sectional study. Applying the same inclusion criteria, definitional criteria for AHT, and methods used in the completed derivation study, Pediatric Brain Injury Research Network investigators captured complete clinical, historical, and radiologic data on 291 acutely head-injured children <3 years of age admitted to PICUs at 14 participating sites, sorted them into comparison groups of abusive and nonabusive head trauma, and measured the screening performance of the AHT CPR. ::: ::: ::: RESULTS ::: In this new patient population, the 4-variable AHT CPR demonstrated sensitivity of .96, specificity of .46, positive predictive value of .55, negative predictive value of .93, positive likelihood ratio of 1.67, and negative likelihood ratio of 0.09. Secondary analysis revealed that the AHT CPR identified 98% of study patients who were ultimately diagnosed with AHT. ::: ::: ::: CONCLUSIONS ::: Four readily available variables (acute respiratory compromise before admission; bruising of the torso, ears, or neck; bilateral or interhemispheric subdural hemorrhages or collections; and any skull fractures other than an isolated, unilateral, nondiastatic, linear, parietal fracture) identify AHT with high sensitivity in young, acutely head-injured children admitted to the PICU.


title: A New 3D Object Pose Detection Method Using LIDAR Shape Set

abstract: In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird’s eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets.


title: Video Anomaly Detection With Compact Feature Sets for Online Performance

abstract: Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene’s activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains, and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing data sets and on a new data set comprising a wide variety of realistic videos captured by surveillance cameras. This particular data set includes surveillance videos depicting criminal activities, car accidents, and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared with state-of-the-art non-online methods.


title: A high-speed low voltage CMOS Schmitt Trigger with adjustable hysteresis

abstract: This paper presents a brief overview of Schmitt Triggers and proposes a low voltage adjustable CMOS Schmitt trigger using body biasing technique. The voltage-feedback inverter with body control is employed to speed up the switching process, and control the intensity of the feedback. The proposed Schmitt trigger makes it particularly attractive for low-voltage high-speed applications. The CMOS Schmitt Trigger circuit has been designed using 40nm 1V CMOS technology and simulated using HSPICE with BSIM4 device models. Simulation results are presented.


title: Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision

abstract: Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energy constraints of mobile devices. In this paper, we propose and develop an algorithm-architecture co-designed system, Euphrates, that simultaneously improves the energy-efficiency and performance of continuous vision tasks. Our key observation is that changes in pixel data between consecutive frames represents visual motion. We first propose an algorithm that leverages this motion information to relax the number of expensive CNN inferences required by continuous vision applications. We co-design a mobile System-on-a-Chip (SoC) architecture to maximize the efficiency of the new algorithm. The key to our architectural augmentation is to co-optimize different SoC IP blocks in the vision pipeline collectively. Specifically, we propose to expose the motion data that is naturally generated by the Image Signal Processor (ISP) early in the vision pipeline to the CNN engine. Measurement and synthesis results show that Euphrates achieves up to 66% SoC-level energy savings (4 times for the vision computations), with only 1% accuracy loss.


title: Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation

abstract: Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.


title: \pi Match: Monocular vSLAM and Piecewise Planar Reconstruction Using Fast Plane Correspondences

abstract: This paper proposes \(\pi \)Match, a monocular SLAM pipeline that, in contrast to current state-of-the-art feature-based methods, provides a dense Piecewise Planar Reconstruction (PPR) of the scene. It builds on recent advances in planar segmentation from affine correspondences (ACs) for generating motion hypotheses that are fed to a PEaRL framework which merges close motions and decides about multiple motion situations. Among the selected motions, the camera motion is identified and refined, allowing the subsequent refinement of the initial plane estimates. The high accuracy of this two-view approach allows a good scale estimation and a small drift in scale is observed, when compared to prior monocular methods. The final discrete optimization step provides an improved PPR of the scene. Experiments on the KITTI dataset show the accuracy of \(\pi \)Match and that it robustly handles situations of multiple motions and pure rotation of the camera. A Matlab implementation of the pipeline runs in about 0.7 s per frame.


title: An alternative Biot's displacement formulation for porous materials.

abstract: This paper proposes an alternative displacement formulation of Biot’s linear model for poroelastic materials. Its advantage is a simplification of the formalism without making any additional assumptions. The main difference between the method proposed in this paper and the original one is the choice of the generalized coordinates. In the present approach, the generalized coordinates are chosen in order to simplify the expression of the strain energy, which is expressed as the sum of two decoupled terms. Hence, new equations of motion are obtained whose elastic forces are decoupled. The simplification of the formalism is extended to Biot and Willis thought experiments, and simpler expressions of the parameters of the three Biot waves are also provided. A rigorous derivation of equivalent and limp models is then proposed. It is finally shown that, for the particular case of sound-absorbing materials, additional simplifications of the formalism can be obtained.


title: Two core implementation of coupled inductor for parallel three-phase power converters

abstract: Coupled inductors are widely used for parallel operation of three-phase converters. By adding one coupled inductor for each phase, the circulating current between two converters can be effectively suppressed. The conventional structure of the coupled inductor is originated from DC-DC conversion and does not employ any characteristics of 3-phase system. In this paper, an integrated structure of coupled inductors is proposed for 3-phase AC-DC power conversion. According to the differential-mode and common-mode, three coupled inductors can be integrated into two; one for suppressing differential-mode circulating current, the other for common-mode circulating current. The proposed integrated coupled inductors can easily be built with standard shape cores. The validity of proposed integration is verified by an experiment with 2kW paralleled converters. With proposed integration, the total weight of the coupled inductors could be reduced by 30%.


title: Evaluation of Mininet-WiFi integration via ns-3

abstract: Mininet is a Linux-based network emulator that is particularly widely used for Software Defined Network experiments, due to its in-built support for OpenFlow switches. However, Mininet currently lacks support for wireless links. A recent work has addressed this limitation by using the real-time feature of ns-3 to integrate the IEEE 802.11 channel emulation feature with Mininet, which we refer to as Mininet-ns3-WiFi. While this approach has great potential to serve as an experimental platform, in particular for Software Defined Wireless Networks, it has not been extensively evaluated in terms of experiment result accuracy and fidelity. This is critical for any system that integrates simulation with real-time components. In this paper, we present a detailed evaluation of the fidelity of experimental results of Mininet-ns3-WiFi. We further present a reliable and low cost method that gives an experimenter an indicator about the fidelity and trustworthiness of the results.


title: A magnetic cell-based sensor

abstract: Cell-based sensing represents a new paradigm for performing direct and accurate detection of cell- or tissue-specific responses by incorporating living cells or tissues as an integral part of a sensor. Here we report a new magnetic cell-based sensing platform by combining magnetic sensors implemented in the complementary metal-oxide-semiconductor (CMOS) integrated microelectronics process with cardiac progenitor cells that are differentiated directly on-chip. We show that the pulsatile movements of on-chip cardiac progenitor cells can be monitored in a real-time manner. Our work provides a new low-cost approach to enable high-throughput screening systems as used in drug development and hand-held devices for point-of-care (PoC) biomedical diagnostic applications.


title: Precision agriculture applications using wireless moisture sensor network

abstract: Improper irrigation will result in waste of water. Proper irrigation system could be achieved by using Wireless sensor networks (WSN) technology. Monitoring and control applications have been tremendously improved by using Wireless sensor networks technology. It enabled efficient communication with many sensors. Wireless moisture sensor network (WMSN) is a WSN with moisture sensors. Efficient irrigation could be achieved through the utilisation of WMSN. WMSN enabled efficient irrigation in a greenhouse for Precision Agriculture (PA) application. In this paper, we describe the overall system design, system architecture, implementation, system test results of WMSN for PA in a greenhouse. Moisture Sensors are used to measure amount of water in the media. Close loop system acquires sensors data and activates the control devices based on predetermined threshold value. A test was conducted to compare the efficiency of irrigation using two methodologies; schedule and automated. The test shows that there is an average savings of 1,500 ml per day per tree.


www.jatit.org THE IMPACT OF CHANGE MANAGEMENT IN ERP SYSTEM: A CASE STUDY OF MADAR


title: Throwing the rascals out? The electoral effects of corruption allegations and corruption scandals in Europe 1981–2011

abstract: Corrupt politicians have to a surprisingly great extent been found to go unpunished by the electorate. These findings are, however, drawn from case studies on a limited number of countries. This study, on the contrary, is based on a unique dataset from 215 parliamentary election campaigns in 32 European countries between 1981 and 2011, from which the electoral effects of corruption allegations and corruption scandals are analyzed. Information about the extent to which corruption allegations and scandals have occurred is gathered from election reports in several political science journals, and the electoral effects are measured in terms of the electoral performances—the difference in the share of votes between two elections—of all parties in government, as well as the main incumbent party, and the extent to which the governments survive the election. The control variables are GDP growth and unemployment rate the year preceding the election, the effective number of parliamentary and electoral parties, and the level of corruption. The results show that both corruption allegation and corruption scandals are significantly correlated with governmental performances on a bivariate basis; however, not with governmental change. When controlling for other factors, only corruption allegation has an independent effect on government performances. The study thus concludes—in line with previous research—that voters actually punish corrupt politicians, but to a quite limited extent.