title: The Impact of Internet Addiction on Life Satisfaction and Life Engagement in Young Adults

abstract: This study examined the impact of Internet addiction (IA) on life satisfaction and life engagement in young adults. A total of 210 University students participated in the study. Multivariate regression analysis showed that the model was significant and contributes 8% of the variance in life satisfaction (Adjusted R 2 =.080, p<.001) and 2.8% of the variance in life engagement (Adjusted R 2 =.028, p<.05). Unstandardized regression coefficient (B) indicates that one unit increase in raw score of Internet addiction leads to .168 unit decrease in raw score of life satisfaction (B=-.168, p<.001) and .066 unit decrease in raw score of life engagement (B=-.066, p<.05). Means and standard deviations of the scores on IA and its dimensions showed that the most commonly given purposes of Internet are online discussion, adult chatting, online gaming, chatting, cyber affair and watching pornography. Means and standard deviations of the scores on IA and its dimensions across different types of social networking sites further indicate that people who frequently participate in skype, twitter and facebook have relatively higher IA score. Correlations of different aspects of Internet use with major variables indicate significant and positive correlations of Internet use with IA, neglect of duty and virtual fantasies. Implications of the findings for theory, research and practice are discussed.


title: Magnetically actuated soft capsule endoscope for fine-needle aspiration biopsy

abstract: This paper presents a magnetically actuated soft capsule endoscope for fine-needle aspiration biopsy (B-MASCE) in the upper gastrointestinal tract. A thin and hollow needle is attached to the capsule, which can penetrate deeply into tissues to obtain subsurface biopsy sample. The design utilizes a soft elastomer body as a compliant mechanism to guide the needle. An internal permanent magnet provides a means for both actuation and tracking. The capsule is designed to roll towards its target and then deploy the biopsy needle in a precise location selected as the target area. B-MASCE is controlled by multiple custom-designed electromagnets while its position and orientation are tracked by a magnetic sensor array. In in vitro trials, B-MASCE demonstrated rolling locomotion and biopsy of a swine tissue model positioned inside an anatomical human stomach model. It was confirmed after the experiment that a tissue sample was retained inside the needle.


title: Active components and clinical applications of olive oil.

abstract: The olive tree, Olea europaea, is native to the Mediterranean basin and parts of Asia Minor. The fruit and compression-extracted oil have a wide range of therapeutic and culinary applications. Olive oil also constitutes a major component of the "Mediterranean diet." The chief active components of olive oil include oleic acid, phenolic constituents, and squalene. The main phenolics include hydroxytyrosol, tyrosol, and oleuropein, which occur in highest levels in virgin olive oil and have demonstrated antioxidant activity. Antioxidants are believed to be responsible for a number of olive oil's biological activities. Oleic acid, a monounsaturated fatty acid, has shown activity in cancer prevention, while squalene has also been identified as having anticancer effects. Olive oil consumption has benefit for colon and breast cancer prevention. The oil has been widely studied for its effects on coronary heart disease (CHD), specifically for its ability to reduce blood pressure and low-density lipoprotein (LDL) cholesterol. Antimicrobial activity of hydroxytyrosol, tyrosol, and oleuropein has been demonstrated against several strains of bacteria implicated in intestinal and respiratory infections. Although the majority of research has been conducted on the oil, consumption of whole olives might also confer health benefits.


title: Muscular strategy shift in human running: dependence of running speed on hip and ankle muscle performance.

abstract: Humans run faster by increasing a combination of stride length and stride frequency. In slow and medium-paced running, stride length is increased by exerting larger support forces during ground contact, whereas in fast running and sprinting, stride frequency is increased by swinging the legs more rapidly through the air. Many studies have investigated the mechanics of human running, yet little is known about how the individual leg muscles accelerate the joints and centre of mass during this task. The aim of this study was to describe and explain the synergistic actions of the individual leg muscles over a wide range of running speeds, from slow running to maximal sprinting. Experimental gait data from nine subjects were combined with a detailed computer model of the musculoskeletal system to determine the forces developed by the leg muscles at different running speeds. For speeds up to 7 m s(-1), the ankle plantarflexors, soleus and gastrocnemius, contributed most significantly to vertical support forces and hence increases in stride length. At speeds greater than 7 m s(-1), these muscles shortened at relatively high velocities and had less time to generate the forces needed for support. Thus, above 7 m s(-1), the strategy used to increase running speed shifted to the goal of increasing stride frequency. The hip muscles, primarily the iliopsoas, gluteus maximus and hamstrings, achieved this goal by accelerating the hip and knee joints more vigorously during swing. These findings provide insight into the strategies used by the leg muscles to maximise running performance and have implications for the design of athletic training programs.


title: Preventing PCR amplification carryover contamination in a clinical laboratory.

abstract: During the past two decades PCR and several other DNA/RNA amplification techniques have become important diagnostic tools in clinical laboratories. Amplification products contamination has been the main impediment to using these techniques routinely in diagnostic laboratories. Over the years, several creative pre- and post-amplification methods have been developed that prevent amplicon carryover contamination. These procedures, coupled with automated systems that employ real-time amplification and simultaneous detection in a closed system, have substantially reduced the possibility of false positive results due to amplification products carryover contamination.


title: A design process for the development of innovative smart clothing that addresses end-user needs from technical, functional, aesthetic and cultural view points

abstract: The research and development of attractive smart clothing, with truly embedded technologies, demands the merging of science and technology with art and design. This paper looks at a comparatively new and unique design discipline that has been given little prior consideration. The concept of 'wearables' crosses the boundaries between many disciplines. A gap exists for a common 'language' that facilitates creativity and a systematic design process. Designers of smart clothing require guidance in their enquiry, as gaining an understanding of issues such as; usability, manufacture, fashion, consumer culture, recycling and end user needs can seem forbidding and difficult to prioritise. This paper presents a design tool in a format that can be understood by practitioners who come from a range of backgrounds. The representation of the 'critical path' is intended as a tool to guide the design research and development process in the application of smart technologies.


Knowledge Graph Embedding with Numeric Attributes of Entities


title: Design of 2×1 circularly polarized microstrip patch antenna array for 5.8 GHz ISM band applications

abstract: This paper describes the design of a circular microstrip patch antenna for 5.8 GHz ISM band applications which is fed by a microstrip line and a two element antenna array of the same. The corporate feeding Method is used to design an array. The industrial, scientific and medical (ISM) radio bands are radio bands (portions of the radio spectrum) reserved internationally for the use of radio frequency (RF) energy for industrial, scientific and medical purposes other than communications. A suitable microstrip patch antenna (MSA) and 2×1 array should be operating in the ISM band radio frequency range and also should have satisfactory radiation property. In start, the paper presents the single element antenna structure and its simulation results and afterwards the two element array. All the simulated results obtained are presented succinctly. High frequencies structure simulation software is used to simulate and optimize the performance of the antenna array.


title: Tree Edit Distance Learning via Adaptive Symbol Embeddings

abstract: Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which we call embedding edit distance learning (BEDL) and which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that BEDL improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.


title: Implementation of Workpiece Recognition and Location Based on Opencv

abstract: A method of workpiece recognition and location by Hu moment invariants based on Open Source Computer Vision(Opencv) is presented in this paper. Firstly, the methods of image preprocessing including image graying, mean filter, image binaryzation by adaptive threshold segmentation are used. Then, we extract the contours from the binary image. Finally, we can identify the object workpiece by matching the extracted contours with the object contour from the template image by Hu moment invariants. At the same time, the method of confirming the workpiece position and direction is put forward. Experimental results show that the proposed method can recognize the target workpiece and locate the position effectively. The used algorithms are simple and fast in operation.


title: Approximate querying of RDF graphs via path alignment

abstract: A query over RDF data is usually expressed in terms of matching between a graph representing the target and a huge graph representing the source. Unfortunately, graph matching is typically performed in terms of subgraph isomorphism, which makes semantic data querying a hard problem. In this paper we illustrate a novel technique for querying RDF data in which the answers are built by combining paths of the underlying data graph that align with paths specified by the query. The approach is approximate and generates the combinations of the paths that best align with the query. We show that, in this way, the complexity of the overall process is significantly reduced and verify experimentally that our framework exhibits an excellent behavior with respect to other approaches in terms of both efficiency and effectiveness.


title: Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network

abstract: Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-todisc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.


title: CRISPR/Cas9, a universal tool for genomic engineering

abstract: The CRISPR/Cas9 system was initially described as an element of archeal and bacterial adaptive immunity, but has recently gained much attention in genetic engineering due to its outstanding ability to be programmed to target any genomic loci through a short 20-nucleotide region of single guide RNA (sgRNA). This review deals with some modern applications of the CRISPR/Cas9 system. First, we describe the basic mechanism of the CRISPR/Cas9 DNA recognition and binding with a particular focus on its off-target activity. The CRISPR/Cas9 off-target activity refers to nonspecific recognition of genomic sites that have partial homology with sgRNA. Such activity occasionally results in unwanted mutations throughout the genome. We also note some recent method improvements for enhancing Cas9 specificity or adding new functions to the system. Since the active use of CRISPR/Cas9 is mostly driven by its remarkable potential for gene therapy and genome engineering, the latest CRISPR/Cas9 applications in these areas are also discussed in our review. In particular, the CRISPR/Cas9 system was recently used to control the human immunodeficiency virus (HIV) infection and to repair genetic abnormalities, such as Duchenne muscular dystrophy or retinitis pigmentosa, both in cell cultures and rodent models. The programmable nature of CRISPR/Cas9 facilitates the creation of transgenic organisms through site-specific gene mutations, knock-ins or large chromosomal rearrangements (deletions, inversions and duplications). CRISPR/Cas9 was also found to be especially useful in pronuclear microinjections of farm animals, which has a strong impact on biotechnology. In addition, we reviewed CRISPR/Cas9-based genetic screens, which provide highly efficient identification of new genes and pathways involved in many biological processes. Finally, we discuss the use of gene drivers based on CRISPR/Cas9, which represent a powerful tool for the modification of ecosystems in the nearest future.


title: mirracle: An augmented reality magic mirror system for anatomy education

abstract: We present an augmented reality magic mirror for teaching anatomy. The system uses a depth camera to track the pose of a user standing in front of a large display. A volume visualization of a CT dataset is augmented onto the user, creating the illusion that the user can look into his body. Using gestures, different slices from the CT and a photographic dataset can be selected for visualization. In addition, the system can show 3D models of organs, text information and images about anatomy. For interaction with this data we present a new interaction metaphor that makes use of the depth camera. The visibility of hands and body is modified based on the distance to a virtual interaction plane. This helps the user to understand the spatial relations between his body and the virtual interaction plane.


title: Bank direct marketing analysis of asymmetric information based on machine learning

abstract: The bank direct marketing campaign for offering products that meet the customers' needs is the challenge problems. The bank direct marketing data analysis is important work that helps the banks predict whether customers will sign a long term deposits with the banks. The method that can predict such customers' needs can be profitable to the banks for improving their marketing campaign strategies. Unfortunately, it is very hard to predict the customers' needs because the available information is asymmetric. In this paper, we propose the method to analyze asymmetric information using SMOTE algorithm and Rotation Forest (PCA)-J48. The SMOTE method is used to modify the data and improve the accuracy of the prediction. The performance of the proposed method is evaluated and compared to Decision Tree, Rotation Forest, Navie Bayes, BayesNet, Multilayer Perceptron Neural Network, RBF Neural Network. The experimental results show the predicting accuracies of all predictors. The experiments show that Rotation Forest (PCA)- J48 can achieve the highest value of accuracy and specificity. However, the sensitivity of Rotation Forest (PCA)-J48 is higher than all methods except BayesNet and Rotation Forest (PCA) RandomTree.


title: Depth Image-Based Rendering With Advanced Texture Synthesis for 3-D Video

abstract: In free viewpoint television or 3D video, depth image based rendering (DIBR) is used to generate virtual views based on a textured image and its associated depth information. In doing so, image regions which are occluded in the original view may become visible in the virtual image. One of the main challenges in DIBR is to extrapolate known textures into the disoccluded area without inserting subjective annoyance. In this paper, a new hole filling approach for DIBR using texture synthesis is presented. Initially, the depth map in the virtual view is filled at disoccluded locations. Then, in the textured image, holes of limited spatial extent are closed by solving Laplace equations. Larger disoccluded regions are initialized via median filtering and subsequently refined by patch-based texture synthesis. Experimental results show that the proposed approach provides improved rendering results in comparison to the latest MPEG view synthesis reference software (VSRS) version 3.6 [1].


title: Using one-time passwords to prevent password phishing attacks

abstract: Phishing is now a serious threat to the security of Internet users' confidential information. Basically, an attacker (phisher) tricks people into divulging sensitive information by sending fake messages to a large number of users at random. Unsuspecting users who follow the instruction in the messages are directed to well-built spoofed web pages and asked to provide sensitive information, which the phisher then steals. Based on our observations, more than 70% of phishing activities are designed to steal users' account names and passwords. With such information, an attacker can retrieve more valuable information from the compromised accounts. Statistics published by the anti-phishing working group (APWG) show that, at the end of Q2 in 2008, the number of malicious web pages designed to steal users' passwords had increased by 258% over the same period in 2007. Therefore, protecting users from phishing attacks is extremely important. A naive way to prevent the theft of passwords is to avoid using passwords. This raises the following question: Is it possible to authenticate a user without a preset password? In this paper, we propose a practical authentication service that eliminates the need for preset user passwords during the authentication process. By leveraging existing communication infrastructures on the Internet, i.e., the instant messaging service, it is only necessary to deploy the proposed scheme on the server side. We also show that the proposed solution can be seamlessly integrated with the OpenID service so that websites supporting OpenID benefit directly from the proposed solution. The proposed solution can be deployed incrementally, and it does not require client-side scripts, plug-ins, nor external devices. We believe that the number of phishing attacks could be reduced substantially if users were not required to provide their own passwords when accessing web pages.


title: Delay Compensation of Tilt Sensors Based on MEMS Accelerometer Using Data Fusion Technique

abstract: In this paper, the inaccuracies of the tilt sensor based on microelectromechanical systems accelerometer and gyroscope during the process of the angle measurement are considered, and a new method is proposed to compensate them, using data fusion of sensors. The tilt sensor has a constant delay and low bandwidth. In the gyroscope, drift phenomena makes some difficulties in the angle measurement. A model for the sensory system is developed and a modified Kalman filter is proposed to compensate the delay of the tilt sensor using the data fusion technique. A complementary filter is designed to overcome the problem of low bandwidth in the tilt sensor. The overall proposed sensor fusion consists of a complementary filter as a prefilter and a modified Kalman filter as a postfilter to solve the problems of bandwidth and delay, respectively. The experimental tests are performed to evaluate the effectiveness of the proposed sensor fusion scheme. In these experiments, the accuracy in the tilt measurement is improved from 10° to 0.9° and 15° to 0.6° in low-frequency tests, and from 18° to 0.8° in higher frequency test. In addition, the bandwidth of the tilt measurement is enhanced from 0.16 (bandwidth of tilt sensor) to 6 Hz. The results show proper performance of the proposed approach in the line of delay compensation and increment of the bandwidth.


title: Fatal haemorrhagic pulmonary oedema and associated angioedema after the ingestion of rofecoxib

abstract: Angioedema is a recognised side effect of rofecoxib, a cyclo-oxygenase-2 (COX-2) inhibitor. But death resulting from a haemorrhagic pulmonary oedema after its ingestion has not been recorded. The case of a 60 year old man who died from haemorrhagic pulmonary oedema in the presence of angioedema after the ingestion of two doses of 12.5 mg of rofecoxib is reported.


title: Field Weakening in Buried Permanent Magnet AC Motor Drives

abstract: The usual uncoupled d - q model of salient pole synchronous machines (Park's model) may be insufficient for accurate modeling of buried magnet permanent magnet machines. The addition of a nonbilateral coupling between the direct and quadrature axis equivalent circuits is shown to improve the steady-state model greatly. The cross coupling reactance has important implications in improving operation in the constant horsepower mode. In particular, it is demonstrated that the cross coupling term acts to reduce the effective internal voltage so that some field weakening can be achieved. The results should be useful in permanent magnet machine design for variable speed drive applications.


title: Systematic Review of Radiofrequency Ablation and Pulsed Radiofrequency for Management of Cervicogenic Headache

abstract: BACKGROUND ::: Cervicogenic headache is a secondary headache that has a source in the upper cervical spine. There is a small but growing body of evidence to establish effectiveness of radiofrequency (RF) neurotomy, and the pulsed RF (PRF) procedure for management of cervicogenic headache. ::: ::: ::: OBJECTIVE ::: To investigate the clinical utility of RF neurotomy, and PRF ablation for the management of cervicogenic headache. ::: ::: ::: STUDY DESIGN ::: Systematic review. ::: ::: ::: METHODS ::: The review included relevant literature identified through searches of PubMed, Cochrane, Clinical trials, U.S. National Guideline Clearinghouse and EMBASE from 1960 to January 2014.The quality assessment and clinical relevance criteria utilized were the Cochrane Musculoskeletal Review Group criteria for randomized control trials and the Newcastle-Ottawa Scale criteria for observational studies. The level of evidence was classified as good, fair, and poor based on the quality of evidence. ::: ::: ::: OUTCOMES MEASURED ::: The primary outcome measures were reduction in pain scores and improvement in quality of life. ::: ::: ::: RESULTS ::: The primary outcome measures were headache relief and improved quality of life. Twenty five studies were identified for full text review of these, 9 studies met inclusion criteria. There were 5 non-randomized, among them 4/5 were of moderate quality, 3/5 showed RF ablation and 1/5 showed PRF as an effective intervention for cervicogenic headache. There were 4 randomized trials among them 2/4 were of high quality, 3/4 investigated RF ablation as an intervention for CHA, 1/4 investigated PRF ablation as an intervention for CHA and none of the randomized studies showed strong evidence for RF and PRF ablation as an effective intervention for CHA. ::: ::: ::: LIMITATIONS ::: In the selected studies there were inconsistencies between randomized trials, flaws in trial design, and gaps in the chain of evidence. ::: ::: ::: CONCLUSION ::: There is limited evidence to support RF ablation for management of CHA as there are no high quality RCTs and/ or multiple consistent non-RCTs without methodological flaws. There is poor evidence to support PRF for CHA as there are no high quality RCTs or Non-RCTs.


title: A precision variable-supply CMOS comparator

abstract: Several new techniques are presented for the design of precision CMOS voltage comparator circuits which operate over a wide range of supply voltages. Since most monolithic A/D converter systems contain an on-chip voltage reference, techniques have been developed to replicate the reference voltage in order to provide stable supply-independent DC bias voltages, and controlled internal voltage swings for the comparator. These techniques are necessary in order to eliminate harmful bootstrapping effects which can potentially occur in all AC coupled MOS analog circuits. An actively controlled biasing scheme has been developed to allow for differentially autozeroing the comparator for applications in differential A/D converter systems. A general approach for selecting the gain in AC-coupled gain stages is also presented. The comparator circuit has been implemented in a standard metal-gate CMOS process. The measured comparator resolution is less than 1 mV, and the allowable supply voltages range from 3.5 to 10 V.


title: A hybrid latent variable neural network model for item recommendation

abstract: Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating additional information, such as item or user descriptions, into collaborative filtering can address the cold-start problem. In this paper, we present a neural network model with latent input variables (latent neural network or LNN) as a hybrid collaborative filtering technique that addresses the cold-start problem. LNN outperforms a broad selection of content-based filters (which make recommendations based on item descriptions) and other hybrid approaches while maintaining the accuracy of state-of-the-art collaborative filtering techniques.


title: Is Customer Satisfaction an Indicator of Customer Loyalty

abstract: Customer satisfaction and loyalty is a well known and established concept in several areas like marketing, consumer research, economic psychology, welfare-economics, and economics. And has long been a topic of high interest in both academia and practice. The aim of the study was to investigate whether customer satisfaction is an indicator of customer loyalty. The findings of the study supported the contention that strong relationship exist between customer satisfaction and loyalty. However, customer satisfaction alone cannot achieve the objective of creating a loyal customer base. Some researchers also argued, that customer satisfaction and loyalty are not directly correlated, particularly in competitive business environments because there is a big difference between satisfaction, which is a passive customer condition, and loyalty, which is an active or proactive relationship with the organization.


title: Pairwise Comparisons Simplified

abstract: This study examines the notion of generators of a pairwise comparisons matrix. Such approach decreases the number of pairwise comparisons from $n\cdot (n-1)$ to $n-1$. An algorithm of reconstructing of the PC matrix from its set of generators is presented.


title: Partitioning hard clustering algorithms based on multiple dissimilarity matrices

abstract: This paper introduces hard clustering algorithms that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. These matrices have been generated using different sets of variables and dissimilarity functions. These methods are designed to furnish a partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These relevance weights change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Experiments with data sets (synthetic and from UCI machine learning repository) described by real-valued variables as well as with time trajectory data sets show the usefulness of the proposed algorithms.


title: Affective Regulation of Stereotype Activation: It’s the (Accessible) Thought That Counts

abstract: Prior research has found that positive affect, compared to negative affect, increases stereotype activation. In four experiments the authors explore whether the link between affect and stereotype activation depends on the relative accessibility of stereotype-relevant thoughts and response tendencies. As well as manipulating mood, the authors measured or manipulated the accessibility of egalitarian response tendencies (Experiments 1 and 2) and counterstereotypic thoughts (Experiments 2 through 4). In the absence of such response tendencies and thoughts, people in positive moods displayed greater stereotype activation—consistent with past research. By contrast, in the presence of accessible egalitarian response tendencies or counterstereotypic thoughts, people in positive moods exhibited less stereotype activation than those in negative moods. Implications of these results for existing affect—cognition models are discussed.


title: An analytical method for diseases prediction using machine learning techniques

abstract: Abstract The use of medical datasets has attracted the attention of researchers worldwide. Data mining techniques have been widely used in developing decision support systems for diseases prediction through a set of medical datasets. In this paper, we propose a new knowledge-based system for diseases prediction using clustering, noise removal, and prediction techniques. We use Classification and Regression Trees (CART) to generate the fuzzy rules to be used in the knowledge-based system. We test our proposed method on several public medical datasets. Results on Pima Indian Diabetes, Mesothelioma, WDBC, StatLog, Cleveland and Parkinson’s telemonitoring datasets show that proposed method remarkably improves the diseases prediction accuracy. The results showed that the combination of fuzzy rule-based, CART with noise removal and clustering techniques can be effective in diseases prediction from real-world medical datasets. The knowledge-based system can assist medical practitioners in the healthcare practice as a clinical analytical method.


title: Visual Feature Integration and the Temporal Correlation Hypothesis

abstract: The mammalian visual system is endowed with a nearly infinite capacity for the recognition of patterns and objects. To have acquired this capability the visual system must have solved what is a fundamentally combinatorial prob lem. Any given image consists of a collection of features, consisting of local contrast borders of luminance and wavelength, distributed across the visual field. For one to detect and recognize an object within a scene, the features comprising the object must be identified and segregated from those comprising other objects. This problem is inherently difficult to solve because of the combinatorial nature of visual images. To appreciate this point, consider a simple local feature such as a small vertically oriented line segment placed within a fixed location of the visual field. When combined with other line segments, this feature can form a nearly infinite number of geometrical objects. Any one of these objects may coexist with an equally large number of other


title: Base-victim compression: an opportunistic cache compression architecture

abstract: The memory wall has motivated many enhancements to cache management policies aimed at reducing misses. Cache compression has been proposed to increase effective cache capacity, which potentially reduces capacity and conflict misses. However, complexity in cache compression implementations could increase cache power and access latency. On the other hand, advanced cache replacement mechanisms use heuristics to reduce misses, leading to significant performance gains. Both cache compression and replacement policies should collaborate to improve performance. In this paper, we demonstrate that cache compression and replacement policies can interact negatively. In many workloads, performance gains from replacement policies are lost due to the need to alter the replacement policy to accommodate compression. This leads to sub-optimal replacement policies that could lose performance compared to an uncompressed cache. We introduce a novel, opportunistic cache compression mechanism, Base-Victim, based on an efficient cache design. Our compression architecture improves performance on top of advanced cache replacement policies, and guarantees a hit rate at least as high as that of an uncompressed cache. For cache-sensitive applications, Base-Victim achieves an average 7.3% performance gain for single-threaded workloads, and 8.7% gain for four-thread multi-program workload mixes.


title: Instance-Based Learning Algorithms

abstract: Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.


title: MEMS Switched Tunable Inductors

abstract: This paper presents a new implementation of integrated tunable inductors using mutual inductances activated by micromechanical switches. To achieve a large tuning range and a high quality factor, silver was used as the structural material, and silicon was selectively removed from the backside of the substrate. Using this method, a maximum tuning of 47% at 6 GHz is achieved for a 1.1 nH silver inductor fabricated on a low-loss polymer membrane. The effect of the quality factor on the tuning characteristic of the inductor is investigated by comparing the measured result of identical inductors fabricated on various substrates. To maintain the quality factor of the silver inductor, the device was encapsulated using a low-cost wafer-level polymer packaging technique.


title: Visualizing patterns of student engagement and performance in MOOCs

abstract: In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.


title: BotViz: A memory forensic-based botnet detection and visualization approach

abstract: Nowadays, there are many serious cyber security threats such as viruses, worms and trojans but without a doubt botnets are one of the largest threats. Although there are numerous ways to discover botnets and mitigate their effects, most methods have problems effecting detection, due to their evasive characteristics. Also, the majority of previous research uses only one data source (e.g. network traffic), which makes the botnet detection process very difficult over a network. This paper proposes a detection and visualization system, BotViz, to visualize botnets by using memory forensics analysis and a new domain generation algorithm detector. BotViz utilizes machine learning techniques to detect anomalous function hooking behaviors. We established a live Zeus botnet to evaluate the efficiency of the BotViz.


title: Analytical and FEA based analysis of homopolar poly-phase transverse flux machine

abstract: This paper proposes a novel topology for a transverse flux machine (TFM) to address the three major issues associated with conventional TFM, namely: multi-stack poly-phase TFM, low power factor and difficulty in construction. The proposed machine has homopolar main field flux set by the rotor and a hetero-polar winding arrangement on the stator. Design equations and equivalent magnetic circuit model of the proposed topology are presented in this paper. The machine is analyzed both by using equivalent magnetic circuit approach and three dimensional finite element analysis (FEA). Results obtained by both the methods are compared.


title: Control-Flow Integrity Principles , Implementations , and Applications

abstract: Current software attacks often build on exploits that subvert machine-code execution. The enforcement of a basic safety property, Control-Flow Integrity (CFI), can prevent such attacks from arbitrarily controlling program behavior. CFI enforcement is simple, and its guarantees can be established formally even with respect to powerful adversaries. Moreover, CFI enforcement is practical: it is compatible with existing software and can be done efficiently using software rewriting in commodity systems. Finally, CFI provides a useful foundation for enforcing further security policies, as we demonstrate with efficient software implementations of a protected shadow call stack and of access control for memory regions.


title: Mobile shopping behaviour: insights into attitudes, shopping process involvement and location

abstract: Purpose – Although there is evidence that adoption of mobile shopping has been slow, the increased functionality offered by smart phones offers significant potential for the development of marketing and retailing through the mobile channel. The purpose of this paper is to add to knowledge on consumer shopping behaviour through mobile devices by exploring attitudes to the use of mobiles in shopping, the use of mobile phones at different stages in the consumer decision-making process, the impact of involvement on the mobile consumer decision-making process and mobile shopping location. Design/methodology/approach – An online panel survey was conducted with a UK panel of nationally representative consumers. The survey collected data in respect of the following products that were ranked on level of involvement on the basis of the importance and effort consumers' associated with their purchase: bread (lowest), washing powder, DVD, footwear, phone and TV (highest). Findings – Use of mobiles for shopping is sign...


The science of emotional intelligence


title: Neuropathology of stress

abstract: Environmental challenges are part of daily life for any individual. In fact, stress appears to be increasingly present in our modern, and demanding, industrialized society. Virtually every aspect of our body and brain can be influenced by stress and although its effects are partly mediated by powerful corticosteroid hormones that target the nervous system, relatively little is known about when, and how, the effects of stress shift from being beneficial and protective to becoming deleterious. Decades of stress research have provided valuable insights into whether stress can directly induce dysfunction and/or pathological alterations, which elements of stress exposure are responsible, and which structural substrates are involved. Using a broad definition of pathology, we here review the “neuropathology of stress” and focus on structural consequences of stress exposure for different regions of the rodent, primate and human brain. We discuss cytoarchitectural, neuropathological and structural plasticity measures as well as more recent neuroimaging techniques that allow direct monitoring of the spatiotemporal effects of stress and the role of different CNS structures in the regulation of the hypothalamic–pituitary–adrenal axis in human brain. We focus on the hypothalamus, hippocampus, amygdala, nucleus accumbens, prefrontal and orbitofrontal cortex, key brain regions that not only modulate emotions and cognition but also the response to stress itself, and discuss disorders like depression, post-traumatic stress disorder, Cushing syndrome and dementia.


title: OntoSOC: Sociocultural Knowledge Ontology

abstract: This paper presents a sociocultural knowledge ontology (OntoSOC) modeling approach. OntoSOC modeling approach is based on Engestrom Human Activity Theory (HAT). That Theory allowed us to identify fundamental concepts and relationships between them. The top-down precess has been used to define differents sub-concepts. The modeled vocabulary permits us to organise data, to facilitate information retrieval by introducing a semantic layer in social web platform architecture, we project to implement. This platform can be considered as a collective memory and Participative and Distributed Information System (PDIS) which will allow Cameroonian communities to share an co-construct knowledge on permanent organized activities.


title: Context-Based IDE Command Recommender System

abstract: Software developer's working process could benefit from the support of an active help system that is able to recommend applicable and useful integrated development environment (IDE) commands. While previous work focused on prediction methods that can identify what developers will eventually discover autonomously, and without taking into account the characteristics of their working tasks, we want to build a system that recommends only commands that lead to better work performance. Since we cannot expect that developers are willing to invest a significant effort to use our recommender system (RS), we are developing a context-aware multi-criteria RS based on implicit feedback. We already created and evaluated context and user models. We also acquired a data set with more than 100,000 command executions. Currently, we are developing RS algorithm for predicting the scores of performance and effort expectancy and developer's intention to use a specific command. We are also developing a user interface, that has to be persuasive, effective, and efficient. To date, a user interface for IDE command RS has not been developed.


title: Eating moment recognition using heart rate responses

abstract: There are many studies for recognizing eating moments using wide types of modality (e.g. arm motion). However, they are needed to be improved for both accuracy and robustness for practical use in daily life. In this paper, we propose a novel recognition method using bimodal heart rate responses caused by eating. Our method combines (i) short-term and (ii) long-term features of heart rate changes. The proposed method was evaluated for recognizing eating moment with the free-environment dataset (9 participants, 604 days), and achieved 98.6% accuracy and 56.9% F-score. The proposed features related to ingestion and digestion contribute to robust eating moment recognition.


title: A 33W GaN HEMT Doherty amplifier with 55% drain efficiency for 2.6GHz base stations

abstract: A 33W average output power Doherty power amplifier (PA) for 2.6GHz band was developed using compact package advanced GaN HEMTs which have Cds reduced 20%. A small-footprint Doherty network was successfully designed with plain small signal analysis, and the method was very practical. The Doherty PA exhibited a saturation output power of 52.5dBm (178W) and a saturated drain efficiency of 65.6%. The Doherty PA also achieved a drain efficiency of 55%, an ACLR of −52.8dBc and a power gain of 13.8dB at the average output power of 45.2dBm (33W) with 4-carrier W-CDMA signal at 2.6GHz using commercially available digital pre-distortion system. These excellent performances show good suitability for 2.6GHz band basestations (BTS), for example, W-CDMA, LTE and WiMAX, and small-sized circuit structure contributes to realize compact remote radio head type of BTS.


Twitter Topic Modeling for Breaking News Detection


title: Open issues in genetic programming

abstract: It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP. We hope this overview will stimulate debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful problem solving algorithms.


title: A proposal on wireless power transfer for medical implantable applications based on reviews

abstract: Since wireless power transfer technology (WPT) gains its popularity, broad range of application and research are performed in the field of medical implantable applications. In this paper, we review the technical status of WPT system applied to medical implant devices. As a practical application of this technology, the current achievements and challenges of wireless capsule endoscopy are reviewed and analyzed, in view of its design, functional requirement and related issues in WPT. And WPT application to artificial heart is also discussed. Based upon the analysis, we propose a research directions and WPT consideration in designing a WPT system for implantable medical applications.


title: Dual-band unidirectional cross dipole antenna

abstract: This paper presents a dual-band unidirectional antenna based on a straight dipole shunted with a meander line dipole antenna. This antenna is fed with a wide band Balun and equipped with reflectors to generate unidirectional patterns. The low and high frequency reflectors are plane and box reflectors, respectively. The antenna is well matched at the designed frequencies of 1 and 2 GHz. The unidirectional patterns are obtained at both frequencies with gain higher than 5.7 dBi. This antenna is used for illuminating microwave signal to an object under-test in a dual-band reflectometer.


title: Understanding bag-of-words model: a statistical framework

abstract: The bag-of-words model is one of the most popular representation methods for object categorization. The key idea is to quantize each extracted key point into one of visual words, and then represent each image by a histogram of the visual words. For this purpose, a clustering algorithm (e.g., K-means), is generally used for generating the visual words. Although a number of studies have shown encouraging results of the bag-of-words representation for object categorization, theoretical studies on properties of the bag-of-words model is almost untouched, possibly due to the difficulty introduced by using a heuristic clustering process. In this paper, we present a statistical framework which generalizes the bag-of-words representation. In this framework, the visual words are generated by a statistical process rather than using a clustering algorithm, while the empirical performance is competitive to clustering-based method. A theoretical analysis based on statistical consistency is presented for the proposed framework. Moreover, based on the framework we developed two algorithms which do not rely on clustering, while achieving competitive performance in object categorization when compared to clustering-based bag-of-words representations.


title: Personality Trait Change in Adulthood

abstract: Recent longitudinal and cross-sectional aging research has shown that personality traits continue to change in adulthood. In this article, we review the evidence for mean-level change in personality traits, as well as for individual differences in change across the life span. In terms of mean-level change, people show increased self-confidence, warmth, self-control, and emotional stability with age. These changes predominate in young adulthood (age 20–40). Moreover, mean-level change in personality traits occurs in middle and old age, showing that personality traits can change at any age. In terms of individual differences in personality change, people demonstrate unique patterns of development at all stages of the life course, and these patterns appear to be the result of specific life experiences that pertain to a person's stage of life.


title: Optimal Portfolio and Consumption in Modified Black-Scholes Model

abstract: The optimal stochastic control is a hot topic among recent problems in economics and finance. In fact, many economical and financial problems present the necessity of taking decisions based on the uncertainty of random nature. This paper modifies the classical Black-Scholes model by taking the consumption into consideration. By splitting the initial endowment into two parts, we respectively find the optimal consumption and optimal portfolio. We also analyze how to get the optimal solution of the combined portfolio and consumption problem in the model.


title: Postpartum depression in adolescent mothers.

abstract: BACKGROUND ::: Postpartum depression has been extensively studied in adults but is less understood in adolescent mothers, despite a prevalence that is double that observed in adult mothers. The purpose of this review was to describe the epidemiology, risk factors, treatment and prognosis for adolescents with postpartum depression. We also sought to identify limitations of the available literature and propose areas for future study targeting postpartum depression in this vulnerable population. ::: ::: ::: METHODS ::: A Medline literature search was conducted for articles published between 1996 and 2015. We identified relevant studies by combining the indexed search terms 'pregnancy in adolescence or teenage pregnancy' and 'depression or postpartum depression'. Additional studies were identified from references of selected articles. We limited our search results to adolescents (18 years or younger) and English language publications. Case studies/series and editorials were excluded. ::: ::: ::: RESULTS ::: The Medline database search identified 134 articles of which 57 met inclusion criteria. Ten additional articles were identified from reference lists yielding a total of n = 67 articles for review. Among the articles selected, 10 described epidemiology, 27 identified risk factors, nine measured long-term outcome and 21 proposed treatment strategies for postpartum depression in adolescent mothers. ::: ::: ::: CONCLUSIONS ::: There is limited literature addressing adolescent postpartum depression, but there has been a significant growth of interest in recent years. There is a need for more randomized control trials to establish gold standards for assessing postpartum depression in adolescent mothers and standards for treatment in these patients.


title: Semi-data-driven network coarsening

abstract: Network coarsening refers to a new class of graph 'zoom-out' operations by grouping similar nodes and edges together so that a smaller equivalent representation of the graph can be obtained for big network analysis. Existing network coarsening methods consider that network structures are static and thus cannot handle dynamic networks. On the other hand, data-driven approaches can infer dynamic network structures by using network information spreading data. However, existing data-driven approaches neglect static network structures that are potentially useful for inferring big networks. In this paper, we present a new semi-data-driven network coarsening model to learn coarsened networks by embedding both static network structure data and dynamic network information spreading data. We prove that the learning model is convex and the Accelerated Proximal Gradient algorithm is adapted to achieve the global optima. Experiments on both synthetic and real-world data sets demonstrate the quality and effectiveness of the proposed method.


title: Modification of Ginseng Flavors by Bitter Compounds Found in Chocolate and Coffee

abstract: Ginseng is not widely accepted by U.S. consumers due to its unfamiliar flavors, despite its numerous health benefits. Previous studies have suggested that the bitter compounds in chocolate and coffee may mask the off-flavors of ginseng. The objectives of this study were to: (1) profile sensory characteristics of ginseng extract solution, caffeine solution, cyclo (L-Pro-L-Val) solution, theobromine solution, and 2 model solutions simulating chocolate bitterness; and (2) determine the changes in the sensory characteristics of ginseng extract solution by the addition of the bitter compounds found in chocolate and coffee. Thirteen solutions were prepared in concentrations similar to the levels of the bitter compounds found in coffee and chocolate products. Twelve panelists participated in a descriptive analysis panel which included time-intensity ratings. Ginseng extract was characterized as sweeter, starchier, and more green tea than the other sample solutions. Those characteristics of ginseng extract were effectively modified by the addition of caffeine, cyclo (L-Pro-L-Val), and 2 model solutions. A model solution simulating dark chocolate bitterness was the least influenced in intensities of bitterness by the addition of ginseng extract. Results from time-intensity ratings show that the addition of ginseng extract increased duration time in certain bitterness of the 2 model solutions. Bitter compounds found in dark chocolate could be proposed to effectively mask the unique flavors of ginseng. Future studies blending aroma compounds of chocolate and coffee into such model solutions may be conducted to investigate the influence on the perception of the unique flavors through the congruent flavors.


Bone Fracture Detection Algorithms Based on Image Processing- A Survey


title: LTE and IEEE 802.11p for vehicular networking: a performance evaluation

abstract: Various wireless communication systems exist, which enable a wide range of applications and use cases in the vehicular environment. These applications can be grouped into three types, namely, road safety, traffic efficiency, and infotainment, each with its own set of functional and performance requirements. In pursuance of assisting drivers to travel safely and comfortably, several of these requirements have to be met simultaneously. While the coexistence of multiple radio access technologies brings immense opportunities towards meeting most of the vehicular networking application requirements, it is equally important and challenging to identify the strength and weaknesses of each technology and understand which technology is more suitable for the given networking scenario. In this paper, we evaluate two of the most viable communication standards, Institute of Electrical and Electronics Engineers (IEEE) 802.11p and long-term evolution (LTE) by 3rd Generation Partnership Project for vehicular networking. A detailed performance evaluation study of the standards is given for a variety of parameter settings such as beacon transmission frequency, vehicle density, and vehicle average speed. Both standards are compared in terms of delay, reliability, scalability, and mobility support in the context of various application requirements. Furthermore, through extensive simulation-based study, we validated the effectiveness of both standards to handle different application requirements and share insight for further research directions. The results indicate that IEEE 802.11p offers acceptable performance for sparse network topologies with limited mobility support. On the other hand, LTE meets most of the application requirements in terms of reliability, scalability, and mobility support; however, it is challenging to obtain stringent delay requirements in the presence of higher cellular network traffic load.


title: An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization

abstract: This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an l1-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of learning distance metric serves to regularize the complexity of the distance model especially in the "less example number p and high dimension d" setting. Theoretically, by analogy to the covariance estimation problem, we find the proposed distance learning algorithm has a consistent result at rate O (√m2 log d)/n) to the target distance matrix with at most m nonzeros per row. Moreover, from the implementation perspective, this l1-penalized log-determinant formulation can be efficiently optimized in a block coordinate descent fashion which is much faster than the standard semi-definite programming which has been widely adopted in many other advanced distance learning algorithms. We compare this algorithm with other state-of-the-art ones on various datasets and competitive results are obtained.


title: AND/OR branch-and-bound search for combinatorial optimization in graphical models

abstract: This is the first of two papers presenting and evaluating the power of a new framework for combinatorial optimization in graphical models, based on AND/OR search spaces. We introduce a new generation of depth-first Branch-and-Bound algorithms that explore the AND/OR search tree using static and dynamic variable orderings. The virtue of the AND/OR representation of the search space is that its size may be far smaller than that of a traditional OR representation, which can translate into significant time savings for search algorithms. The focus of this paper is on linear space search which explores the AND/OR search tree. In the second paper we explore memory intensive AND/OR search algorithms. In conjunction with the AND/OR search space we investigate the power of the mini-bucket heuristics in both static and dynamic setups. We focus on two most common optimization problems in graphical models: finding the Most Probable Explanation in Bayesian networks and solving Weighted CSPs. In extensive empirical evaluations we demonstrate that the new AND/OR Branch-and-Bound approach improves considerably over the traditional OR search strategy and show how various variable ordering schemes impact the performance of the AND/OR search scheme.


title: 15.4 A 0.8V 10b 80kS/s SAR ADC with duty-cycled reference generation

abstract: Autonomous wireless sensor nodes need low-power low-speed ADCs to digitize the sensed signal. State-of-art SAR ADCs can accomplish this goal with high power-efficiency (<10fJ/conversion-step) [1-4]. The reference voltage design is critical for the ADC performance to obtain good PSRR, low line-sensitivity and a stable supply-independent full-scale range. However, solutions for efficient reference voltage generators (RVGs) are typically ignored in low-power ADC publications. In reality, due to the low power supply (usually sub-1 V) and limited available power (nW-range), the RVG is a challenge within the sensor system. In this work, a 2.4fJ/conversion-step SAR ADC with integrated reference is implemented. The 0.62V CMOS RVG consumes 25nW. To further reduce RVG power, it can be duty-cycled down to 10% with no loss in ADC performance. Additionally, the ADC uses a bidirectional dynamic comparator to improve the power efficiency even more.


title: Deep learning using robust interdependent codes

abstract: We investigate a simple yet effective method to introduce inhibitory and excitatory interactions between units in the layers of a deep neural network classifier. The method is based on the greedy layer-wise procedure of deep learning algorithms and extends the denoising autoencoder (Vincent et al., 2008) by adding asymmetric lateral connections between its hidden coding units, in a manner that is much simpler and computationally more efficient than previously proposed approaches. We present experiments on two character recognition problems which show for the first time that lateral connections can significantly improve the classification performance of deep networks.


title: Cognition in Friedreich's ataxia: a behavioral and multimodal imaging study

abstract: Objective ::: ::: Friedreich's ataxia (FRDA) is a spinocerebellar degenerative disorder, in which cognitive deficits are sparsely explored. In this behavioral and multimodal magnetic resonance imaging (MRI) study, we investigated the neurocognitive profile and cortico-cerebellar dysfunctions underlying executive functioning in individuals with FRDA. ::: ::: ::: ::: Methods ::: ::: 22 FRDA patients and 22 controls were clinically and neuropsychologically examined. Fifteen of each underwent structural and functional MRI using a verbal-fluency task with phonemic and semantic conditions. Gray (GM) and white matter (WM) alterations were assessed by means of voxel-based morphometry and diffusion-tensor imaging. ::: ::: ::: ::: Results ::: ::: The neuropsychological profile demonstrated deficits in verbal fluency, working memory and social cognition. Functional MRI data showed most pronounced group-differences in phonemic fluency with patients exhibiting enhanced activity in the cerebellum (VI, Crus I), fronto-insular, premotor and temporo-occipital regions. The semantic condition only revealed reduced activity in the anterior cerebellum; for overt speech, we found increased activity in the motor cortex. Functional connectivity-analysis showed higher co-activation within cerebellar and cortical regions, respectively, and impaired interregional coupling between the cerebellum and fronto-insular cortex for phonemic processing, which was also related to poorer task performance. GM reduction in FRDA was mainly found in lobule VI, whereas WM degeneration was more pronounced including brainstem, cerebellum, and cortex. Decreased cerebellar GM was associated with enhanced activity in the fronto-insular cortex, while loss of WM integrity may translate cortico-cerebellar pathway disruptions. ::: ::: ::: ::: Interpretation ::: ::: The pattern of increased neural response with both cerebellar and cortical involvement underlying executive functioning indicates functional reorganization driven by disease-related structural damage in FRDA.


title: N-Gram-Based Text Categorization

abstract: Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors in email, and character recognition errors in documents that come through OCR. Text categorization must work reliably on all input, and thus must tolerate some level of these kinds of problems. We describe here an N-gram-based approach to text categorization that is tolerant of textual errors. The system is small, fast and robust. This system worked very well for language classification, achieving in one test a 99.8% correct classification rate on Usenet newsgroup articles written in different languages. The system also worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject, achieving as high as an 80% correct classification rate. There are also several obvious directions for improving the system`s classification performance in those cases where it did not do as well. The system is based on calculating and comparing profiles of N-gram frequencies. First, we use the system to compute profiles on training set data that represent the variousmore » categories, e.g., language samples or newsgroup content samples. Then the system computes a profile for a particular document that is to be classified. Finally, the system computes a distance measure between the document`s profile and each of the category profiles. The system selects the category whose profile has the smallest distance to the document`s profile. The profiles involved are quite small, typically 10K bytes for a category training set, and less than 4K bytes for an individual document. Using N-gram frequency profiles provides a simple and reliable way to categorize documents in a wide range of classification tasks.« less


title: Lexical feature based phishing URL detection using online learning

abstract: Phishing is a form of cybercrime where spammed emails and fraudulent websites entice victims to provide sensitive information to the phishers. The acquired sensitive information is subsequently used to steal identities or gain access to money. This paper explores the possibility of utilizing confidence weighted classification combined with content based phishing URL detection to produce a dynamic and extensible system for detection of present and emerging types of phishing domains. Our system is capable of detecting emerging threats as they appear and subsequently can provide increased protection against zero hour threats unlike traditional blacklisting techniques which function reactively.


title: Experimental results for artificial intelligence-based self-organized 5G networks

abstract: Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective.


title: Access to and utilisation of health services for the poor in Uganda: a systematic review of available evidence.

abstract: Inequalities in the burden of disease and access to health care is a prominent concern in Uganda and other sub-Saharan African countries. This is a systematic review of socio-economic differences in morbidity and access to health care in Uganda. It includes published studies from electronic databases and official reports from surveys done by government, bilateral and multilateral agencies and universities. The outcome measures studied were: the distribution of HIV/AIDS; maternal and child morbidity; and access to and utilisation of health services for people belonging to different socio-economic and vulnerability groups. Forty-eight of 678 identified studies met our inclusion criteria. Results indicate that the poor and vulnerable experience a greater burden of disease but have lower access to health services than the less poor. Barriers to access arise from both the service providers and the consumers. Distance to service points, perceived quality of care and availability of drugs are key determinants of utilisation. Other barriers are perceived lack of skilled staff in public facilities, late referrals, health worker attitude, costs of care and lack of knowledge. Longitudinal and controlled studies are needed to see if strategies to improve access to services reach the poor.


title: GestureWrist and GesturePad: unobtrusive wearable interaction devices

abstract: In this paper we introduce two input devices for wearable computers, called GestureWrist and GesturePad. Both devices allow users to interact with wearable or nearby computers by using gesture-based commands. Both are designed to be as unobtrusive as possible, so they can be used under various social contexts. The first device, called GestureWrist, is a wristband-type input device that recognizes hand gestures and forearm movements. Unlike DataGloves or other hand gesture-input devices, all sensing elements are embedded in a normal wristband. The second device, called GesturePad, is a sensing module that can be attached on the inside of clothes, and users can interact with this module from the outside. It transforms conventional clothes into an interactive device without changing their appearance.


title: Discriminative Label Consistent Domain Adaptation

abstract: Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target. We present in this paper a novel unsupervised DA method for cross-domain visual recognition which simultaneously optimizes the three terms of a theoretically established error bound. Specifically, the proposed DA method iteratively searches a latent shared feature subspace where not only the divergence of data distributions between the source domain and the target domain is decreased as most state-of-the-art DA methods do, but also the inter-class distances are increased to facilitate discriminative learning. Moreover, the proposed DA method sparsely regresses class labels from the features achieved in the shared subspace while minimizing the prediction errors on the source data and ensuring label consistency between source and target. Data outliers are also accounted for to further avoid negative knowledge transfer. Comprehensive experiments and in-depth analysis verify the effectiveness of the proposed DA method which consistently outperforms the state-of-the-art DA methods on standard DA benchmarks, i.e., 12 cross-domain image classification tasks.


title: An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace

abstract: The rise of e-commerce has unlocked practical applications for algorithmic pricing (also called dynamic pricing algorithms), where sellers set prices using computer algorithms. Travel websites and large, well known e-retailers have already adopted algorithmic pricing strategies, but the tools and techniques are now available to small-scale sellers as well. ::: ::: While algorithmic pricing can make merchants more competitive, it also creates new challenges. Examples have emerged of cases where competing pieces of algorithmic pricing software interacted in unexpected ways and produced unpredictable prices, as well as cases where algorithms were intentionally designed to implement price fixing. Unfortunately, the public currently lack comprehensive knowledge about the prevalence and behavior of algorithmic pricing algorithms in-the-wild. ::: ::: In this study, we develop a methodology for detecting algorithmic pricing, and use it empirically to analyze their prevalence and behavior on Amazon Marketplace. We gather four months of data covering all merchants selling any of 1,641 best-seller products. Using this dataset, we are able to uncover the algorithmic pricing strategies adopted by over 500 sellers. We explore the characteristics of these sellers and characterize the impact of these strategies on the dynamics of the marketplace.


title: Applying WinWin to quality requirements: a case study

abstract: This paper describes the application of the WinWin paradigm to identify and resolve conflicts in a series of real-client, student-developer digital library projects. The paper is based on a case study of the statistical analysis of 15 projects and an in-depth analysis of one representative project. These analyses focus on the conflict resolution process, stakeholders' roles and their relationships to quality artifacts, and tool effectiveness. We show that stakeholders tend to accept satisfactory rather than optimal resolutions. Users and customers are more proactive in stating win conditions, whereas developers are more active in working toward resolutions. Further, we suggest that knowledge-based automated aids have potential to significantly enhance process effectiveness and efficiency. Finally, we conclude that such processes and tools have theoretical and practical implications in the quest for better software requirements elicitation.


title: Disc Golf, a Growing Sport Description and Epidemiology of Injuries

abstract: Background Disc golf is a sport played much like traditional golf, but rather than using a ball and club, players throw flying discs with various throwing motions. It has been played by an estimated 8 to 12 million people in the United States. Like all sports, injuries sustained while playing disc golf are not uncommon. Although formalized in the 1970s, it has grown at a rapid pace; however, disc golf–related injuries have yet to be described in the medical literature. Purpose To describe the most common injuries incurred by disc golf players while comparing the different types of throwing styles. Study Design Descriptive epidemiology study. Methods The data in this study were collected from 883 disc golf players who responded to an online survey collected over a 1-month period. Respondents answered 49 questions related to demographics, experience, style of play, and injury details. Using a chi-square analysis, common injuries sustained in players using backhand and forehand throwing styles were compared. Results More than 81% of respondents stated that they had sustained an injury playing disc golf, including injuries to the elbow (n = 325), shoulder (n = 305), back (n = 218), and knee (n = 199). The injuries were most commonly described as a muscle strain (n = 241), sprain (n = 162), and tendinitis (n = 145). The type of throw primarily used by players varied, with 86.2% using backhand, 12.7% using forehand, and 1.1% using an overhead throw. Players using a forehand throw were more likely to sustain an elbow injury (P = .014). Many players (n = 115) stated they had undergone surgery due to a disc golf–related injury, with the most common surgeries including meniscal, shoulder, spine, and foot/ankle surgeries. Conclusion The majority of surveyed disc golfers sustained at least 1 injury while playing disc golf, with many requiring surgery. The types of injuries sustained by players varied by the types of throw primarily used. As the sport of disc golf continues to expand, health professionals should be aware of injuries sustained, with future studies focusing on injury prevention and education strategies.


title: Theoretical comparison of testing methods

abstract: Comparison of software testing methods is meaningful only if sound theory relates the properties compared to actual software quality. Existing comparisons typically use anecdotal foundations with no necessary relationship to quality, comparing methods on the basis of technical terms the methods themselves define. In the most seriously flawed work, one method whose efficacy is unknown is used as a standard for judging other methods! Random testing, as a method that can be related to quality (in both the conventional sense of statistical reliability, and the more stringent sense of software assurance), offers the opportunity for valid comparison.


title: A cloud based health insurance plan recommendation system

abstract: The recent concept of "Health Insurance Marketplace" introduced to facilitate the purchase of health insurance by comparing different insurance plans in terms of price, coverage benefits, and quality designates a key role to the health insurance providers. Currently, the web based tools available to search for health insurance plans are deficient in offering personalized recommendations based on the coverage benefits and cost. Therefore, anticipating the users' needs we propose a cloud based framework that offers personalized recommendations about the health insurance plans. We use the Multi-attribute Utility Theory (MAUT) to help users compare different health insurance plans based on coverage and cost criteria, such as: (a) premium, (b) co-pay, (c) deductibles, (d) co-insurance, and (e) maximum benefit offered by a plan. To overcome the issues arising possibly due to the heterogeneous data formats and different plan representations across the providers, we present a standardized representation for the health insurance plans. The plan information of each of the providers is retrieved using the Data as a Service (DaaS). The framework is implemented as Software as a Service (SaaS) to offer customized recommendations by applying a ranking technique for the identified plans according to the user specified criteria. We present a cloud based health insurance plan recommendation system.We propose a standard ontological representation for all the health insurance plans.An algorithm to determine the similarities between the user requirements and plans is presented.We propose a ranking technique based on the Multi-attribute Utility Theory (MAUT).


title: Wasserstein discriminant analysis

abstract: Wasserstein discriminant analysis (WDA) is a new supervised linear dimensionality reduction algorithm. Following the blueprint of classical Fisher Discriminant Analysis, WDA selects the projection matrix that maximizes the ratio of the dispersion of projected points pertaining to different classes and the dispersion of projected points belonging to a same class. To quantify dispersion, WDA uses regularized Wasserstein distances. Thanks to the underlying principles of optimal transport, WDA is able to capture both global (at distribution scale) and local (at samples’ scale) interactions between classes. In addition, we show that WDA leverages a mechanism that induces neighborhood preservation. Regularized Wasserstein distances can be computed using the Sinkhorn matrix scaling algorithm; the optimization problem of WDA can be tackled using automatic differentiation of Sinkhorn’s fixed-point iterations. Numerical experiments show promising results both in terms of prediction and visualization on toy examples and real datasets such as MNIST and on deep features obtained from a subset of the Caltech dataset.


title: An electrical muscle stimulation haptic feedback for mixed reality tennis game

abstract: We have developed a novel haptic interface for mixed reality applications. Specifically, we have constructed an electrical muscle stimulation system which is wirelessly controlled by a computer and generates electrical pulses with controlled amplitude, timing, and frequencies. The characteristics of the pulses are similar to the commercial electrical muscle stimulators used in medical applications. We apply these pulses through two electrode pads attached to the user's forearms. Hence, the user can feel muscle contractions when pulses are activated.


title: New IBD genetics: common pathways with other diseases

abstract: Complex disease genetics has been revolutionised in recent years by the advent of genome-wide association (GWA) studies. The chronic inflammatory bowel diseases (IBDs), Crohn9s disease and ulcerative colitis have seen notable successes culminating in the discovery of 99 published susceptibility loci/genes (71 Crohn9s disease; 47 ulcerative colitis) to date. Approximately one-third of loci described confer susceptibility to both Crohn9s disease and ulcerative colitis. Amongst these are multiple genes involved in IL23/Th17 signalling ( IL23R , IL12B , JAK2 , TYK2 and STAT3 ), IL10 , IL1R2 , REL , CARD9 , NKX2.3 , ICOSLG , PRDM1 , SMAD3 and ORMDL3 . The evolving genetic architecture of IBD has furthered our understanding of disease pathogenesis. For Crohn9s disease, defective processing of intracellular bacteria has become a central theme, following gene discoveries in autophagy and innate immunity (associations with NOD2 , IRGM , ATG16L1 are specific to Crohn9s disease). Genetic evidence has also demonstrated the importance of barrier function to the development of ulcerative colitis ( HNF4A , LAMB1 , CDH1 and GNA12 ). However, when the data are analysed in more detail, deeper themes emerge including the shared susceptibility seen with other diseases. Many immune-mediated diseases overlap in this respect, paralleling the reported epidemiological evidence. However, in several cases the reported shared susceptibility appears at odds with the clinical picture. Examples include both type 1 and type 2 diabetes mellitus. In this review we will detail the presently available data on the genetic overlap between IBD and other diseases. The discussion will be informed by the epidemiological data in the published literature and the implications for pathogenesis and therapy will be outlined. This arena will move forwards very quickly in the next few years. Ultimately, we anticipate that these genetic insights will transform the landscape of common complex diseases such as IBD.


title: Designing Engaging Games Using Bayesian Optimization

abstract: We use Bayesian optimization methods to design games that maximize user engagement. Participants are paid to try a game for several minutes, at which point they can quit or continue to play voluntarily with no further compensation. Engagement is measured by player persistence, projections of how long others will play, and a post-game survey. Using Gaussian process surrogate-based optimization, we conduct efficient experiments to identify game design characteristics---specifically those influencing difficulty---that lead to maximal engagement. We study two games requiring trajectory planning, the difficulty of each is determined by a three-dimensional continuous design space. Two of the design dimensions manipulate the game in user-transparent manner (e.g., the spacing of obstacles), the third in a subtle and possibly covert manner (incremental trajectory corrections). Converging results indicate that overt difficulty manipulations are effective in modulating engagement only when combined with the covert manipulation, suggesting the critical role of a user's self-perception of competence.


title: Design Tradeoffs between Stator Core Loss and Torque Ripple in IPM Machines

abstract: High stator core losses can pose a significant problem in IPM machines operating over wide constant-power speed ranges. At lower speeds, the torque ripple can be undesirably large in some IPM machine designs, contributing to acoustic noise and vibration. While previous work has addressed these two problems independently, this paper shows that the conditions for reducing stator core losses during flux-weakening operation, dominated by harmonic eddy-current losses in the stator teeth, can conflict with the conditions for reducing the torque ripple of IPM machines. It is also shown that the resulting design tradeoffs depend on the details of the IPM machine topologies that are under consideration. The appropriate IPM machine topologies that offer more favorable tradeoffs are identified in order to achieve the best possible compromise of high-speed stator core losses and torque ripple characteristics.


title: On Visual Similarity Based 3D Model Retrieval

abstract: A large number of 3D models are created and available on the Web, since more and more 3D modelling and digitizing tools are developed for ever increasing applications. The techniques for content-based 3D model retrieval then become necessary. In this paper, a visual similarity-based 3D model retrieval system is proposed. This approach measures the similarity among 3D models by visual similarity, and the main idea is that if two 3D models are similar, they also look similar from all viewing angles. Therefore, one hundred orthogonal projections of an object, excluding symmetry, are encoded both by Zernike moments and Fourier descriptors as features for later retrieval. The visual similarity-based approach is robust against similarity transformation, noise, model degeneracy etc., and provides 42%, 94% and 25% better performance (precision-recall evaluation diagram) than three other competing approaches: (1)the spherical harmonics approach developed by Funkhouser et al., (2)the MPEG-7 Shape 3D descriptors, and (3)the MPEG-7 Multiple View Descriptor. The proposed system is on the Web for practical trial use (http://3d.csie.ntu.edu.tw), and the database contains more than 10,000 publicly available 3D models collected from WWW pages. Furthermore, a user friendly interface is provided to retrieve 3D models by drawing 2D shapes. The retrieval is fast enough on a server with Pentium IV 2.4GHz CPU, and it takes about 2 seconds and 0.1 seconds for querying directly by a 3D model and by hand drawn 2D shapes, respectively.


title: Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

abstract: Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault diagnosis has some deficiencies, such as complex computation of feature vectors, excessive dependence on prior knowledge and diagnostic expertise, and limited capacity for learning complex relationships in fault signals. Furthermore, following the increase in condition data, how to promptly process the massive fault data and automatically provide accurate diagnosis has become an urgent need to solve. Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines. In this paper, a nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain. For exploring the discrimination hidden in the acquired data, a stacked sparse autoencoders-based deep neural network is established and performed with an unsupervised learning procedure followed by a supervised fine-tuning process. We studied the significance of compressed acquisition and provided the effects of key factors and comparison with traditional methods. The effectiveness of the proposed method is validated using data sets from rolling element bearings and the analysis shows that it is able to obtain high diagnotic accuracies and is superior to the existing methods. The proposed method reduces the need of human labor and expertise and provides new strategy to handle the massive data more easily.


title: Neuroendocrine and Reproductive Consequences of Overexpression of Growth Hormone in Transgenic Mice

abstract: AbstractAvailability of recombinant growth hormone (GH) and development of long-acting formulations of this material will undoubtedly lead to widespread use of GH in animal industry and in medicine. GH can act, directly or indirectly, on multiple targets, but its influence on the reproductive system and on the hormonal control of reproduction is poorly understood. Overexpression of GH genes in transgenic animals provides a unique opportunity to study the effects of long-term GH excess. Transgenic mice overexpressing bovine, ovine, or rat GH (hormones with actions closely resembling, if not identical to, those of endogenous [mouse] GH), exhibit enhancement of growth, increased adult body size, and reduced life-span as well as a number of endocrine and reproductive abnormalities. Ectopic overexpression of bovine GH (bGH) driven by metallothionein or phosphoenolpyruvate carboxykinase promoters is associated with altered activity of hypothalamic neurons which produce somatostatin, loss of adenohypophyseal GH ...


Melanoma Skin Cancer Detection by Segmentation and Feature Extraction using combination of OTSU and STOLZ Algorithm Technique


title: Creating Intelligent Business Systems by Utilising Big Data and Semantics

abstract: Increasing volumes of data are generated from a variety of industries, organizations and people. Such data is now seen as a key business asset, since analysing it allows business decision makers to make choices with confidence. This data is often “big data” – a data of such volume, velocity and variety that conventional processing techniques are not suitable. We research the specific challenge of variety, where data comprises a number of incompatible formats. Semantic technology, or the use of ontologies, is seen as a core approach to resolving this challenge and aligning the data generated from heterogeneous data sources. This paper lays down the issues which should be addressed and reviews the work which has been done in the area of integrating structured and unstructured data sources, with a special focus on the financial domain. We then chart a research plan in the area following Design Science research.


title: PowerMatcher: multiagent control in the electricity infrastructure

abstract: Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation.


title: The Mechanics of Trust : A Framework for Research and Design

abstract: With an increasing number of technologies supporting transactions over distance and replacing traditional forms of interaction, designing for trust in mediated interactions has become a key concern for researchers in human computer interaction (HCI). While much of this research focuses on increasing users' trust, we present a framework that shifts the perspective towards factors that support trustworthy behavior. In a second step, we analyze how the presence of these factors can be signalled. We argue that it is essential to take a systemic perspective for enabling well-placed trust and trustworthy behavior in the long term. For our analysis we draw on relevant research from sociology, economics, and psychology, as well as HCI. We identify contextual properties (motivation based on temporal, social, and institutional embeddedness) and the actor's intrinsic properties (ability, and motivation based on internalized norms and benevolence) that form the basis of trustworthy behavior. Our analysis provides a frame of reference for the design of studies on trust in technology-mediated interactions, as well as a guide for identifying trust requirements in design processes. We demonstrate the application of the framework in three scenarios: call centre interactions, B2C e-commerce, and voice-enabled on-line gaming.


title: Word Reordering and a Dynamic Programming Beam Search Algorithm for Statistical Machine Translation

abstract: In this article, we describe an efficient beam search algorithm for statistical machine translation based on dynamic programming (DP). The search algorithm uses the translation model presented in Brown et al. (1993). Starting from a DP-based solution to the traveling-salesman problem, we present a novel technique to restrict the possible word reorderings between source and target language in order to achieve an efficient search algorithm. Word reordering restrictions especially useful for the translation direction German to English are presented. The restrictions are generalized, and a set of four parameters to control the word reordering is introduced, which then can easily be adopted to new translation directions. The beam search procedure has been successfully tested on the Verbmobil task (German to English, 8,000-word vocabulary) and on the Canadian Hansards task (French to English, 100,000-word vocabulary). For the medium-sized Verbmobil task, a sentence can be translated in a few seconds, only a small number of search errors occur, and there is no performance degradation as measured by the word error criterion used in this article.


V 2 X communication overview and V 2 I traffic light demonstrator


title: Analysing Post-Editing Performance: Correlations with Years of Translation Experience

abstract: Post-editing (PE) is still a new activity for many translators. The lack of training, clear and consistent guidelines and international standards may cause difficulties in the transition from translation to PE. Aiming to gain a better understanding of these difficulties and using data gathered from a pilot project, this paper explores possible correlations between PE performance and previous translation experience. We test a combination of the LISA QA Model and the GALE postediting guidelines as a typology for classifying post-editing changes implemented by six post-editors for French and Spanish (three for each target language). This enables a comparison of the types of changes made for the two target languages. We also measure speed and keyboard/mouse activity and link those to translator experience. The insight gathered may be useful for devising future PE guidelines and training programmes.


title: How Well Sentence Embeddings Capture Meaning

abstract: Several approaches for embedding a sentence into a vector space have been developed. However, it is unclear to what extent the sentence's position in the vector space reflects its semantic meaning, rather than other factors such as syntactic structure. Depending on the model used for the embeddings this will vary -- different models are suited for different down-stream applications. For applications such as machine translation and automated summarization, it is highly desirable to have semantic meaning encoded in the embedding. We consider this to be the quality of semantic localization for the model -- how well the sentences' meanings coincides with their embedding's position in vector space. Currently the semantic localization is assessed indirectly through practical benchmarks for specific applications. In this paper, we ground the semantic localization problem through a semantic classification task. The task is to classify sentences according to their meaning. A SVM with a linear kernel is used to perform the classification using the sentence vectors as its input. The sentences from subsets of two corpora, the Microsoft Research Paraphrase corpus and the Opinosis corpus, were partitioned according to their semantic equivalence. These partitions give the target classes for the classification task. Several existing models, including URAE, PV--DM and PV--DBOW, were assessed against a bag of words benchmark.


title: Stanley Milgram: Understanding Obedience and its Implications

abstract: Milgram's Obedience to Authority Experiments Milgram's Theory and Explanation of his Findings The Reaction of the Psychological Community Power, Domination and the Sociology of the Holocaust Political Culture, Political Theory and Social Influence The Individual in a Social World Contemporary Resonance of Milgram's Research Afterword


Exploring Semantic Representation in Brain Activity Using Word Embeddings