Requirements Elicitation for an Inter-Organizational Business Intelligence System for Small and Medium Retail Enterprises

Business Intelligence (BI) is on everyone's lips nowadays, since it provides businesses with the possibility to analyze their business practices and improve them. However, Small and Medium Enterprises (SME) often cannot leverage the positive effects of BI because of missing resources like personnel, knowledge, or money. Since SME pose a major form of business organization, this fact has to be overcome. As the retail industry is a substantial part of the SME branch, we propose an inter-organizational approach for a BI system for retail SME, which allows them to collaboratively collect data and perform analysis task. The aim of our ongoing research effort is the development of such a system following the Design Science Research Methodology. Within this article, the status quo of current BI practices in SME in the retail industry is analyzed through qualitative interviews with ten SME managers. Afterwards, adoption and success factors of BI systems and Inter-organizational Information Systems are worked out in a comprehensive structured literature review. Based on the status quo and the adoption and success factors, first requirements for the acceptance of an inter-organizational BI system are identified and validated in another round of qualitative interviews. This leads to nine functional requirements and three non-functional requirements, which can be used for designing and implementing an inter-organizational BI system for SME in the following research efforts.


Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers’ premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.


Bert: a scheduler for best-effort and realtime paths

We describe a new algorithm, called BERT, that can be used to schedule both best effort and realtime tasks on a multimedia workstation. BERT exploits two innovations. First, it is based on the virtual clock algorithm originally developed to schedule bandwidth on packet switches. Because this algorithm maintains a relationship between virtual time and real time, our algorithm allows us to simultaneously factor realtime deadlines into the scheduling decision, and to allocate some fraction of the processor to best effort tasks. Second, BERT includes a mechanism that allows one task to steal cycles from another. This mechanism is valuable for two reasons: it allows us to distinguish among tasks based on their importance, and it results in a robust algorithm that is not overly sensitive to the task having made a perfect reservation.


Similarity and Locality Based Indexing for High Performance Data Deduplication

Data deduplication has gained increasing attention and popularity as a space-efficient approach in backup storage systems. One of the main challenges for centralized data deduplication is the scalability of fingerprint-index search. In this paper, we propose SiLo, a near-exact and scalable deduplication system that effectively and complementarily exploits similarity and locality of data streams to achieve high duplicate elimination, throughput, and well balanced load at extremely low RAM overhead. The main idea behind SiLo is to expose and exploit more similarity by grouping strongly correlated small files into a segment and segmenting large files, and to leverage the locality in the data stream by grouping contiguous segments into blocks to capture similar and duplicate data missed by the probabilistic similarity detection. SiLo also employs a locality based stateless routing algorithm to parallelize and distribute data blocks to multiple backup nodes. By judiciously enhancing similarity through the exploitation of locality and vice versa, SiLo is able to significantly reduce RAM usage for index-lookup, achieve the near-exact efficiency of duplicate elimination, maintain a high deduplication throughput, and obtain load balance among backup nodes.


Functional System and Areal Organization of a Highly Sampled Individual Human Brain

Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.


A Fast Similarity Join Algorithm Using Graphics Processing Units

A similarity join operation A BOWTIEepsiv B takes two sets of points A, B and a value epsiv isin Ropf, and outputs pairs of points p isin A,q isin B, such that the distance D(p, q) les epsiv. Similarity joins find use in a variety of fields, such as clustering, text mining, and multimedia databases. A novel similarity join algorithm called LSS is presented that executes on a graphics processing unit (GPU), exploiting its parallelism and high data throughput. As GPUs only allow simple data operations such as the sorting and searching of arrays, LSS uses these two operations to cast a similarity join operation as a GPU sort-and-search problem. It first creates, on the fly, a set of space-filling curves on one of its input datasets, using a parallel GPU sort routine. Next, LSS processes each point p of the other dataset in parallel. For each p, it searches an interval of one of the space-filling curves guaranteed to contain all the pairs in which p participates. Using extensive theoretical and experimental analysis, LSS is shown to offer a good balance between time and work efficiency. Experimental results demonstrate that LSS is suitable for similarity joins in large high-dimensional datasets, and that it performs well when compared against two existing prominent similarity join methods.


Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source–destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events.


A 1.6Gbps Digital Clock and Data Recovery Circuit

A digital clock and data recovery circuit employs simple 3-level digital-to-analog converters to interface the digital loop filter to the voltage controlled oscillator and achieves low jitter performance. Test chip fabricated in a 0.13mum CMOS process achieves BER < 10-12 , plusmn1500ppm lock-in range, plusmn2500ppm tracking range, recovered clock jitter of 8.9ps rms and consumes 12mW power from a single-pin 1.2V supply, while operating at 1.6Gbps


Deep Convolutional Networks as shallow Gaussian Processes

We show that the output of a (residual) convolutional neural network (CNN) with an appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike “deep kernels”, has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer. The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GPs with a comparable number of parameters. 1


From cyber-physical systems to Industry 4.0: make future manufacturing become possible

Recently, ‘Industry 4.0’ has become the supporting force in the manufacturing field. From CPS to Industry 4.0, they are leading a new round of industrial revolution. To meet the fourth industrial revolution, USA, European countries, Japan, South Korea and China have presented different versions of the so-called ‘Industry 4.0’ strategies respectively, which are different from each other in the content, but their common goal is to achieve manufacturing structural adjustment and restructuring and upgrading, to seize a place for future manufacturing, to provide users with better service. What is more, we summarised the five major trends of the future manufacturing. Then, we introduce some enabling technologies about Industry 4.0, among them, CPS plays a key role in the Industry 4.0 factory. Based on the customer to business (C2B) mode, we regard everything as a service, and propose a structure of Industry 4.0. Finally, the automotive industry as an example, we make a brief introduction about an Industry 4.0 case. [Received 15 December 2015; Revised 30 May 2016; Accepted 30 May 2016]


Line-start synchronous reluctance motors: Design guidelines and testing via active inertia emulation

Line-Start Synchronous Reluctance (LS-SyR) Motors are a viable solution to reach IE4 efficiency class with the frame size of induction motors having standard efficiency. The proposed paper reviews the guidelines for the design of LS-SyR machines and introduces a new technique for testing their capability of load synchronization. Design wise, the efficiency at synchronous speed and the load pull-in capability must be traded off. The key design principles of LS-SyR machines are reviewed in this sense, and two manufacturing solutions are proposed for comparison. A new experimental method for the evaluation of the pull-in characteristic is proposed. The test fixture consists of a torque-controlled ac drive load, equipped with a shaft accelerometer. The load applied to the line-start machine under test is augmented with a component proportional to the acceleration measurement, to emulate additional inertia.


Toward De-Anonymizing Bitcoin by Mapping Users Location

The Bitcoin system (https://bitcoin.org) is a pseudo-anonymous currency that can dissociate a user from any real-world identity. In that context, a successful breach of the virtual and physical divide represents a signficant aw in the Bit-coin system [1]. In this project we demonstrate how to glean information about the real-world users behind Bitcoin transactions. We analyze publicly available data about the cryptocurrency. In particular, we focus on determining information about a Bitcoin user's physical location by examining that user's spending habits.


Twitter spam detection based on deep learning

Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.


The Effects of Feedback Interventions on Performance : A Historical Review , a Meta-Analysis , and a Preliminary Feedback Intervention Theory

the total number of papers may exceed 10,000. Nevertheless, cost consideration forced us to consider mostly published papers and technical reports in English. 4 Formula 4 in Seifert (1991) is in error—a multiplier of n, of cell size, is missing in the numerator. 5 Unfortunately, the technique of meta-analysis cannot be applied, at present time, to such effects because the distribution of dis based on a sampling of people, whereas the statistics of techniques such as ARIMA are based on the distribution of a sampling of observations in the time domain regardless of the size of the people sample involved (i.e., there is no way to compare a sample of 100 points in time with a sample of 100 people). That is, a sample of 100 points in time has the same degrees of freedom if it were based on an observation of 1 person or of 1,000 people. 258 KLUGER AND DENISI From the papers we reviewed, only 131 (5%) met the criteria for inclusion. We were concerned that, given the small percentage of usable papers, our conclusions might not fairly represent the larger body of relevant literature. Therefore, we analyzed all the major reasons to reject a paper from the meta-analysis, even though the decision to exclude a paper came at the first identification of a missing inclusion criterion. This analysis showed the presence of review articles, interventions of natural feedback removal, and papers that merely discuss feedback, which in turn suggests that the included studies represent 1015% of the empirical FI literature. However, this analysis also showed that approximately 37% of the papers we considered manipulated feedback without a control group and that 16% reported confounded treatments, that is, roughly two thirds of the empirical FI literature cannot shed light on the question of FI effects on performance—a fact that requires attention from future FI researchers. Of the usable 131 papers (see references with asterisks), 607 effect sizes were extracted. These effects were based on 12,652 participants and 23,663 observations (reflecting multiple observations per participant). The average sample size per effect was 39 participants. The distribution of the effect sizes is presented in Figure 1. The weighted mean (weighted by sample size) of this distribution is 0.41, suggesting that, on average, FI has a moderate positive effect on performance. However, over 38% of the effects were negative (see Figure 1). The weighted variance of this distribution is 0.97, whereas the estimate of the sampling error variance is only 0.09. A potential problem in meta-analyses is a violation of the assumption of independence. Such a violation occurs either when multiple observations are taken from the same study (Rosenthal, 1984) or when several papers are authored by the same person (Wolf, 1986). In the present investigation, there were 91 effects derived from the laboratory experiments reported by Mikulincer (e.g., 1988a, 1988b). This raises the possibility that the average effect size is biased, because his studies manipulated extreme negative FIs and used similar tasks. In fact, the weighted average d in Mikulincer's studies was —0.39; whereas in the remainder of the


Random telegraph noise (RTN) in scaled RRAM devices

The random telegraph noise (RTN) related read instability in resistive random access memory (RRAM) is evaluated by employing the RTN peak-to-peak (P-p) amplitude as a figure of merit (FoM). Variation of the FoM value over multiple set/reset cycles is found to follow the log-normal distribution. P-p decreases with the reduction of the read current, which allows scaling of the RRAM operating current. The RTN effect is attributed to the mechanism of activation/deactivation of the electron traps in (in HRS) or near (in LRS) the filament that affects the current through the RRAM device.


Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners

When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, usually planners are used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation of the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterized planner, showing that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which has 62 parameters and over 6.5 × 10 possible configurations. We demonstrate that by using this highly parameterized planning system in combination with the off-the-shelf, state-of-the-art automatic algorithm configuration procedure ParamILS, the planner can be specialized obtaining significantly improved performance.


Screen Printing of Highly Loaded Silver Inks on Plastic Substrates Using Silicon Stencils.

Screen printing is a potential technique for mass-production of printed electronics; however, improvement in printing resolution is needed for high integration and performance. In this study, screen printing of highly loaded silver ink (77 wt %) on polyimide films is studied using fine-scale silicon stencils with openings ranging from 5 to 50 μm wide. This approach enables printing of high-resolution silver lines with widths as small as 22 μm. The printed silver lines on polyimide exhibit good electrical properties with a resistivity of 5.5×10(-6) Ω cm and excellent bending tolerance for bending radii greater than 5 mm (tensile strains less than 0.75%).


Minimum Error Rate Training in Statistical Machine Translation

Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality. These training criteria make use of recently proposed automatic evaluation metrics. We describe a new algorithm for efficient training an unsmoothed error count. We show that significantly better results can often be obtained if the final evaluation criterion is taken directly into account as part of the training procedure.


Bayesian Unsupervised Word Segmentation with Nested Pitman-Yor Language Modeling

In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference. Our model is a nested hierarchical Pitman-Yor language model, where Pitman-Yor spelling model is embedded in the word model. We confirmed that it significantly outperforms previous reported results in both phonetic transcripts and standard datasets for Chinese and Japanese word segmentation. Our model is also considered as a way to construct an accurate word n-gram language model directly from characters of arbitrary language, without any “word” indications.


The Second International Chinese Word Segmentation Bakeoff

The second international Chinese word segmentation bakeoff was held in the summer of 2005 to evaluate the current state of the art in word segmentation. Twenty three groups submitted 130 result sets over two tracks and four different corpora. We found that the technology has improved over the intervening two years, though the out-of-vocabulary problem is still or paramount importance.


The Mathematics of Statistical Machine Translation: Parameter Estimation

We describe a series o,f five statistical models o,f the translation process and give algorithms,for estimating the parameters o,f these models given a set o,f pairs o,f sentences that are translations o,f one another. We define a concept o,f word-by-word alignment between such pairs o,f sentences. For any given pair of such sentences each o,f our models assigns a probability to each of the possible word-by-word alignments. We give an algorithm for seeking the most probable o,f these alignments. Although the algorithm is suboptimal, the alignment thus obtained accounts well for the word-by-word relationships in the pair o,f sentences. We have a great deal o,f data in French and English from the proceedings o,f the Canadian Parliament. Accordingly, we have restricted our work to these two languages; but we,feel that because our algorithms have minimal linguistic content they would work well on other pairs o,f languages. We also ,feel, again because of the minimal linguistic content o,f our algorithms, that it is reasonable to argue that word-by-word alignments are inherent in any sufficiently large bilingual corpus.


A cross-cultural test of the Maluma-Takete phenomenon.


Occupational therapy interventions for employment and education for adults with serious mental illness: a systematic review.

In this systematic review, we investigated research literature evaluating the effectiveness of occupational therapy interventions focusing on participation and performance in occupations related to paid and unpaid employment and education for people with serious mental illness. The review included occupation- and activity-based interventions and interventions addressing performance skills, aspects of the environment, activity demands, and client factors. The results indicate that strong evidence exists for the effectiveness of supported employment using individual placement and support to result in competitive employment. These outcomes are stronger when combined with cognitive or social skills training. Supported education programs emphasizing goal setting, skill development, and cognitive training result in increased participation in educational pursuits. The evidence for instrumental activities of daily living interventions that targeted specific homemaking occupations and supported parenting was limited but positive. Environmental cognitive supports, such as signs, and other compensatory strategies are useful in managing maladaptive behavior.


THE FUNCTIONAL APPROACH TO THE STUDY OF ATTITUDES

At the psychological level the reasons for holding or for changing attitudes are found in the functions they perform for the individual, specifically the functions of adjustment, ego defense, value expression, and knowledge. The conditions necessary to arouse or modify an attitude vary according to the motivational basis of the attitude. Ego-defensive attitudes, for example, can be aroused by threats, appeals to hatred and repressed impulses, and authoritarian suggestion, and can be changed by removal of threat, catharsis, and self-insight. Expressive attitudes are aroused by cues associated with the individual's values and by the need to reassert his self-image and can be changed by showing the appropriateness of the new or modified beliefs to the self-concept Brain washing is primarily directed at the value-expressive function and operates by controlling all environmental supports of old values. Changing attitudes may involve generalization of change to related areas of belief and feeling. Minimal generalization seems to be the rule among adults; for example, in politics voting for an opposition candidate does not have much effect upon party identification. The author is Professor of Psychology at the University of Michigan, former president of the Society for the Psychological Study of Social Issues, and coeditor of Research Methods in the Behavioral Sciences and Public Opinion and Propaganda.


Automatic linkage of vital records.

The term record linkage has been used to indicate the bringing together of two or more separately re?orded pieces of information concerning a particular individual or family (I). Defined in this broad manner, it includes almost any use of a file of records to determine what has subsequently happened to people about whom one has some prior information, The various facts concerning an individual which in any modern society are recorded routinely would, if brought together, form an extensively documented history of his 1ife. In theory at least, an understanding might be derived from such collective histories concerning many of the factors which operate to influence the welfare of human populations, factors about which we are at present almost entirely in ignorance. Of course, much of the recorded information is in a relatively inaccessible form; but, even when circumstances have been most favorable, as in the registrations of births, deaths, and marriages, and in the census, there has been little recognition of the special value of the records as a source of statistics when they are brought together so as to relate the successive events in the lives of particular individuals and families. The chief reason for this lies in the high cost of searching manually for large numbers of single documents among vast accumulations of files. It is obvious that the searching could be mechanized, but as yet there has been no clear demonstration that machines can carry out the record linkages rapidly enough, cheaply enough, and with sufficient accuracy to make this practicable. The need for various follow-up studies such as might be carried out with the aid of record linkage have been discussed in detail elsewhere (I, 2), and there are numerous examples o{ important surveys which could be greatly extended in scope if existing record files were more readily linkable (3). Our special interest in the techniques of record linkage relates to their possible use (i) for keeping track of large groups of individuals who have been exposed to low levels of radiation, in order to determine the causes of their eventual deaths (see 4, chap. 8, para. 48; 5), and (ii) for assessing the relative importance of repeated natural mutations on the one hand, and of fertility differentials on the other, in maintaining the frequency of genetic defects in human populations (see 4, chap. 6, para. 36c). Our own studies (6) were started as part of a plan to look for possible differentials of family fertility in relation to the presence or absence of hereditary disease (through the use of vital records and a register of hm-sdicapped children). The first step has been the development of a method for linking birth records to marriage records automatically with a Datatron 205 computer. For this purpose use has been made of the records of births which occurred in the Canadian province of British Columbia during the year 1955 (34, 138 births) and of the marriages which took place in the same province over the 10-year period 1946-55 (114,47 1 marriages). Fortunately, these records were already in punch-card form as a part of Canada’s National Index, and from them could be extracted most of the necessary information on names and other


Financial Stock Market Forecast using Data Mining Techniques

The automated computer programs using data mining and predictive technologies do a fare amount of trades in the markets. Data mining is well founded on the theory that the historic data holds the essential memory for predicting the future direction. This technology is designed to help investors discover hidden patterns from the historic data that have probable predictive capability in their investment decisions. The prediction of stock markets is regarded as a challenging task of financial time series prediction. Data analysis is one way of predicting if future stocks prices will increase or decrease. Five methods of analyzing stocks were combined to predict if the day's closing price would increase or decrease. These methods were Typical Price (TP), Bollinger Bands, Relative Strength Index (RSI), CMI and Moving Average (MA). This paper discussed various techniques which are able to predict with future closing stock price will increase or decrease better than level of significance. Also, it investigated various global events and their issues predicting on stock markets. It supports numerically and graphically.


A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer

The advance of deep learning has made huge changes in computer vision and produced various off-the-shelf trained models. Particularly, Convolutional Neural Network (CNN) has been widely used to build image classification model which allow researchers transfer the pre-trained learning model for other classifications. We propose a transfer learning method to detect breast cancer using histopathology images based on Google's Inception v3 model which were initially trained for the classification of non-medical images. The pilot study shows the feasibility of transfer learning in the detection of breast cancer with AUC of 0.93.


Data Management for Journalism

We describe the power and potential of data journalism, where news stories are reported and published with data and dynamic visualizations. We discuss the challenges facing data journalism today and how recent data management tools such as Google Fusion Tables have helped in the newsroom. We then describe some of the challenges that need to be addressed in order for data journalism to reach its full


Escape loneliness by going digital: a quantitative and qualitative evaluation of a Dutch experiment in using ECT to overcome loneliness among older adults.

BACKGROUND
This study evaluates the outcomes of an Internet-at-home intervention experiment that intended to decrease loneliness among chronically ill and physically handicapped older adults through introducing them to the use of an electronic communication facility.

METHOD
To determine the effectiveness of the experiment in terms of reducing loneliness, 15 older adults were interviewed three times: shortly before the start, two years later and immediately after termination of the experiment, while their loneliness scores at zero and post-measurement were compared with those of a control group.

RESULTS
Both the participants and the control persons experienced a reduction in loneliness over time. However, the reduction was only significant for the intervention participants. Moreover, the changes in loneliness were significantly greater among the participants compared to the control persons. When looking more in detail, the effect of the experiment was only significant regarding emotional loneliness and among the highest educated. Findings of the qualitative research enabled us to understand the mechanisms through which the intervention helped alleviate loneliness. E-mail was found to facilitate social contact. Furthermore, the computer and Internet were often used to pass the time, taking people's minds off their loneliness. Unexpectedly, the intervention also improved people's self-confidence.

CONCLUSION
The decline in loneliness is likely to be greater if persons under more favorable circumstances are selected and if more social functions of the Internet are used.


Semantic Foggy Scene Understanding with Synthetic Data

This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with clear-weather images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN). In particular, a complete pipeline to add synthetic fog to real, clear-weather images using incomplete depth information is developed. We apply our fog synthesis on the Cityscapes dataset and generate Foggy Cityscapes with 20,550 images. SFSU is tackled in two ways: (1) with typical supervised learning, and (2) with a novel type of semi-supervised learning, which combines (1) with an unsupervised supervision transfer from clear-weather images to their synthetic foggy counterparts. In addition, we carefully study the usefulness of image dehazing for SFSU. For evaluation, we present Foggy Driving, a dataset with 101 real-world images depicting foggy driving scenes, which come with ground truth annotations for semantic segmentation and object detection. Extensive experiments show that (1) supervised learning with our synthetic data significantly improves the performance of state-of-the-art CNN for SFSU on Foggy Driving; (2) our semi-supervised learning strategy further improves performance; and (3) image dehazing marginally advances SFSU with our learning strategy. The datasets, models and code are made publicly available.


An architecture-based approach to self-adaptive software

CONSIDER THE FOLLOWING SCEnario. A fleet of unmanned air vehicles undertakes a mission to disable an enemy airfield. Pre-mission intelligence indicates that the airfield is not defended, and mission planning proceeds accordingly. While the UAVs are en route to the target, new intelligence indicates that a mobile surface-to-air missile launcher now guards the airfield. The UAVs autonomously replan their mission, dividing into two groups—a SAM-suppression unit and an airfield-suppression unit—and proceed to accomplish their objectives. During the flight, specialized algorithms for detecting and recognizing SAM launchers automatically upload and are integrated into the SAM-suppression unit’s software. In this scenario, new software components are dynamically inserted into fielded, heterogeneous systems without requiring system restart, or indeed, any downtime. Mission replanning relies on analyses that include feedback from current performance. Furthermore, such replanning can take place autonomously, can involve multiple, distributed, cooperating planners, and where major changes are demanded and require human approval or guidance, can cooperate with mission analysts. Throughout, system integrity requires the assurance of consistency, correctness, and coordination of changes. Other applications for fleets of UAVs might include environment and land-use monitoring, freeway-traffic management, fire fighting, airborne cellular-telephone relay stations, and damage surveys in times of natural disaster. How wasteful to construct afresh a specific software platform for each new UAV application! Far better if software architects can simply adapt the platform to the application at hand, and better yet, if the platform itself adapts on demand even while serving some other purpose. For example, an irborne sensor platform designed for environmental and land-use monitoring could prove useful for damage surveys following an earthquake or hurricane, provided someone could change the software quickly enough and with sufficient assurance that the new system would perform as intended. Software engineering aims for the systematic, principled design and deployment of applications that fulfill software’s original promise—applications that retain full plasticity throughout their lifecycle and that are as easy to modify in the field as they are on the drawing board. Software engineers have pursued many techniques for achieving this goal: specification languages, high-level programming languages, and object-oriented analysis and design, to name just a few. However, while each contributes to the goal, the sum total still falls short. Self-adaptive software will provide the key. Many disciplines will contribute to its progress, but wholesale advances require a sysSELF-ADAPTIVE SOFTWARE REQUIRES HIGH DEPENDABILITY, ROBUSTNESS, ADAPTABILITY, AND AVAILABILITY. THIS ARTICLE DESCRIBES AN INFRASTRUCTURE SUPPORTING TWO SIMULTANEOUS PROCESSES IN SELF-ADAPTIVE SOFTWARE: SYSTEM EVOLUTION, THE CONSISTENT APPLICATION OF CHANGE OVER TIME, AND SYSTEM ADAPTATION, THE CYCLE OF DETECTING CHANGING CIRCUMSTANCES AND PLANNING AND DEPLOYING RESPONSIVE MODIFICATIONS.


Approach and Avoidance Achievement Goals and Intrinsic Motivation : A Mediational Analysis

Most contemporary achievement goal conceptualizations consist of a performance goal versus mastery goal dichotomy. The present research offers an alternative framework by partitioning the performance goal orientation into independent approach and avoidance motivational orientations. Two experiments investigated the predictive utility of the proposed approach-avoidance achievement goal conceptualization in the intrinsic motivation domain. Results from both experiments supported the proposed framework; only performance goals grounded in the avoidance of failure undermined intrinsic motivation. Task involvement was validated as a mediator of the observed effects on intrinsic motivation. Ramifications for the achievement goal approach to achievement motivation and future research avenues are discussed.


Intrinsic cancer subtypes-next steps into personalized medicine

Recent technological advances have significantly improved our understanding of tumor biology by means of high-throughput mutation and transcriptome analyses. The application of genomics has revealed the mutational landscape and the specific deregulated pathways in different tumor types. At a transcriptional level, multiple gene expression signatures have been developed to identify biologically distinct subgroups of tumors. By supervised analysis, several prognostic signatures have been generated, some of them being commercially available. However, an unsupervised approach is required to discover a priori unknown molecular subtypes, the so-called intrinsic subtypes. Moreover, an integrative analysis of the molecular events associated with tumor biology has been translated into a better tumor classification. This molecular characterization confers new opportunities for therapeutic strategies in the management of cancer patients. However, the applicability of these new molecular classifications is limited because of several issues such as technological validation and cost. Further comparison with well-established clinical and pathological features is expected to accelerate clinical translation. In this review, we will focus on the data reported on molecular classification in the most common tumor types such as breast, colorectal and lung carcinoma, with special emphasis on recent data regarding tumor intrinsic subtypes. Likewise, we will review the potential applicability of these new classifications in the clinical routine.


3-D Deep Learning Approach for Remote Sensing Image Classification

Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today’s large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs.


Towards Know-how Mapping Using Goal Modeling

In organizing the knowledge in a field of study, it is common to use classification techniques to organize concepts and approaches along dimensions of interest. In technology domains, an advance often appears in the form of a new way or method for achieving an objective. This paper proposes to use goal modeling to map the means-ends knowledge (“know-how”) in a domain. A know-how map highlights the structure of recognized problems and known solutions in the domain, thus facilitating gap identification and prompting new research and innovation. We contrast the proposed goal-oriented approach with a claim-oriented approach, using Web Page Ranking as a sample domain.


Controlling Home Appliances Remotely through Voice Command

the main concern in systems development is the integration of technologies to increase customer satisfaction. Research presented in this paper focuses mainly in three things first to understand the speech or voice of user second is to control the home appliances through voice call and third is to finds intrusion in the house. The user can make a voice call in order to perform certain actions such as switching lights on/off, getting the status of any appliance etc. And when system finds intrusion it sends an alert voice message to preconfigured cell when the user is away from the place. The proposed system is implemented using voice Global System for Mobile Communications (GSM) and wireless technology based on .NET framework and Attention (AT) commands. Microsoft speech reorganization engine, speech SDK 5.1 is used to understand the voice command of user. As it is wireless so more cost effective and easy to use. The GSM technology used in system provide the everywhere access of the system for security. Experimental results show that the system is more secure and cost effective as compared to existing systems. We conclude that this system provides solution for the problems faced by home owner in daily life and make their life easy and comfortable by proposing cost effective and reliable solution. Keywords-component; Voice GSM; Voice message; radio friquencey (RF); AT commands.


RFID Coverage Extension Using Microstrip-Patch Antenna Array [Wireless Corner]

In this paper, a UHF-band 2 times 2 microstrip phased-array antenna is designed and implemented to extend the coverage of an RFID reader system. The phased-array antenna has four microstrip-patch antennas, three Wilkinson power dividers, and a transmission-line phase shifter. These are printed on a dielectric substrate with a dielectric constant of 4.5. The array has dimensions of 34 cm times 45 cm, operating at a frequency of 867 MHz, as specified in RFID Gen2 protocol European standards. The phased-array antenna has a measured directivity of 12.1 dB, and the main-beam direction can be steered to angles of plusmn 40deg, with a HPBW of 90deg. The phased-array antenna is used as the receiving antenna in a commercial reader system. Experimental results indicate that the coverage of the RFID system with the phased-array antenna is superior to the coverage with a conventional broader-beamwidth microstrip-patch antenna. The proposed system can also be used for a wireless positioning system.


N-Fold Inspection: A Requirements Analysis Technique

N-fold inspection uses traditional inspections of the user requirements document (URD) but replicates the inspection activities using N independent teams. A pilot study was conducted to explore the usefulness of N-fold inspection during requirements analysis. A comparison of N-fold inspection with other development techniques reveals that N-fold inspection is a cost-effective method for finding faults in the URD and may be a valid technique in the development of mission-critical software systems.


A Benchmark for Open Link Prediction

Knowledge graphs (KG) and open information extraction systems (OIE) both represent information by a set of (subject, relation, object)-triples. In KGs, arguments and relations are taken from a closed vocabulary, i.e., entities are disambiguated and relations are assumed to be semantically distinct. In OIE, arguments and relations are extracted from natural language text. There is no predefined or closed vocabulary, many different mentions may refer to the same entity, and relations can be semantically close but not equal, e.g. “visited by” and “frequently visited by”. The set of OIE triples can be viewed as an open knowledge graph (OKG). In this paper, we study the problem of open link prediction: Given a mention of an entity and an open relation (e.g., “A. Einstein” and “is a native of”), predict correct entity mentions (e.g., “Imperial Germany”) using solely the OKG. Models that aim to solve the open link prediction task sucessfully need to be able to reason about different entity mentions, as well as the relations between them, in a fully automatic, unsupervised way. As a benchmark for this task we design an OKG dataset, an evaluation protocol and present and evaluate a number of baseline models. In particular, we compare a token-based co-occurrence model, a non token-based link prediction model, and several token-based link prediction models. Our results indicate that the latter combination is beneficial.


Dioxin characterisation , formation and minimisation during municipal solid waste ( MSW ) incineration : review

The present review discusses the current views on methods to minimise dioxins, namely polychlorinated dibenzodioxins (PCDDs) and dibenzofurans (PCDFs), formation in MSW incineration systems. The structure of this group of compounds is discussed initially and then the toxic equivalence scale is presented for the most common isomers and congeners in the dioxin family. The current status on dioxin limits imposed in various countries and by various organisations is presented. A detailed analysis of the theories leading to dioxin formation in MSW incineration is given, since, this has been one of the most controversial areas of dioxin chemistry for the past 20 years. Three dioxin formation theories were considered possible for a long time; (i) from PCDD/PCDFs originally present in the furnace feedstock; (ii) from precursor compounds (foundation formatting molecules which could react rapidly with other groups in the system to form dioxins) in the MSW feed; (iii) from de novo synthesis of smaller, relatively innocuous chemical molecules combining together to form the dioxins. Methods (ii) and (iii) are based on heterogeneously catalysed reactions. Some researchers are considering possible homogeneous thermal reaction formation of dioxin. This review demonstrates that with the advanced modern MSW combustion systems, option (i) is a most unlikely route and also methods (ii) and (iii) are quite feasible. Based on thermodynamic and kinetic data in the literature, the rate and extent of the formation of dioxins and their precursors by certain mechanisms can definitely be contributing to routes (ii) and (iii). Since even the most advanced MSW combustion systems do not produce complete combustion, predominantly because of inadequate feed preparation and turbulence, some de novo synthesis of precursors can also take place. These ‘de novo precursors’ could be carried through the combustion unit adsorbed or absorbed on particulate material such as soot and dust, but also these precursors could be formed during the cooling process by heterogeneous catalytic reactions and go on to form dioxins. The maximum rate of formation of PCDD/PCDFs from both sources lies in the temperature range 300–400 ◦C. This knowledge of formation rates and mechanisms provides the basis of designing combustion systems. A two stage approach is adopted; firstly, system design to achieve complete combustion and minimise formation; secondly, end-of-pipe treatment systems to remove dioxins. In the first case, combustion temperature should be above 1000 ◦C, combustion residence time should be greater than 1 s, combustion chamber turbulence should be represented by a Reynolds number greater than 50,000, good MSW feed preparation and controlled feed rate are also critical. In the second category, very rapid gas cooling from 400 to 250 ◦C should be achieved, semi-dry lime scrubbing and Abbreviations:AC, activated carbon; ACSS, activated carbon scrubbing solution; APCD, air pollution control device; APME, Association of Plastics Manufacturers in Europe; ASME, American Society of Mechanical Engineers; BF, bag filter; BPM, best practical means; COT, UK Committee on Toxicology of Chemicals in Food, Consumer Products and Environment; CTZ, critical temperature zone; DOE, Department of the Environment, UK; DIAC, direct injec tion of activated carbon; DS, dry scrubber; DSI, dry sorbent injection; EADON, toxic equivalence factors proposed by Eadon method; EEC, European Economi c Community; EGB, electro granular bed; ESP, electrostatic precipitator; FF, fabric filter; FRG, Federal Republic of Germany; GCP, good combustion pr actice; HMIP, Her Majesty’s Inspectorate on Pollution, UK; HOC, hearth-oven coke; HRGC, high resolution gas chromatography; HRMS, high resolution mass spectrometry; IARC, International Agency for Research on Cancer; Kow, octanol-water partition coefficient; MSW, municipal solid waste; MSWI, municipal solid waste incinerator; NATO, Northern Atlantic Treaty Organisation; NOAEL, no observed adverse effect level; OCDD, octachlorinated dibenzodio xin; OCDF, octachlorinated dibenzofuran; PCB, polychlorinated biphenyls; PCCS, programmable computer control system; PCDD, polychlorinated dibenzodiox in; PCDF, polychlorinated dibenzofuran; PCP, pentachlorophenol; RDF, refuse derived fuel; SCR, selective catalytic reduction/reactor; SDA, spray dry abs orber; TALuft, Technische Airbildung Luft; TCDD, tetrachlorinated dibenzodioxin; TCDF, tetrachlorinated dibenzofuran; TDI, tolerable daily intake; TEF, toxi c equivalence factor; TEQ, toxic equivalence; USEPA, United States Environmental Protection Agency; WHO, World Health Organisation; WQ, water quench; WS, wet scrubber E-mail address:kemckayg@ust.hk (G. McKay). 1385-8947/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S1385-8947(01)00228-5 344 G. McKay / Chemical Engineering Journal 86 (2002) 343–368 bag filtration coupled with activated carbon injection adsorption as end-of-pipe treatments can all play a role in prevention or minimisation of dioxins in the final flue gas emission to the atmosphere. © 2002 Elsevier Science B.V. All rights reserved.


Compressive sensing

1 Scope The Shannon/Nyquist sampling theorem tells us that in order to not lose information when uniformly sampling a signal we must sample at least two times faster than its bandwidth. In many applications, including digital image and video cameras, the Nyquist rate can be so high that we end up with too many samples and must compress in order to store or transmit them. In other applications, including imaging systems (medical scanners, radars) and high-speed analog-to-digital converters, increasing the sampling rate or density beyond the current state-of-the-art is very expensive. In this lecture, we will learn about a new technique that tackles these issues using compressive sensing [1, 2]. We will replace the conventional sampling and reconstruction operations with a more general linear measurement scheme coupled with an optimization in order to acquire certain kinds of signals at a rate significantly below Nyquist.


Formal Specification with the Java Modeling Language

This text is a general, self contained, and tool independent introduction into the Java Modeling Language, JML. It appears in a book about the KeY approach and tool, because JML is the dominating starting point of KeY style Java verification. However, this chapter does not depend on KeY, nor any other specific tool, nor on any specific verification methodology. With this text, the authors aim to provide, for the time being, the definitive, general JML tutorial. Other chapters in this book discuss the particular usage of JML in KeY style verification.1 In this chapter, however, we only refer to KeY in very few places, without relying on it. This introduction is written for all readers with an interest in formal specification of software in general, and anyone who wants to learn about the JML approach to specification in particular. A preliminary version of this chapter appeared as a technical report [Huisman et al., 2014].


Low complexity photo sensor dead pixel detection algorithm

Although the number of pixels in image sensors is increasing exponentially, production techniques have only been able to linearly reduce the probability that a pixel will be defective. The result is a rapidly increasing probability that a sensor will contain one or more defective pixels. The defect pixel detection and defect pixel correction are operated separately but the former must employ before the latter is in use. Traditional detection scheme, which finds the defect pixels during manufacturing, is not able to discover the spread defect pixels years late. Consequently, the lifetime and robust defect pixel detection technique, which identifies the fault pixels when camera is in use, is more practical and developed. The paper presents a two stages dead pixel detection technique without complicated mathematic computations so that the embedded devices can easily implement it. Using six dead pixel types are tested and the experimental result indicates that it can be accelerated more than four times the detection time.


Probabilistic Latent Semantic Indexing

Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.


Latent Semantic Indexing (LSI): TREC-3 Report

We used LSI for both TREC-3 routing and adhoc tasks. For the routing tasks an LSI space was constructed using the training documents. We compared profiles constructed using just the topic words (no training) with profiles constructed using the average of relevant documents (no use of the topic words). Not surprisingly, the centroid of the relevant documents was 30% better than the topic words. This simple feedback method was quite good compared to the routing performance of other systems. Various combinations of information from the topic words and relevant documents provide small additional improvements in performance. For the adhoc task we compared LSI to keyword vector matching (i.e. using no dimension reduction). Small advantages were obtained for LSI even with the long TREC topic statements.


Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks

In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness measures, LPOCSIN utilizes a simple yet effective one. Using this measure, we transform the cluster assignment problem into a series of alternating linear programs, and further propose a highly efficient procedure for solving those alternating problems. We evaluate LPOCSIN and other state-of-the-art methods by extensive experiments covering a wide range of synthetic and real networks. The experiments show that LPOCSIN significantly outperforms the other methods in recovering ground-truth clusters while being an order of magnitude faster than the most efficient state-of-the-art method.


A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.


The Software Concordance: A User Interface for Advanced Software Documents

The Software Concordance is a hypermedia software development environment exploring how document technology and versioned hypermedia can improve software document management. The Software Concordance’s central tool is a document editor that integrates program analysis and hypermedia services for both source code and multimedia documentation in XML. The editor allows developers to embed inline multimedia documentation, including images and audio clips, into their program sources and to bind them to any program fragment. Web style hyperlinks are also supported. The developers are able to move seamlessly between source code and the documentation that describes its motivation, design, correctness and use without disrupting the integrated program analysis services, which include lexing, parsing and type checking. This paper motivates the need for environments like the Software Concordance, describes the key features and architecture of its document editor and discusses future work to improve its functionality.


Cloud-enabled privacy-preserving collaborative learning for mobile sensing

In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.


Malware Detection Using Deep Transferred Generative Adversarial Networks

Malicious software is generated with more and more modified features of which the methods to detect malicious software use characteristics. Automatic classification of malicious software is efficient because it does not need to store all characteristic. In this paper, we propose a transferred generative adversarial network (tGAN) for automatic classification and detection of the zero-day attack. Since the GAN is unstable in training process, often resulting in generator that produces nonsensical outputs, a method to pre-train GAN with autoencoder structure is proposed. We analyze the detector, and the performance of the detector is visualized by observing the clustering pattern of malicious software using t-SNE algorithm. The proposed model gets the best performance compared with the conventional machine learning algorithms.


Example-based hinting of true type fonts

Hinting in TrueType is a time-consuming manual process in which a typographer creates a sequence of instructions for better fitting the characters of a font to a grid of pixels. In this paper, we propose a new method for automatically hinting TrueType fonts by transferring hints of one font to another. Given a hinted source font and a target font without hints, our method matches the outlines of corresponding glyphs in each font, and then translates all of the individual hints for each glyph from the source to the target font. It also translates the control value table (CVT) entries, which are used to unify feature sizes across a font. The resulting hinted font already provides a great improvement over the unhinted version. More importantly, the translated hints, which preserve the sound, hand-designed hinting structure of the original font, provide a very good starting point for a professional typographer to complete and fine-tune, saving time and increasing productivity. We demonstrate our approach with examples of automatically hinted fonts at typical display sizes and screen resolutions. We also provide estimates of the time saved by a professional typographer in hinting new fonts using this semi-automatic approach.


Learning Progressions: Supporting Instruction and Formative Assessment Margaret Heritage the Council of Chief State School Officers T He Fast Scass  Formative Assessment for Teachers and Learners Learning Progressions: Supporting Instruction and Formative Assessment

By its very nature, learning involves progression. To assist in its emergence, teachers need to understand the pathways along which students are expected to progress. These pathways or progressions ground both instruction and assessment. Yet, despite a plethora of standards and curricula, many teachers are unclear about how learning progresses in specific domains. This is an undesirable situation for teaching and learning, and one that particularly affects teachers’ ability to engage in formative assessment.


Corporate Social Responsibility and Innovation in Management Accounting

• This study develops a conceptual framework to illustrate how sustainability issues are embedded in the management control system to operationalise firms' corporate social responsibility (CSR) objectives and strategies. • Categorising firms' CSR activities into Responsive and Strategic CSR agendas (Porter and Kramer 2006), this framework emphasises that different uses of MCS must be adopted to effectively operationalise different CSR agendas. • Boundary systems and diagnostic uses of budgets and performance management systems are more pertinent to operationalising Responsive CSR agendas, whereas the belief system and interactive uses of MCS are more effective in facilitating the selection and implementation of Strategic CSR programmes. • This study provides empirical evidence on the different types of Responsive and Strategic CSR programmes in two Chinese State-owned Enterprises (SOEs). Our empirical data shed light on the role of MCS in implementing CSR strategies at these firms.


A High Gain Input-Parallel Output-Series DC/DC Converter With Dual Coupled Inductors

High voltage gain dc-dc converters are required in many industrial applications such as photovoltaic and fuel cell energy systems, high-intensity discharge lamp (HID), dc back-up energy systems, and electric vehicles. This paper presents a novel input-parallel output-series boost converter with dual coupled inductors and a voltage multiplier module. On the one hand, the primary windings of two coupled inductors are connected in parallel to share the input current and reduce the current ripple at the input. On the other hand, the proposed converter inherits the merits of interleaved series-connected output capacitors for high voltage gain, low output voltage ripple, and low switch voltage stress. Moreover, the secondary sides of two coupled inductors are connected in series to a regenerative capacitor by a diode for extending the voltage gain and balancing the primary-parallel currents. In addition, the active switches are turned on at zero current and the reverse recovery problem of diodes is alleviated by reasonable leakage inductances of the coupled inductors. Besides, the energy of leakage inductances can be recycled. A prototype circuit rated 500-W output power is implemented in the laboratory, and the experimental results shows satisfactory agreement with the theoretical analysis.


Computer analysis of computed tomography scans of the lung: a survey

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.


A 0.5-V bulk-input fully differential operational transconductance amplifier

We present a fully differential two-stage Miller op-amp operating from a 0.5 V power supply. The input signal is applied to the bulk nodes of the input devices. A prototype was designed in a standard 0.18 /spl mu/m CMOS process using standard 0.5 V V/sub T/ devices. It has a measured 52 dB DC gain, a 2.5 MHz gain-bandwidth and consumes 110 /spl mu/W.


A 40-50-GHz SiGe 1 : 8 differential power divider using shielded broadside-coupled striplines

This paper presents a 1 : 8 differential power divider implemented in a commercial SiGe BiCMOS process using fully shielded broadside-coupled striplines integrated vertically in the silicon interconnect stackup. The 1 : 8 power divider is only 1.12 x1.5 mm2 including pads, and shows 0.4-dB rms gain imbalance and <3deg rms phase imbalance from 40 to 50 GHz over all eight channels, a measured power gain of 14.9 plusmn0.6 dB versus a passive divider at 45 GHz, and a 3-dB bandwidth from 37 to 52 GHz. A detailed characterization of the shielded broadside-coupled striplines is presented and agrees well with simulations. These compact lines can be used for a variety of applications in SiGe/CMOS millimeter-wave circuits, including differential signal distribution, miniature power dividers, matching networks, filters, couplers, and baluns.


AutoRec: Autoencoders Meet Collaborative Filtering

This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.


A –$21.2$ -dBm Dual-Channel UHF Passive CMOS RFID Tag Design

Previous research results showed that UHF passive CMOS RFID tags had difficulty to achieve sensitivity less than -20 dBm. This paper presents a dual-channel 15-bit UHF passive CMOS RFID tag prototype that can work at sensitivity lower than -20 dBm. The proposed tag chip harvests energy and backscatters uplink data at 866.4-MHz (for ETSI) or 925-MHz (for FCC) channel and receives downlink data at 433-MHz channel. Consequently, the downlink data transmission does not interrupt our tag from harvesting RF energy. To use the harvested energy efficiently, we design a tag chip that includes neither a regulator nor a VCO such that the harvested energy is completely used in receiving, processing, and backscattering data. Without a regulator, our tag uses as few active analog circuits as possible in the receiver front-end. Instead, our tag uses a novel digital circuit to decode the received data. Without a VCO, the design of our tag can extract the required clock signal from the downlink data. Measurement result shows that the sensitivity of the proposed passive tag chip can reach down to -21.2 dBm. Such result corresponds to a 19.6-m reader-to-tag distance under 36-dBm EIRP and 0.4-dBi tag antenna gain. The chip was fabricated in TSMC 0.18- μm CMOS process. The die area is 0.958 mm ×0.931mm.


Quantifying and Visualizing Attribute Interactions

Interactions are the prototypical examples of unexpected and irreducible patterns in data that encompass several attributes. We attempt to unify the divergent definitions and conceptions of interactions present in the literature, and propose two separate conceptions of what interactions are: if a group of attributes interacts observationally, we cannot understand their relationship without looking at all the attributes simultaneously; if a group of attributes interacts representationally, we cannot create a model that would not include conditions involving all the attributes. Rather than test for interactions, we attempt to quantify them, and visually present the most important interactions of the data. Our quantification is based on how well we can approximate a joint probability distribution without admitting that there are interactions. Judging from these illustrations, most machine learning procedures are not treating interactions appropriately.


Development of a coax-to-microstrip transition over the range of 1–15GHz

In this paper, a coax-to-microstrip transition incorporating a miniature coaxial connector with glass bead is developed. The transition is firstly simulated with the aid of HFSS and then measured in the frequency range of 1-15 GHz. The simulated and measured results are found in good agreement.


18 kW three phase inverter system using hermetically sealed SiC phase-leg power modules

Power electronics play an important role in electricity utilization from generation to end customers. Thus, high-efficiency power electronics help to save energy and conserve energy resources. Research on silicon carbide (SiC) power electronics has shown their better efficiency compared to Si power electronics due to the significant reduction in both conduction and switching losses. Combined with their high-temperature capability, SiC power electronics are more reliable and compact. This paper focuses on the development of such a high efficiency, high temperature inverter based on SiC JFET and diode modules. It involves the work on high temperature packaging (>200 °C), inverter design and prototype development, device characterization, and inverter testing. A SiC inverter prototype with a power rating of 18 kW is developed and demonstrated. When tested at moderate load levels compared to the inverter rating, an efficiency of 98.2% is achieved by the initial prototype without optimization, which is higher than most Si inverters.


VQA: Visual Question Answering


Training Deep Neural Networks on Noisy Labels with Bootstrapping

Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting. The performance depends critically on the amount of labeled examples, and in current practice the labels are assumed to be unambiguous and accurate. However, this assumption often does not hold; e.g. in recognition, class labels may be missing; in detection, objects in the image may not be localized; and in general, the labeling may be subjective. In this work we propose a generic way to handle noisy and incomplete labeling by augmenting the prediction objective with a notion of consistency. We consider a prediction consistent if the same prediction is made given similar percepts, where the notion of similarity is between deep network features computed from the input data. In experiments we demonstrate that our approach yields substantial robustness to label noise on several datasets. On MNIST handwritten digits, we show that our model is robust to label corruption. On the Toronto Face Database, we show that our model handles well the case of subjective labels in emotion recognition, achieving state-of-theart results, and can also benefit from unlabeled face images with no modification to our method. On the ILSVRC2014 detection challenge data, we show that our approach extends to very deep networks, high resolution images and structured outputs, and results in improved scalable detection.


The Type of Information Overload Affects Electronic Knowledge Repository continuance

In the present competitive organizational environment more organizations are implementing knowledge management initiatives to gain strategic advantage. One such initiative is that of implementing electronic knowledge repositories (EKR) which often leads to a rapid increase in the quantity of information employees must process daily, raising concerns of employees being overloaded. This is especially true for current EKRs using distributive technology, enabling customizable individual workspaces which can result in loose knowledge structures. This paper identifies a new type of information overload (IO), extending the concept as occurring in both knowledge seekers and contributors and uses cognitive dissonance theory to provide evidence that IO can change employees’ perception of EKR usage. This research paper provides the first empirical evidence that overload has no sufficient affect on EKR continuance intention directly, but has a significant negative affect on the two main success measures: Perceived usefulness and satisfaction of the system.


Learning to aggregate vertical results into web search results

Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting not only which verticals to present (the focus of most prior research), but also predicting where in the Web results to present them (i.e., above or below the Web results, or somewhere in between). Learning models to aggregate results from multiple verticals is associated with two major challenges. First, because verticals retrieve different types of results and address different search tasks, results from different verticals are associated with different types of predictive evidence (or features). Second, even when a feature is common across verticals, its predictiveness may be vertical-specific. Therefore, approaches to aggregating vertical results require handling an inconsistent feature representation across verticals, and, potentially, a vertical-specific relationship between features and relevance. We present 3 general approaches that address these challenges in different ways and compare their results across a set of 13 verticals and 1070 queries. We show that the best approaches are those that allow the learning algorithm to learn a vertical-specific relationship between features and relevance.


Clustering Validity Based on the Improved Hubert \Gamma Statistic and the Separation of Clusters

The validity of clustering is one important research field in clustering analysis, and many clustering validity functions have been proposed, especially those based on the geometrical structure of data set, such as Dunn's index and Xie-Beni index. In this way, the compactness and the separation of clusters are usually taken into account. Xie-Beni index decreases with the number of partitions increasing. It is difficult to choose the optimal number of clusters when there are lots of clusters in data. In this paper, a novel clustering validity function is proposed, which is based on the improved Huber Gamma statistic combined with the separation of clusters. Unlike other clustering validity, the function has the only maximum with the clustering number increasing. The experiments indicate that the function can be used as the optimal index for the choice of the clustering numbers


Advanced algorithms for neural networks: a C++ sourcebook


A Dual-Polarized Dual-Band Antenna With High Gain for 2G/3G/LTE Indoor Communications

A compact dual-polarized dual-band omnidirectional antenna with high gain is presented for 2G/3G/LTE communications, which comprise two horizontal polarization (HP) and a vertical polarization (VP) elements. The upper HP element consists of four pairs of modified printed magneto-electric (ME) dipoles that are fed by a four-way power divider feeding network, and eight pieces of arc-shaped parasitic patches that are printed on both sides of the circular printed circuit board alternately. The four-way power divider feeding network together with the four pairs of ME dipoles mainly provide a stable 360° radiation pattern and high gain, while the eight pieces of patches are used to enhance the bandwidth. The lower HP element is similar to the upper one except that it do not have the parasitic patches. The VP element consists of four pairs of cone-shaped patches. Different from the HP element, the upper VP element provides the lower frequency band while the lower VP one yields the upper frequency band. The VP element and the HP element are perpendicularly arranged to obtain the compact and dual-polarized features. Measured results show that a bandwidth of 39.6% (0.77–1.15 GHz) with a gain of about 2.6 dBi and another bandwidth of 55.3% (1.66–2.93 GHz) with a gain of about 4.5 dBi can be achieved for the HP direction, while a bandwidth of 128% (0.7–3.2 GHz) with a gain of around 4.4 dBi can be acquired for the VP direction. Port isolation larger than 20 dB and low-gain variation levels within 2 dBi are also obtained. Hence, the proposed antenna is suitable for 2G/3G/LTE indoor communications.


Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics

Retroviral capsid proteins are conserved structurally but assemble into different morphologies. The mature human immunodeficiency virus-1 (HIV-1) capsid is best described by a ‘fullerene cone’ model, in which hexamers of the capsid protein are linked to form a hexagonal surface lattice that is closed by incorporating 12 capsid-protein pentamers. HIV-1 capsid protein contains an amino-terminal domain (NTD) comprising seven α-helices and a β-hairpin, a carboxy-terminal domain (CTD) comprising four α-helices, and a flexible linker with a 310-helix connecting the two structural domains. Structures of the capsid-protein assembly units have been determined by X-ray crystallography; however, structural information regarding the assembled capsid and the contacts between the assembly units is incomplete. Here we report the cryo-electron microscopy structure of a tubular HIV-1 capsid-protein assembly at 8 Å resolution and the three-dimensional structure of a native HIV-1 core by cryo-electron tomography. The structure of the tubular assembly shows, at the three-fold interface, a three-helix bundle with critical hydrophobic interactions. Mutagenesis studies confirm that hydrophobic residues in the centre of the three-helix bundle are crucial for capsid assembly and stability, and for viral infectivity. The cryo-electron-microscopy structures enable modelling by large-scale molecular dynamics simulation, resulting in all-atom models for the hexamer-of-hexamer and pentamer-of-hexamer elements as well as for the entire capsid. Incorporation of pentamers results in closer trimer contacts and induces acute surface curvature. The complete atomic HIV-1 capsid model provides a platform for further studies of capsid function and for targeted pharmacological intervention.


Quantitative functional evaluation of a 3D-printed silicone-embedded prosthesis for partial hand amputation: A case report.

STUDY DESIGN
A male patient with partial hand amputation of his nondominant hand, with only stumps of the proximal phalanx of the first and fifth finger, was evaluated. The performance of using two alternative 3D printed silicone-embedded personalized prostheses was evaluated using the quantitative Jebsen Hand Function Test.

INTRODUCTION
Custom design and fabrication of 3D printed prostheses appears to be a good technique for improving the clinical treatment of patients with partial hand amputations. Despite its importance the literature shows an absence of studies reporting on quantitative functional evaluations of 3D printed hand prostheses.

PURPOSE OF THE STUDY
We aim at producing the first quantitative assessment of the impact of using 3D printed silicone-embedded prostheses that can be fabricated and customized within the clinical environment.

METHODS
Alginate molds and computed tomographic scans were taken from the patient's hand. Each candidate prosthesis was modeled in Computer Aided Design software and then fabricated using a combination of 3D printed parts and silicone-embedded components.

DISCUSSION
Incorporating the patient's feedback during the design loop was very important for obtaining a good aid on his work activities. Although the explored patient-centered design process still requires a multidisciplinary team, functional benefits are large.

CONCLUSION(S)
Quantitative data demonstrates better hand performance when using 3D printed silicone-embedded prosthesis vs not using any aid. The patient accomplished complex tasks such as driving a nail and opening plastic bags. This was impossible without the aid of produced prosthesis.


A new hybrid imperialist swarm-based optimization algorithm for university timetabling problems


Graphical Models for Probabilistic and Causal Reasoning

This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network. Figure 1 illustrates a simple yet typical Bayesian network. It describes the causal relationships among the season of the year (X1), whether rain falls (X2) during the season, whether the sprinkler is on (X3) during that season, whether the pavement would get wet (X4), and whether the pavement would be slippery (X5). All variables in this figure are binary, taking a value of either true or false, except the root variable X1 which can take one of four values: Spring, Summer, Fall, or Winter. Here, the absence of a direct link between X1 and X5, for example, captures our understanding that the influence of seasonal variations on the slipperiness of the pavement is mediated by other conditions (e.g., the wetness of the pavement). As this example illustrates, a Bayesian network constitutes a model of the environment rather than, as in many other knowledge representation schemes (e.g., logic, rule-based systems and neural networks), a model of the reasoning process. It simulates, in fact, the causal mechanisms that operate in the environment, and thus allows the investigator to answer a variety of queries, including: associational queries, such as “Having observed A, what can we expect of B?”; abductive queries, such as “What is the most plausible explanation for a given set of observations?”; and control queries; such as “What will happen if we intervene and act on the environment?” Answers to the first type of query depend only on probabilistic


Semantic portrait color transfer with internet images

We present a novel color transfer method for portraits by exploring their high-level semantic information. First, a database is set up which consists of a collection of portrait images download from the Internet, and each of them is manually segmented using image matting as a preprocessing step. Second, we search the database using Face++ to find the images with similar poses to a given source portrait image, and choose one satisfactory image from the results as the target. Third, we extract portrait foregrounds from both source and target images. Then, the system extracts the semantic information, such as faces, eyes, eyebrows, lips, teeth, etc., from the extracted foreground of the source using image matting algorithms. After that, we perform color transfer between corresponding parts with the same semantic information. We get the final transferred result by seamlessly compositing different parts together using alpha blending. Experimental results show that our semantics-driven approach can generate better color transfer results for portraits than previous methods and provide users a new means to retouch their portraits.


Real-Time Detection of Camera Tampering

This paper presents a novel technique for camera tampering detection. It is implemented in real-time and was developed for use in surveillance and security applications. This method identifies camera tampering by detecting large differences between older frames of video and more recent frames. A buffer of incoming video frames is kept and three different measures of image dissimilarity are used to compare the frames. After normalization, a set of conditions is tested to decide if camera tampering has occurred. The effects of adjusting the internal parameters of the algorithm are examined. The performance of this method is shown to be extremely favorable in real-world settings.


Effect of Methanol Addition on the Resistivity and Morphology of PEDOT:PSS Layers on Top of Carbon Nanotubes for Use as Flexible Electrodes.

UNLABELLED
Overcoating carbon nanotube (CNT) films on flexible poly(ethylene terephthalate) (PET) foils with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (

PEDOT
PSS) layers reduces the surface roughness, which is interesting for use in organic electronics. Adding methanol to the

PEDOT
PSS aqueous solution used for spin coating of the

PEDOT
PSS layer improves the wetting behavior of the CNT/PET surface. Samples with different volume fractions of methanol (0, 33, 50, 67, and 75 vol %) are compared with respect to the transmission, horizontal, and vertical resistivity. With grazing-incidence small-angle X-ray scattering, the film morphologies are probed, which is challenging because of the substrate flexibility. At 50 vol %, methanol optimum conditions are achieved with the resistivity close to that of the bare CNT/PET substrates because of the best contact between the

PEDOT
PSS film and CNT surface. At lower methanol ratios, the

PEDOT
PSS films cannot adapt the CNT morphology, and at higher methanol ratios, they rupture into domains and no continuous

PEDOT
PSS layers are formed.


Matching resumes and jobs based on relevance models

We investigate the difficult problem of matching semi-structured resumes and jobs in a large scale real-world collection. We compare standard approaches to Structured Relevance Models (SRM), an extensionof relevance-based language model for modeling and retrieving semi-structured documents. Preliminary experiments show that the SRM approach achieved promising performance and performed better than typical unstructured relevance models.


Business processes--attempts to find a definition

Definitions of business process given in much of the literature on Business Process Management are limited in depth and their related models of business processes are correspondingly constrained. After giving a brief history of the progress of business process modeling techniques from production systems to the office environment, this paper proposes that most definitions are based on machine metaphor type explorations of a process. While these techniques are often rich and illuminating it is suggested that they are too limited to express the true nature of business processes that need to develop and adapt to today’s challenging environment.


A multi-view deep learning architecture for classification of breast microcalcifications

In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. We describe a deep-learning classification method that is based on two view-level decisions, implemented by two neural networks, followed by a single-neuron layer that combines the viewlevel decisions into a global decision that mimics the biopsy results. Our method is evaluated on a large multi-view dataset extracted from the standardized digital database for screening mammography (DDSM). Experimental results show that our network structure significantly improves on previously suggested methods.


Four-Dimensional Discrete-time Lotka-Volterra Models with an Application to Ecology

This paper presents a study of the two-predators-two-preys discrete-time Lotka-Volterra model with selfinhibition terms for preys with direct applications to ecological problems. Parameters in the model are modified so that each of them has its own biological meaning, enabling more intuitive interpretation of biological conditions to the model . Moreover, the modified version is applicable to simulate a part of a large ecosystem, not only a closed predator-prey system with four species. An easy graphical method of analysis of the conditions on parameters ensuring long persistence under coexistence is presented. As an application, it is explained that a predator specie who feed on relatively small number of preys compared to the other predator species must be selective on the available preys in order for long persistence of the ecosystem. This may be regarded as a theoretical explanation of the existence of flush-pursuer birds, those who uses highly specialized hunting strategy and cross-adapts to the ecosystem relative to the ordinary bird species.


Time Series Forecasting Based on Augmented Long Short-Term Memory

In this paper, we use recurrent autoencoder model to predict the time series in single and multiple steps ahead. Previous prediction methods, such as recurrent neural network (RNN) and deep belief network (DBN) models, cannot learn long term dependencies. And conventional long short-term memory (LSTM) model doesn’t remember recent inputs. Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time. Based on comprehensive experiments, we show that the proposed methods significantly improves the state-of-art performance on chaotic time series benchmark and also has better performance on real-world data. Both single-output and multiple-output predictions are investigated.


Nasal base reduction by alar release: a laboratory evaluation.

BACKGROUND
When reducing the broad nasal base, there is a limit to the amount of soft tissue that can be resected, beyond which the anatomy distorts and the nostrils become stenotic (if resection enters the nostril). Alar mobilization by freeing soft-tissue attachments helps. This study purported to examine the nature of those attachments and the extent of medialization.

METHODS
The supporting tissues of the ala were sequentially divided in 16 fresh hemifacial cadavers. Key structures included the following: (1) the soft tissues and pyriform ligament of the anterior maxilla, (2) the periosteum posterior to the pyriform rim (in the bony nasal vault), and (3) the soft tissues along the horizontal pyriform rim. After release of each tethering region, the ala-pyriform distance was measured.

RESULTS
After releasing the anterior maxillary periosteum and pyriform ligament along the vertical pyriform rim, the ala-pyriform distance was reduced by a mean of 1.9 mm. After releasing the periosteum posterior to the pyriform rim (in the nasal vault), it was reduced by a mean of 1.7 mm. Releasing the soft tissues (which were thick medially) of the horizontal pyriform rim reduced the mean distance 1.0 mm for a total of 4.6 mm. Medialization resulting from anterior and posterior releases was significantly greater than that from the horizontal pyriform rim (p < 0.0006 and p < 0.015, respectively), but they were not significantly different from one another.

CONCLUSIONS
This cadaver study confirmed the role of the stabilizing effect of the pyriform ligament and the periosteum lateral and posterior to the pyriform rim. The total release was substantial, suggesting a clinical means of achieving tension-free alar medialization.


Deep Network Guided Proof Search

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go. Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification. Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved. Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.


Ontological Constructs to Create Money Laundering Schemes

There is an increasing tendency in the money laundering sector to utilize electronic commerce and web services. Misuse of web services and electronic money transfers occurs at many points in complex trading schemes. We provide ontological components that can be combined to construct some of these money laundering schemes. These constructs can be helpful for investigators, in order to decompose suspected financial schemes and recognize