snu-aidas/HVMap-dataset huggingface dataset:
Applying machine learning to software fault-proneness prediction

The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module’s fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA’s Metrics Data Program data repository. 2007 Elsevier Inc. All rights reserved.
Fashioning with Networks: Neural Style Transfer to Design Clothes

Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how the style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a user’s preference and by learning the user’s fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how well they align with the user’s fashion style.
Affective Deprivation Disorder : Does it Constitute a Relational Disorder ?

Harriet Simons is Adjunct Associate Professor at Smith College School for Social Work and runs a private social work practice for individuals and couples specializing in infertility and Asperger’s relationships. Among her published works are Wanting Another Child: Coping with Secondary Infertility. Jossey-Bass, 1998; (Contributor), Infertility Counseling: A Handbook for Clinicians. Parthenon Press, 1997; and (Co-editor and Contributor) Infertility: Medical, Emotional and Social Considerations. Human Sciences Press, 1984. Jason Thompson is Community Support Officer at SPARAL Disability Services specializing in rehabilitation and lifestyle support for individuals with intellectual and physical disabilities. He has completed certified studies in Applied Behaviour Analysis and Normative Male Alexithymia. He recently published an essay on the subject of alexithymia Alexithymia: An Imaginative Approach, Psychotherapy Australia Journal, vol 14, No 4, Aug. 2008
Estimation of ECG features using LabVIEW

320 Abstract—Various methods have been proposed and used for ECG feature extraction with considerable percentage of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs. In this work we have developed an algorithm based on wavelet transform approach using LabVIEW 8.6. LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a graphical programming language that uses icons instead of lines of text to create programs. Unlike text based programming language, LabVIEW uses the data flow programming, where the flow of data determines execution. The flexibility, modular nature and ease to use programming possible with labview, makes it less complex. The proposed algorithm is executed in two stages. Firstly, it preprocesses (denoises) the signal to remove the noise from the ECG signal. Then it detects P-wave, T-wave, QRS complex and their onsets and offsets. LabVIEW and the related toolkits, advanced signal processing toolkit and math script are used to build the graphical programme for both the stages. The algorithm is evaluated with ECG data files, with different ECG morphologies and noise content taken from CSE multilead data base which contains 5000 samples per ECG, recorded at a sampling frequency of 500Hz.
System-level fault-tolerance in large-scale parallel machines with buffered coscheduling

Summary form only given. As the number of processors for multiteraflop systems grows to tens of thousands, with proposed petaflops systems likely to contain hundreds of thousands of processors, the assumption of fully reliable hardware has been abandoned. Although the mean time between failures for the individual components can be very high, the large total component count will inevitably lead to frequent failures. It is therefore of paramount importance to develop new software solutions to deal with the unavoidable reality of hardware faults. We will first describe the nature of the failures of current large-scale machines, and extrapolate these results to future machines. Based on this preliminary analysis we will present a new technology that we are currently developing, buffered coscheduling, which seeks to implement fault tolerance at the operating system level. Major design goals include dynamic reallocation of resources to allow continuing execution in the presence of hardware failures, very high scalability, high efficiency (low overhead), and transparency - requiring no changes to user applications. Preliminary results show that this is attainable with current hardware.
ETS gene fusions in prostate cancer: from discovery to daily clinical practice.

CONTEXT
In 2005, fusions between the androgen-regulated transmembrane protease serine 2 gene, TMPRSS2, and E twenty-six (ETS) transcription factors were discovered in prostate cancer.


OBJECTIVE
To review advances in our understanding of ETS gene fusions, focusing on challenges affecting translation to clinical application.


EVIDENCE ACQUISITION
The PubMed database was searched for reports on ETS fusions in prostate cancer.


EVIDENCE SYNTHESIS
Since the discovery of ETS fusions, novel 5' and 3' fusion partners and multiple splice isoforms have been reported. The most common fusion, TMPRSS2:ERG, is present in approximately 50% of prostate-specific antigen (PSA)-screened localized prostate cancers and in 15-35% of population-based cohorts. ETS fusions can be detected noninvasively in the urine of men with prostate cancer, with a specificity rate in PSA-screened cohorts of >90%. Reports from untreated population-based cohorts suggest an association between ETS fusions and cancer-specific death and metastatic spread. In retrospective prostatectomy cohorts, conflicting results have been published regarding associations between ETS fusions and cancer aggressiveness. In addition to serving as a potential biomarker, tissue and functional studies suggest a specific role for ETS fusions in the transition to carcinoma. Finally, recent results suggest that the 5' and 3' ends of ETS fusions as well as downstream targets may be targeted therapeutically.


CONCLUSIONS
Recent studies suggest that the first clinical applications of ETS fusions are likely to be in noninvasive detection of prostate cancer and in aiding with difficult diagnostic cases. Additional studies are needed to clarify the association between gene fusions and cancer aggressiveness, particularly those studies that take into account the multifocal and heterogeneous nature of localized prostate cancer. Multiple promising strategies have been identified to potentially target ETS fusions. Together, these results suggest that ETS fusions will affect multiple aspects of prostate cancer diagnosis and management.
A Prospective, Open-Label Study of Hyaluronic Acid-Based Filler With Lidocaine (VYC-15L) Treatment for the Correction of Infraorbital Skin Depressions.

BACKGROUND
Infraorbital skin depressions are one of the most troublesome facial areas for aesthetically aware patients.


OBJECTIVE
Evaluate effectiveness and safety of Juvéderm Volbella with Lidocaine (VYC-15L; Allergan plc, Dublin, Ireland) for correction of bilateral infraorbital depressions.


METHODS
In this 12-month, prospective, uncontrolled, open-label study, subjects aged ≥18 years with infraorbital depressions rated ≥1 on the Allergan Infra-oRbital Scale (AIRS) received injections of VYC-15L with optional touch-up treatment on Day 14. The primary efficacy measure was ≥1 AIRS grade improvement from baseline at month 1.


RESULTS
Of 80 subjects initially treated with VYC-15L, 75 (94%) completed the study. All injections were intentionally deep, most using multiple microbolus technique. At 1 month, 99.3% of eyes achieved ≥1 AIRS grade improvement. The responder rate (subjects with ≥1 AIRS grade improvement in both eyes) was 99% at month 1, 92% at month 6, and 54% at month 12. Most injection site reactions (e.g., bruising, redness, irregularities/bumps) were mild and resolved by day 14. Late-onset mild to moderate edema was observed in 11% of eyes at month 6% and 4% of eyes at month 12.


CONCLUSION
VYC-15L is effective and safe for the treatment of infraorbital depressions, with effectiveness lasting up to 12 months.
RoboCup: The Robot World Cup Initiative

The Robot World Cup Initiative (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. The rst RoboCup competition will be held at IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor-fusion. Unlike AAAI robot competition, which is tuned for a single heavyduty slow-moving robot, RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's nal target is a world cup with real robots, RoboCup o ers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment.
Tensor2Tensor for Neural Machine Translation

Tensor2Tensor is a library for deep learning models that is very well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model. 1 Neural Machine Translation Background Machine translation using deep neural networks achieved great success with sequence-tosequence models Sutskever et al. (2014); Bahdanau et al. (2014); Cho et al. (2014) that used recurrent neural networks (RNNs) with LSTM cells Hochreiter and Schmidhuber (1997). The basic sequence-to-sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed-sized state vector. This is followed by an RNN decoder, which generates the target sentence, one token at a time, from the state vector. While a pure sequence-to-sequence recurrent neural network can already obtain good translation results Sutskever et al. (2014); Cho et al. (2014), it suffers from the fact that the whole input sentence needs to be encoded into a single fixed-size vector. This clearly manifests itself in the degradation of translation quality on longer sentences and was partially overcome in Bahdanau et al. (2014) by using a neural model of attention. Convolutional architectures have been used to obtain good results in word-level neural machine translation starting from Kalchbrenner and Blunsom (2013) and later in Meng et al. (2015). These early models used a standard RNN on top of the convolution to generate the output, which creates a bottleneck and hurts performance. Fully convolutional neural machine translation without this bottleneck was first achieved in Kaiser and Bengio (2016) and Kalchbrenner et al. (2016). The model in Kaiser and Bengio (2016) (Extended Neural GPU) used a recurrent stack of gated convolutional layers, while the model in Kalchbrenner et al. (2016) (ByteNet) did away with recursion and used left-padded convolutions in the decoder. This idea, introduced in WaveNet van den Oord et al. (2016), significantly improves efficiency of the model. The same technique was improved in a number of neural translation models recently, including Gehring et al. (2017) and Kaiser et al. (2017).
To See or not to See – Innovative Display Technologies as Enablers for Ergonomic Cockpit Concepts Ergonomic requirements future mobility future functionality

Introduction Given the fact that the driving task will remarkably change by the increase of assistance and information systems in the future innovative display technologies play a central role for the ergonomic realization of the driver vehicle interaction. Increasing assistance and even automation of the driving task do not lead to a decrease or disappearance of visual information but instead request new and in some cases revolutionary concepts to close the loop between driver, vehicle and traffic environment. Augmenting information in contact analog head-up-displays for navigation and driver assistance is a very promising approach. Replacement of mirrors via camera monitor systems is a further example. Free programmable cluster instruments in combination with HUD promise to resolve the problem of information density produced by an increasing amount of ADAS and IVIS functionality.
A study of deep convolutional auto-encoders for anomaly detection in videos

The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and challenges. Convolutional Neural Networks have achieved the state-of-the-art performance for object recognition in recent years, since they learn features automatically during the training process. From the anomaly detection perspective, the Convolutional Autoencoder (CAE) is an interesting choice, since it captures the 2D structure in image sequences during the learning process. This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score. By exploring the CAE architecture, we also propose a method for aggregating high-level spatial and temporal features with the input frames and investigate how they affect the CAE performance. An easy-to-use measure of video spatial complexity was devised and correlated with the classification performance of the CAE. The proposed methods were evaluated by means of several experiments with public-domain datasets. The promising results support further research in this area. © 2017 Published by Elsevier B.V.
A client-side analysis of TLS usage in mobile apps

As mobile applications become more pervasive, they provide us with a variety of online services that range from social networking to banking and credit card management. Since many of these services involve communicating and handling of private user information – and also due to increasing security demands from users – the use of TLS connections has become a necessity for today’s mobile applications. However, an improper use of TLS and failure to adhere to TLS security guidelines by app developers, exposes users to agents performing TLS interception thus giving them a false sense of security. Unfortunately, researchers and users alike lack of information and easy-to-deploy mechanisms to analyze how securely mobile apps implement TLS. Hence, in order to understand and assess the security of mobile app communications, it is crucial to study their use of the TLS protocol. In this poster we present a method to study the use of TLS in mobile apps using the data provided by the ICSI Haystack app [2], a mobile measurement platform that enables on-device analysis of mobile traffic without requiring root access. The unique vantage point provided by the Haystack platform enables a variety of measurements from the edge of the network with real user workload and the added bonus of having contextual information on the device to supplement the data collection.
Enzyme-Enhanced Microbial Fuel Cells
Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence

Introduction Breast cancer (BC) is the most common cancer in women, affecting about 10% of all women at some stages of their life. In recent years, the incidence rate keeps increasing and data show that the survival rate is 88% after five years from diagnosis and 80% after 10 years from diagnosis [1]. Early prediction of breast cancer is one of the most crucial works in the follow-up process. Data mining methods can help to reduce the number of false positive and false negative decisions [2,3]. Consequently, new methods such as knowledge discovery in databases (KDD) has become a popular research tool for medical researchers who try to identify and exploit patterns and relationships among large number of variables, and predict the outcome of a disease using historical cases stored in datasets [4].
Growers versus showers: a meaningful (or useful) distinction?

Yafi et al. have conducted a study that will be of great interest to the lay community and also of import to practicing urologists who routinely encounter patients with concerns about the appearance of their phallus [1]. In one study 14% of men expressed dissatisfaction with their genitals with flaccid penile length being the most common source of dissatisfaction [2]. The concerns of such men tend to be the perception that their penis is small, or at least smaller than the penises of other men.
LIBOL: a library for online learning algorithms

LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large-scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers, and also have made comprehensive documents available for both beginners and advanced users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform for conducting online learning research.
Survey propagation: An algorithm for satisfiability

We study the satisfiability of randomly generated formulas formed by M clauses of exactly K literals over N Boolean variables. For a given value of N the problem is known to be most difficult when α = M/N is close to the experimental threshold αc separating the region where almost all formulas are SAT from the region where all formulas are UNSAT. Recent results from a statistical physics analysis suggest that the difficulty is related to the existence of a clustering phenomenon of the solutions when α is close to (but smaller than) αc. We introduce a new type of message passing algorithm which allows to find efficiently a satisfying assignment of the variables in this difficult region. This algorithm is iterative and composed of two main parts. The first is a message-passing procedure which generalizes the usual methods like Sum-Product or Belief Propagation: It passes messages that may be thought of as surveys over clusters of the ordinary messages. The second part uses the detailed probabilistic information obtained from the surveys in order to fix variables and simplify the problem. Eventually, the simplified problem that remains is solved by a conventional heuristic. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 27, 201–226, 2005
Arabic Script Recognition : A Survey

Optical character recognition (OCR) is essential in various real-world applications, such as digitizing learning resources to assist visually impaired people and transforming printed resources into electronic media. However, the development of OCR for printed Arabic script is a challenging task. These challenges are due to the specific characteristics of Arabic script. Therefore, different methods have been proposed for developing Arabic OCR systems, and this paper aims to provide a comprehensive review of these methods. This paper also discusses relevant issues of printed Arabic OCR including the challenges of printed Arabic script and performance evaluation. It concludes with a discussion of the current status of printed Arabic OCR, analyzing the remaining problems in the field of printed Arabic OCR and providing several directions for future research. Keywords—Optical character recognition; arabic printed OCR; arabic text recognition; arabic OCR survey; feature extraction; segmentation; classification
Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?

Haxby et al. [Science 293 (2001) 2425] recently argued that category-related responses in the ventral temporal (VT) lobe during visual object identification were overlapping and distributed in topography. This observation contrasts with prevailing views that object codes are focal and localized to specific areas such as the fusiform and parahippocampal gyri. We provide a critical test of Haxby's hypothesis using a neural network (NN) classifier that can detect more general topographic representations and achieves 83% correct generalization performance on patterns of voxel responses in out-of-sample tests. Using voxel-wise sensitivity analysis we show that substantially the same VT lobe voxels contribute to the classification of all object categories, suggesting the code is combinatorial. Moreover, we found no evidence for local single category representations. The neural network representations of the voxel codes were sensitive to both category and superordinate level features that were only available implicitly in the object categories.
The happy and unhappy faces of narcissism

Several theorists have argued in favor of a distinction between overt and covert narcissism, and factor analytic studies have supported this distinction. In this paper I demonstrate that overt narcissists report higher self-esteem and higher satisfaction with life, whereas covert narcissists report lower self-esteem and lower satisfaction with life. I also present mediational models to explain why overt narcissists are relatively happy and covert narcissists are relatively unhappy. In analyses using both partial correlations and structural equation modeling, self-esteem consistently mediated the associations between both types of narcissism and happiness, whereas self-deception did not. These results further demonstrate some of the selfcentered benefits associated with overt narcissism and some of the strong psychological costs associated with covert narcissism. # 2002 Elsevier Science Ltd. All rights reserved.
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