In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious and evolving security threats to Internet users. To protect legitimate users from these threats, ...anti-malware software products from different companies, including Comodo, Kaspersky, Kingsoft, and Symantec, provide the major defense against malware. Unfortunately, driven by the economic benefits, the number of new malware samples has explosively increased: anti-malware vendors are now confronted with millions of potential malware samples per year. In order to keep on combating the increase in malware samples, there is an urgent need to develop intelligent methods for effective and efficient malware detection from the real and large daily sample collection. In this article, we first provide a brief overview on malware as well as the anti-malware industry, and present the industrial needs on malware detection. We then survey intelligent malware detection methods. In these methods, the process of detection is usually divided into two stages:
feature extraction
and
classification/clustering
. The performance of such intelligent malware detection approaches critically depend on the extracted features and the methods for classification/clustering. We provide a comprehensive investigation on both the feature extraction and the classification/clustering techniques. We also discuss the additional issues and the challenges of malware detection using data mining techniques and finally forecast the trends of malware development.
With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting ...different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.
A Survey on Deep Learning Pouyanfar, Samira; Sadiq, Saad; Yan, Yilin ...
ACM computing surveys,
09/2019, Volume:
51, Issue:
5
Journal Article
Peer reviewed
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build ...computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.
•A multi-time-scale approach is proposed for electric power demand forecasting.•Historical load is modeled as a time-series with multiple seasonality levels.•Each seasonal cycle of load data is ...studied without additional input.•Akaike/Bayesian information criteria is used for accuracy evaluation.•Box-Jenkins method is enhanced for modeling the load data over a time period.
Short-term load forecasting is essential for reliable and economic operation of power systems. Short-term forecasting covers a range of predictions from a fraction of an hour-ahead to a day-ahead forecasting. An accurate load forecast results in establishing appropriate operational practices and bidding strategies, as well as scheduling adequate energy transactions. This paper presents a generalized technique for modeling historical load data in the form of time-series with different cycles of seasonality (e.g., daily, weekly, quarterly, annually) in a given power network. The proposed method separately models both non-seasonal and seasonal cycles of the load data using auto-regressive (AR) and moving-average (MA) components, which only rely on historical load data without requiring any additional inputs such as historical weather data (which might not be available in most cases). The accuracy of data modeling is examined using the Akaike/Bayesian information criteria (AIC/BIC) which are two effective quantification methods for evaluation of data forecasting. In order to validate the effectiveness and accuracy of the proposed forecaster, we use the hourly-metered load data of PJM network as a real-world input dataset.
Studies evaluating acceptability of simplified follow-up after medical abortion have focused on high-resource or urban settings where telephones, road connections, and modes of transport are ...available and where women have formal education.
To investigate women's acceptability of home-assessment of abortion and whether acceptability of medical abortion differs by in-clinic or home-assessment of abortion outcome in a low-resource setting in India.
Secondary outcome of a randomised, controlled, non-inferiority trial.
Outpatient primary health care clinics in rural and urban Rajasthan, India.
Women were eligible if they sought abortion with a gestation up to 9 weeks, lived within defined study area and agreed to follow-up. Women were ineligible if they had known contraindications to medical abortion, haemoglobin < 85 mg/l and were below 18 years.
Abortion outcome assessment through routine clinic follow-up by a doctor was compared with home-assessment using a low-sensitivity pregnancy test and a pictorial instruction sheet. A computerized random number generator generated the randomisation sequence (1:1) in blocks of six. Research assistants randomly allocated eligible women who opted for medical abortion (mifepristone and misoprostol), using opaque sealed envelopes. Blinding during outcome assessment was not possible.
Women's acceptability of home-assessment was measured as future preference of follow-up. Overall satisfaction, expectations, and comparison with previous abortion experiences were compared between study groups.
731 women were randomized to the clinic follow-up group (n = 353) or home-assessment group (n = 378). 623 (85%) women were successfully followed up, of those 597 (96%) were satisfied and 592 (95%) found the abortion better or as expected, with no difference between study groups. The majority, 355 (57%) women, preferred home-assessment in the event of a future abortion. Significantly more women, 284 (82%), in the home-assessment group preferred home-assessment in the future, as compared with 188 (70%) of women in the clinic follow-up group, who preferred clinic follow-up in the future (p < 0.001).
Home-assessment is highly acceptable among women in low-resource, and rural, settings. The choice to follow-up an early medical abortion according to women's preference should be offered to foster women's reproductive autonomy.
ClinicalTrials.gov NCT01827995.
The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health ...data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.
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