Miracle-dz/UniFAR-FTUs huggingface dataset:
Onamd: An Online Android Malware Detection Approach

Android mobile phone is one of the most usable smartphone today which makes Android the most anticipated operating system in the market. However, the increase in malware threats with the growth in Android market cannot be neglected. Android is an open source platform which gives full leverage to developers on one side, but also provides an open door to malware. Dynamic analysis provides a comprehensive view of malware detection, but it is still subjective to the high-cost development and manual endeavors in test analysis. In this article, we propose a mechanism based on static and dynamic features for malware detection in Android. We leverage the proposed technique and develop a system, called an online Android Malware Detection Approach ONAMD. Initially, the ONAMD extracts the information (e.g., requested permissions, and basic data info, etc Next, it applies SVM and Random Forest algorithm that enhances the malware modeling capability to classify the application as benign or malicious. We applied our approach to 600 applications. The experimental result shows the efficiency of our approach which takes halftime and better recalls rate than Androguard.
Biohydrogen production by Thermoanaerobacterium thermosaccharolyticum KKU-ED1: Culture conditions optimization using mixed xylose/arabinose as substrate

Background: Biological hydrogen production by microorganisms can be divided into two main categories i.e. photosynthetic organisms that produce hydrogen using light as energy source and anaerobic bacteria that produce hydrogen via dark fermentation. Dark fermentative hydrogen production by anaerobic bacteria has the advantages of a higher HPR without illumination and of the capability to convert various kinds of substrate. Results: Thermophilic hydrogen producer was isolated from elephant dung and identified as Thermoanaerobacterium thermosaccharolyticum KKU-ED1 by 16S rRNA gene analysis, which was further used to produce hydrogen from mixed pentose sugar i.e., xylose/arabinose. The optimum conditions for hydrogen production from mixed xylose/arabinose by KKU-ED1 were a 1:1 xylose/arabinose mixture at the total concentration of 5 g/L, initial pH of 6.5 and temperature of 55oC. Under the optimum conditions, hydrogen from sugar derived from acid-hydrolyzed sugarcane bagasse at a reducing sugar concentration were achieved. Soluble metabolite product (SMP) was predominantly acetic acid indicating the acetate-type fermentation. Conclusions: The strain KKU-ED1 appeared to be a suitable candidate for thermophilic fermentative hydrogen production from hemicellulosic fraction of lignocellulosic materials due to its ability to use various types of carbon sources.
THE EFFECT OF HIGH DOSE OF GREEN TEA ON THERAPEUTIC ACTION OF GAMMA RADIATION IN LIVER CARCINOMA.

Rana Essam 2 , Fathia EL Sharkawi 1 , Mahmoud Shabon 2 and Hussien Khaled 3 . 1. Department of Biochemistry and Molecular Biology, Faculty of Pharmacy, Helwan University, Egypt. 2. Egyptian Nuclear and Radiological Regulatory Authority. 3. Medical Oncology Department, National Cancer Institute, Cairo University, Egypt. Manuscript Info Abstract . Manuscript History
Studies on Effect of Propagation Environment for Softwood Grafting in Guava (Psidium guajava L.) cv. Sardar

Guava is considered as one of the exquisite and nutritionally valuable fruit crops. It excels most other fruit crops in productivity, hardiness, adaptability and nutritive value. Besides its high nutritive value, it bears heavy crop every year and gives good economic returns involving very little input (Singh, 2007). Most of guava plantations in India are seedling origin. Traditionally, it is mostly propagated from seed (Zamir et al., 2003). However, plants raised from seeds are not true to type and eventually take longer time to reach to bearing stage. They are also propagated by layering, however it is combursome and has limitation for large scale and rapid multiplication. Softwood grafting, which is successfully practiced in fruit crops like mango, sapota cashew, jack fruit with good response can also be practice in guava. This method is easy, convenient in handling, involves simple skill and takes short period. The most important feature of the method is that, it allows higher rate of multiplication of plants and resultant plants will be vigorous. The propagation environment for grafting is one of the most important factors which plays a key role in success of grafting. The year round fluctuation in temperature, relative humidity and sunlight results in varying degree of success in softwood grafting in An experiment was conducted to study the on effect of different propagation environment for softwood grafting in guava cv. Sardar at the Department of Fruit Science, Kittur Rani Channamma College of Horticulture, Arabhavi, University of Horticultural Sciences, Bagalkot during the period of October 2012 to January 2013. There was significant difference for graft success and growth of grafts among different propagation environments. Significantly high graft success and survival percentage was noticed in mist house eco system (62.00 and 97.40 %, respectively) followed by shade house (48.00 and 95.00 % respectively). While minimum graft success and survival percentage was recorded under poly tunnel (10 and 41.67 %, respectively). Growth parameters like maximum number of sprouts (4.62) highest length of sprout (6.30 cm) and maximum number of leaves (21.60) were registered in grafts placed under mist house eco system. International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 6 (2017) pp. 2779-2783 Journal homepage: http://www.ijcmas. com
A novel FOREX prediction methodology based on fundamental data

Markets play a critical role in economics of the world and the distribution of wealth. Predicting them can help with preventing crashes and avoiding severe losses, or making significant profits. But such prediction is not easy due to the very complex nature of markets and the wide variety of the influence factors involved. Technical analysts or chartists rely on historical chart data to predict patterns based on previous behaviours of graphs. This approach is fairly straightforward and has also been automated to a great extent. There are computer programs or predictor robots that use the technical approach and facilitate buy or sell decisions. However, market behaviour obviously is more than repetition of old patterns and many of the events in the outside world have constant impacts on it. These external pieces of information can vary from political events to economic statistics. Fundamental analysts are those with a knowledge and understanding of the world events on market behaviour. This requires knowledge of politics, micro and macroeconomics to say the least, and hence, there are far fewer of such analysts. However, the very successful analysts like Warren Buffet have repeatedly emphasized on consideration of fundamental data in prediction calculations. Nevertheless, proper fundamental analysis remains to be a challenge and even a bigger challenge when it comes to its automation. There are very few research efforts and approaches which look into possibilities of automation of fundamental analysis. Hence, this work initiated a novel approach on fundamental data manipulation for identification of relationships between market behaviour and external information. This work made an effort to apply the afore in the foreign exchange market by observing the USD/GBP currency pair. In this research, an approach was devised and proposed for integration of fundamental data into automatic prediction. In this approach 3 main sources for fundamental data were identified. From these sources, data was extracted, organized and then fed into a proposed neural network during 6 experiments. The experiments put the possible relationships between the identified fundamental data and the price movements of the chosen currency pair (USD/GBP) to test. The test results indentified the datasets with plausible relationships with the market behaviour. The observed positive output of 3 different sets of data-input proved the proposed methodology to be of considerable value for market prediction. Key words: Foreign exchange market prediction, stock market prediction, neural networks, fundamental analysis, market behaviour.
Factors influencing the market for branded mass customized buildings

The concept of mass customisation is not new yet the UK construction industry has yet to grasp this opportunity to deliver greater value to its customers. The government report Rethinking Construction [Egan 1998] clearly identifies this issue: ‘ We have repeatedly heard the claim that construction is different from manufacturing because every product is unique. We do not agree. Not only are many buildings such as houses, essentially repeat products which can be continually improved but, more importantly, the process of construction is itself repeated in its essentials from project to project. ’ Egan delivered this report in 1998 but CLASP, for example, highlighted the advantages of standardisation in 1959 in the conclusions to their Annual Report [CLASP 1959]:’The consortium is now an established and powerful force in building, responsible for a significant number of the country’s new schools as well as for a growing number of other public buildings. The second year of operations has confirmed that the consortium with its big orders and its design resources, is the kind of organization most capable of realizing the full economic advantage of factory production methods. It leads therefore towards the more enlightened building industry for which we all strive. ’ A review of government funded construction reports between 1944-98 [Murray 2003] emphasises the continued presence of these recurring themes in appraisal of the construction process. The opportunity is seemingly clear. Designing and constructing from scratch, each time a client requires building infrastructure, is wasteful and inefficient. A radical market change is needed where built environment customers experience much greater certainty and value whilst retaining choice, and at the same time enabling constructors to improve their profit margins by sharing the rewards of jointly maximising value. This vision requires the replacement of a significant portion of the current bespoke market for the design, delivery and procurement of non-residential buildings with a combination of standardised and customised product offerings. This paper details information obtained to date from an ongoing IMCRC funded study entitled ‘Building the Brand’ at Loughborough University.
Speech-to-speech translation : a massively parallel memory-based approach

List of Figures. List of Tables. Preface. 1. Introduction. 2. Current Research toward Speech-to-Speech Translation. 3. Design Philosophy behind the DMDIALOG System. 4. The DMDIALOG System. 5. DMSNAP: an Implementation on the SNAP Semantic Network Array Processor. 6. ASTRAL: an Implementation on the ISM2 Associative Memory Processor. 7. Memoir: an Alternative View. 8. Conclusion. Index.
Bootstrapping

Part I. Premises: 1. A problematic picture of the personal interface 2. Social change and networks Part II. The Prehistory of the Laboratory: 3. Douglas C. Engelbart and the ARPA community 4. The augmentation framework and the relativist tradition Part III. Kinaesthetics and the Hypertextual Piano: Feeling the Code: 5. From physico-motor skills to kinaesthetic communication 6. The mouse is more than a pointing device Part IV. The Social Construction of the Personal Interface: 7. The beginnings of the hypermedium 8. The genesis of the graphic interface 9. The (inter)personal interface Part V. Coda: 10. When hand and memory meet again.
Improving Case Based Software Effort Estimation by an Ant Colony Algorithm

Predicting accurate efforts at the early stages of the Software Life Cycle is one of the greatest challenges in software industry. Effort estimation remains a very difficult task since the software requirements are not well known and understood/or the details have not yet been specified. Thus, most of software effort estimation models are built on using historical project data. For this purpose, CBR based prediction models have been extensively used. In CBR techniques, choosing the most similar case has a strong impact on the prediction accuracy, nevertheless, it remains a hard task especially on the presence of alternatives projects; projects that are equally similar. This paper proposed a new method to select the most appropriate case by using ant colony optimization algorithm. The ACO algorithm will be used to search for the best case of past cases based on the different features similarity values (FR_similarity, NFRs_similarity and DPs_similarity). To verify the efficiency and performance of the proposed model, an example was conducted and the results were compared with that of the real estimation. The results of the example show that proposed model in this paper is an attractive alternative to retrieve the most appropriate case and is useful and beneficial for decision making during the effort estimation.
Record Duplication Detection in Database: A Review

The recognition of similar entities in databases has gained substantial attention in many application areas. Despite several techniques proposed to recognize and locate duplication of database records, there is a dearth of studies available which rate the effectiveness of the diverse techniques used for duplicate record detection. Calculating time complexity of the proposed methods reveals their performance rating. The time complexity calculation showed that the efficiency of these methods improved when blocking and windowing is applied. Some domain-specific methods train systems to optimize results and improve efficiency and scalability, but they are prone to errors. Most of the existing methods fail to either discuss, or lack thoroughness in consideration of scalability. The process of sorting and searching form an essential part of duplication detection, but they are time-consuming. Therefore this paper proposes the possibility of eliminating the sorting process by utilization of tree structure to improve the record duplication detection. This has added benefits of reducing time required, and offers a probable increase in scalability. For database system, scalability is an inherent feature for any proposed solution, due to the fact that the data size is huge. Improving the efficiency in identifying duplicate records in databases is an essential step for data cleaning and data integration methods. This paper reveals that the current proposed methods lack in providing solutions that are scalable, high accurate, and reduce the processing time during detecting duplication of records in database. The ability to provide solutions to this problem will improve the quality of data that are used for decision making process.
Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions inﬂuenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model typical behaviour and its deviations on theWeb.The website itself, together with transaction server logs, includes information about the site structure, content and about the actual user actions (clicks) within the site. User actions logically reﬂect the behaviour, while other sources indicate his/her context. Combination of these data sources allows to model the typical user behaviour and his/her preferences. The long-term behaviour describes relatively stable user preferences based on extensive user history. As the Web has become more and more dynamic, modelling user behaviour from the long-term perspective does not satisfy requirements of current Web based applications. On the other side, the short-term behaviour describes current user activity and his/her actual intent. However, this source of information is often noisy. To address these shortcomings the state-of-the-art combines both perspectives, which allows to meaningful and timely modelling of user behaviour. In this paper, we provide a comprehensive survey Journal of
Body shrinkage due to Arctic warming reduces red knot fitness in tropical wintering range

Consequences conferred at a distance Migratory animals have adapted to life in multiple, sometimes very different environments. Thus, they may show particularly complex responses as climates rapidly change. Van Gils et al. show that body size in red knot birds has been decreasing as their Arctic breeding ground warms (see the Perspective by Wikelski and Tertitski). However, the real toll of this change appears not in the rapidly changing northern part of their range but in the apparently more stable tropical wintering range. The resulting smaller, short-billed birds have difficulty reaching their major food source, deeply buried mollusks, which decreases the survival of birds born during particularly warm years. Science, this issue p. 819; see also p. 775 A warming Arctic decreases the fitness of migratory red knots in their distant wintering range. Reductions in body size are increasingly being identified as a response to climate warming. Here we present evidence for a case of such body shrinkage, potentially due to malnutrition in early life. We show that an avian long-distance migrant (red knot, Calidris canutus canutus), which is experiencing globally unrivaled warming rates at its high-Arctic breeding grounds, produces smaller offspring with shorter bills during summers with early snowmelt. This has consequences half a world away at their tropical wintering grounds, where shorter-billed individuals have reduced survival rates. This is associated with these molluscivores eating fewer deeply buried bivalve prey and more shallowly buried seagrass rhizomes. We suggest that seasonal migrants can experience reduced fitness at one end of their range as a result of a changing climate at the other end.
Using Publisher Partisanship for Partisan News Detection

News is the main source of information about events in our neighborhood and around the globe. In an era of digital news, where sources vary in quality and news spreads fast, it is critical to understand what is being consumed by the public. Partisan news is news that favors certain political parties or ideologies. It is undesirable because news organization should aim for an objective and balanced reporting. An automatic system that can classify news to be partisan or non-partisan is thus desired. Such a system (partisan news detector) requires a sufficient amount of data to learn the pattern of partisanship in news articles. However, these labels are expensive to collect since manual inspection of each article is required. Inferring partisanship labels from the partisanship of publishers is another approach that has been used in previous research. By treating all articles by partisan publishers to be partisan news and those by non-partisan publishers to be non-partisan, it is easy to collect a large number of labeled articles. This way of deciding labels is noisy, making it more difficult for a detector to learn. This thesis compared the performance of using publisher-level labels and articlelevel labels for partisan news detection. The detector was designed as a binary classifier. We compared the difference in performance across several feature sets to ensure the observed difference was due to the annotation level, not the choice of specific classifiers. The experiments were performed on two datasets of different properties to ensure the generalizability of the results. We found that classifiers trained with publisher-level labels have higher recall but lower F1-score compared to classifiers trained with articlelevel labels. We also observed that the classifiers overfit on publishers but reducing the overfitting with feature selection did not improve the performance. Comparing the performance difference between the two datasets, we concluded that an important factor that determines the performance achievable by the publisher-level labels is the quality of publishers that are included in the dataset. This is valuable for future dataset collection. Our work provides a benchmark performance of publisher-level labels, which can be used as baselines for future research that investigate other methodologies to utilize the noisy labels. We also compared the performance between the two levels of annotation and concluded that partisan news detectors trained with article-level labels are more practical to be used in a fully-automated system since they have on average 10% higher F1-scores than those trained with publisher-level labels. However, the high recall of the latter makes them applicable in use cases where high recall is desired.
Optimal Planning of Solar PV Using Simple Model for New Feed-in Tariff in Indonesia

Indonesia is still struggling to attract the investor to involve in solar photovoltaic (PV) project. There is a rapid transformation in PV regulation by the government. Four regulation related to solar PV is delivered from 2016 until 2018. In 2018, the feed-in tariff regulation was established. The feed-in tariff aims the electricity customer to install the PV system and inject the excess energy to the grid. However, this rapid regulation changing is not yet attracting developer nor customer to invest in the solar PV. This paper proposes a simple model to calculate the optimal size of PV for new feed-in tariff regulation in Indonesia. Feed-in tariff regulation allows the customer to inject the energy produced by PV to the grid. The regulation provides 65% compensation of electricity price from energy sent to the grid with several limitations. There is difficulty to estimate daily energy data. Then, a technical calculation is proposed by using a simple model so that it can help the customer in sizing the solar PV system. Adding to the proposal of the methodology, this research also develops an application to help customers in calculating the optimal size of solar PV and its profitability. The new feed-in tariff rule will be an attraction for the customer. However, it needs a comprehensive calculation so that solar PV can be profitable. Oversized PV systems can cause financial losses with the large investment and limitation on the calculation of energy injected into the grid. The result shows that the PV simple model is successfully developed to help the consumer obtain the optimal PV size.