Bad Droid! An in-depth empirical study on the occurrence and impact of Android specific code smells

Knowing the impact of bad programming practices or code smells has led researchers to conduct numerous studies in software maintenance. Most of the studies have defined code smells as bad practices that may affect the quality of the software. However, most of the existing research is heavily focused on detecting traditional code smells and less focused on mobile application specific Android code smells. Presently, there is a few papers that focus on android code smells - a catalog for Android code smells. This catalog defines 30 Android specific code smell that may impact maintainability of an app. In this research, we plan to introduce a detector tool called \textit{BadDroidDetector} for Android code smells that can detect 13 code smells from the catalog. We will also conduct an empirical study to know the distribution of 13 smell that we detect and know the severity of these smells

Contribution of Onion Seed Production to Poverty Reduction: A Case Study of Malakand Division, Pakistan

According to the latest estimates, roughly one-third of the total population of the developing countries live in poverty, majority of which are rural inhabitants (as reported 35 percent of the Pakistani rural mass). In Pakistan, the income distribution has worsened in the rural areas while it has marginally improved in urban areas during the period 1979 through 1996-97 [Pakistan (2001)]. The rural poverty is continuously feeding unemployment through migration of unskilled people to the urban areas. Poverty reduction is a priority area for Pakistan. The government is taking measures for addressing problems of the poor who are the most vulnerable amongst the different socioeconomic groups. Poverty alleviation is the main focus of the government in addition to develop physical infrastructure in rural areas and remove income disparities between income groups and regions. The government of Pakistan has initiated measures to poverty reduction through establishing number of institutions namely Pakistan Poverty Alleviation Fund, Micro-credit Bank (Khushali Bank), Pakistan Baitual Mal, Income Safety Nets, and launching Khushal Pakistan Programme and Food Support Programme. All these programmes are aiming at helping poor and hungry people by providing them food for temporary relief and micro credit for initiating sustainable economic activities. Since the majority of our population is living in rural areas, so the government is diverting more resources to improve the access for rural services and encourage greater participation in economic activities through creating employment opportunities. The programmes in education, health and population sectors have been specifically designed to extend socioeconomic opportunities to rural poor.

Physiological responses of seven varieties of soybean [Glycine max (L.) Merr.] to salt stress

In agriculture, salinity is one of the most significant abiotic stresses that plants confront and harms agricultural productivity, physiological, growth and development processes. In the present study, there were 7 different varieties of soybean (Ajmeri, William-82, D.A, PSC-60, Rawal-1, NARC-1 and NARC-2,) were tested under NaCl concentration level (0 mM and 150 mM) to determine their physiological performance under control and experimental conditions. The present investigation aimed to select salt tolerant varieties. Under salt stress, different varieties have differed significantly in the biological yield, chlorophyll contents, antioxidant activity and ionic concentrations. The results showed that among the seven varieties evaluated NARC-1 and NARC-2 are producing higher biological yield and antioxidant activity than others under 150 mM NaCl. NARC-1 and NARC-2 under 150 mM NaCl concentration produced significantly higher biomass in comparison with other varieties and similarly enhance the antioxidant activity by decreasing the catalase activity. The relative water content (RWC) of plants was measured 15, 30, 45 and 60 days after the treatment was applied, as well as at harvest time, along with the grain yield and characters related to yield. The 7 different soybean varieties tested showed significant differences in grain yield and yield-associated characters when exposed to NaCl salinity. The salinity had a greater impact on Ajmeri and William than on NARC-1 and NARC-2. Under salt stress, the grain yield of the NaRC-1 and NARC-2 varieties was 70% and 65% respectively, while the yields of the Ajmeri and William varieties were 41% and 38% respectively. The salinity-induced decrease in grain yield was traced to fewer pods per plant, fewer seeds per pod and a lighter weight per 100 grains. However, the number of pods per plant was most affected compared to the other characters. It was also observed that Na+ ion concentrations were elevated in the shoot under salt stress in all varieties. However, NARC-1 and NARC-2 showed low salt concentration in shoot as compared to other varieties. SDS-PAGE revealed significant variations in the protein profile of seedling soybean varieties. NARC-1 and NARC-2 have shown a unique banding pattern under salt stress with a molecular weight of 60 and 130 kDa. The results indicate that salinity (NaCl) triggered an antioxidant response in tolerant varieties (NARC-1 and NARC-2) of Glycine max (L.). This study suggested that both varieties have more capability and appropriate survival under salt stress as compared to other varieties

A Resource-Based Assessment of the Gnutella File-Sharing Network

This paper reviews the growth behavior of a popular peer-to-peer network. We propose a dynamic hypothesis that the growth, overshoot, and collapse trajectories may be the result of complex causal interactions between inadequate resources, private provision of common goods, free riding, and membership dynamics. We draw parallels with other systems that are well-understood and known to exhibit similar trajectories. Computer experiments confirm that free riding by peers may lead to inadequacy of resources, decline in network performance, high attrition rates, and collapse. However, if freeloading tendencies are not strong, which is usually true in smaller groups, then the P2P system will function without oscillations. An experiment that considers improvements in search algorithms suggests that the reduction of total network traffic may not be sufficient to eliminate system fluctuations in the long run

Converter transformers – A crucial component of HVDC system

Global HVDC converter transformer market is projected to reach $5.3 billion by 2025 at a CAGR of approximately 18 %. The market is mainly driven by new installations of HVDC systems for new off-shore wind systems and long-distance power transfer to load centers, however, refurbishment also play a significant role. Some of the leading players in the global HVDC converter transformer market today are ABB, Siemens, GE-Alstom, BHEL, TBEA, XD transformers, NR Electric, RXPE and C-EPRI. ABB and Siemens still have the largest market share of HVDC systems globally and are well positioned to win projects in Europe, North and South America

Experimental study on silver nanoparticles: synthesis, photo-degradation and analysis

The aim of present study was waste water treatment via advanced oxidation process (AOP). Wet chemical precipitation method was used to prepare silver nanoparticles (Ag NPs). The Ag NPs were employed for photo catalytic degradation of Congo red (CR) dye in aqueous medium. The scanning electron microscopy (SEM) investigation shows agglomerated form of Ag NPs. The average sizes of agglomerations are below 600 nm. Energy dispersive X-rays spectroscopy (EDX) and ultraviolet light visible spectroscopy (UV/Vis) also established the formation of Ag NPs. The photo-degradation study reveals that Ag NPs degraded by 73% of CR dye in 480 min. Catalytic dosage study shows the dye degradation was increased vice versa as increased the amount of Ag NPs and then almost level off after 0.025 g of catalyst. In pH study it was observed that degradation of CR dye increased as pH increased. The recovered catalyst study also significantly degraded the CR dye

A New Feature Extraction Method for TMNN-Based Arabic Character Classification

This paper describes a hybrid method of typewritten Arabic character recognition by Toeplitz Matrices and Neural Networks (TMNN) applying a new technique for feature selecting and data mining. The suggested algorithm reduces the NN input data to only the most significant and essential-for-classification points. Four items are determined to resemble the distribution percentage of the essential feature points in each part of the extracted character image. Feature points are detected depending on a designed algorithm for this aim. This algorithm is of high performance and is intelligent enough to define the most significant points which satisfy the sufficient conditions to recognize almost all written fonts of Arabic characters. The number of essential feature points is reduced by at least 88 %. Calculations and data size are then consequently decreased in a high percentage. The authors achieved a recognition rate of 97.61 %. The obtained results have proved high accuracy, high speed and powerful classification

Typing pattern analysis for fake profile detection in social Media

Nowadays, interaction with fake profiles of a genuine user in social media is a common problem. General users may not easily identify profiles created by fake users. Although various research works are going on all over the world to detect fake profiles in social media, focus of this paper is to remove additional efforts in detection procedure. Behavioral biometrics like typing pattern of users can be considered to classify genuine profile and fake profile without disrupting normal activities of the users. In this paper, DEEP_ID model is designed to detect fake profiles in Facebook like social media considering typing patterns like keystroke, mouse-click, and touch stroke. Proposed model can silently detect the profiles created by fake users when they type or click in social media from desktop, laptop, or touch devices. DEEP_ID model can also identify whether genuine profiles have been hacked by fake users or not in the middle of the session. The objective of proposed work is to demonstrate the hypothesis that user recognition algorithms applied to raw data can perform better if requirement for feature extraction can be avoided, which in turn can remove the problem of inappropriate attribute selection. Proposed DEEP_ID model is based on multi-view deep neural network, where network layers can learn data representation for user recognition based on raw data of typing pattern without feature selection and extraction. Proposed DEEP_ID model has achieved better results compared to traditional machine learning classifiers. It provides strong evidence that the stated hypothesis is valid. Evaluation results indicate that Deep_ID model is highly accurate in profile detection and efficient enough to perform fast detection