AppsPlayground

Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a smartphone application by executing some parts of the application on a remote server. Supporting such capability is not easy for smartphone application developers due to (1) correctness: some code, e.g., that for GPS, gravity, and other sensors, can run only on the smartphone so that developers have to identify which parts of the application cannot be offloaded; (2) effectiveness: the reduced execution time must be greater than the network delay caused by computation offloading so that developers need to calculate which parts are worth offloading; (3) adaptability: smartphone applications often face changes of user requirements and runtime environments so that developers need to implement the adaptation on offloading. More importantly, considering the large number of today's smartphone applications, solutions applicable for legacy applications will be much more valuable. In this paper, we present a tool, named DPartner, that automatically refactors Android applications to be the ones with computation offloading capability. For a given Android application, DPartner first analyzes its bytecode for discovering the parts worth offloading, then rewrites the bytecode to implement a special program structure supporting on-demand offloading, and finally generates two artifacts to be deployed onto an Android phone and the server, respectively. We evaluated DPartner on three real-world Android applications, demonstrating the reduction of execution time by 46%-97% and battery power consumption by 27%-83%.

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe that the time for active sensing and processing of an app can vary up to three times with 27 people's sensor traces collected over three weeks. PowerForecaster adopts a novel power emulator that emulates the power use of a sensing app while reproducing users' physical activities and phone use patterns, achieving accurate, personalized power estimation. Our experiments with three commercial apps and two research prototypes show that PowerForecaster achieves 93.4% accuracy under 20 use cases. Also, we optimize the system to accelerate emulation speed and reduce overheads, and show the effectiveness of such optimization techniques.

This paper presents the development of a remote experimentation for automatic control engineering education. By using a smartphone application, students can interact with real laboratory didactic equipment. Through a friendly graphical user interface and information provided by built-in smartphone sensors, students can modify the process parameters and verify in real time what's happening on the experimental setup. Mobile device vibration technology is used to add haptic tactile feedback to the application. This additional sense complements the visual and hearing information the user usually acquires when performing a practical experience. Therefore, students pay more attention to the results achieved online. Taking advantage of today's smartphone technological features, the students feel highly motivated to use this device to achieve improvements in their learning process. To reach a great number of users, the application runs across the most widely used mobile platforms. All user lab activities are stored in a database for the teacher later analysis. Likewise, students can store the resulted experiments on their mobile device when finished in order to make an off-line result analysis.

Social networks have been part of people's daily life and plenty of users have registered accounts in multiple social networks. Interconnections among multiple social networks add a multiplier effect to social applications when fully used. With the sharp expansion of network size, traditional standalone algorithms can no longer support computing on large scale networks while alternatively, distributed and parallel computing become a solution to utilize the data-intensive information hidden in multiple social networks. As such, synergistic partitioning, which takes the relationships among different networks into consideration and focuses on partitioning the same nodes of different networks into same partitions. With that, the partitions containing the same nodes can be assigned to the same server to improve the data locality and reduce communication overhead among servers, which are very important for distributed applications. To date, there have been limited studies on multiple large scale network partitioning due to three major challenges: 1) the need to consider relationships across multiple networks given the existence of intricate interactions, 2) the difficulty for standalone programs to utilize traditional partitioning methods, 3) the fact that to generate balanced partitions is NP-complete. In this paper, we propose a novel framework to partition multiple social networks synergistically. In particular, we apply a distributed multilevel k-way partitioning method to divide the first network into k partitions. Based on the given anchor nodes which exist in all the social networks and the partition results of the first network, using MapReduce, we then develop a modified distributed multilevel partitioning method to divide other networks. Extensive experiments on two real data sets demonstrate that our method can significantly outperform baseline independent-partitioning method in accuracy and scalability.

The mechanics of ice rubble plays an important role in many different engineering applications, including ice-structure interactions with oil and gas infrastructure, river and lake engineering, and ship-ice interactions in northern shipping lanes. Of particular interest are the massive accumulations of rubble formed by shear or compression in the ice cover, which consolidate to form sea ice ridges that can be hazards to such structures. These are common ice features in Arctic and sub-Arctic environments and as a result often govern the design loads for ships, coastal and offshore structures operating in these environments. In addition, ridge keels can scour the seafloor in relatively shallow waters posing a threat to pipelines and other subsea facilities. It is not clear what load an ice rubble feature can exert on a given structure and how it will deform. It will depend on a number of parameters including the age of the feature, its composition and structure, and its strength and failure behaviour. This paper will examine the mechanical properties of ice rubble over multiple scales. The paper will begin at the one block level, describing how ice block properties vary over time, before advancing to look at the bonding/sintering processes that occur between two ice blocks and eventually the processes that take place between multiple ice blocks (i.e., ice rubble) and large scale sea ice ridges. Particular attention will be paid to the effects temperature and pressure have on ice rubble, as these parameters are believed to be important to our understanding of its behavior.

Comparison of results from large-scale and small-scale tunnel experiments

Android is one of the leading operating systems for smartphones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing the Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts’ intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for the detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce (1) a novel technique for opcodes embedding, which we name Op2Vec, and (2) based on the learned Op2Vec, we have developed a dataset for end-to-end detection of Android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted feature extraction and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976, and F1 score of 0.979.

Cells grouping scheme against pilot contamination in large scale antennas system

Introducing Amylo-Glo, a novel fluorescent amyloid specific histochemical tracer especially suited for multiple labeling and large scale quantification studies

All kinds of social media accounts in China have formed a network with multiple subjects, large scale, complex structure, and diverse functions, becoming the main supplier of information for society. What are its core nodes and main communication channels, and how can overall communication efficiency be improved? These questions guide the analysis of China’s current social media communication. Using the highly recognizable information of the COVID-19 pandemic, this study tracks and analyzes communication effects and paths, and examines structural characteristics from the perspective of complexity theory. The findings show that in China’s complex social media network, traditional mainstream media and accounts hosted by party and government organizations are the most important communication nodes, whose linkage provides a framework function. During the static management of Xi’an and Shanghai, the communication structure exhibited three-point and four-point models, centered on local and central mainstream media accounts. Therefore, this study proposes improving the efficiency and quality of communication by optimizing the network structure, repositioning the roles of mainstream media and government accounts, and adjusting their responsibilities and powers, so as to enhance the network’s functions in information dissemination, public opinion guidance, social mobilization, and information purification.

The development of today's smartphone technology has a huge impact to the world of information technology and telecommunications. The emergence of a variety of applications for smartphones can improve the performance and effectiveness of an activity. Some restaurants have made use of smartphones in the business process, but only as a recording medium order or see a list of menus available in the restaurant. The purpose of this paper is to make the application as a whole that integrated the frontend and backend operations that help the process from ordering food, serving customer to managing restaurant data such as restaurant menus, employees, and transactions. The method used is the method of analysis (study of literature, analysis of similar applications, questionnaires, and interviews) and extreme programming design method. The results of this research is frontend application for food ordering and restaurant service and a backend system that has additional features such as restaurant profiles, restaurant menu data includes descriptions, recommended menuand images, promos, restaurant rating or menu rating, the waiter call feature, view bill and request features bill. The conclusion of this research is the developed application can help ordering process and restaurant servicesprocess in a restaurant with good customer satisfaction and good application performance.

The popularity and openness of Android platform encourage malware authors to penetrate various market places with malicious applications. As a result, malware detection has become a critical topic in security. Currently signature-based system is able to detect malware only if it is properly documented. This reveals the need to find new malware detection techniques. In our framework, a statistical technique for Android malware detection using opcodes extracted from various applications is proposed. This technique is evaluated against malware apk samples from contagio dataset and benign apk samples from various markets. The prominent features that result in reduced misclassification rates are determined using Hartley's test.

Cellular networks employ a specific radio resource management policy distinguishing them from wired and Wi-Fi networks. A lack of awareness of this important mechanism potentially leads to resource-inefficient mobile applications. We perform the first network-wide, large-scale investigation of a particular type of application traffic pattern called periodic transfers where a handset periodically exchanges some data with a remote server every t seconds. Using packet traces containing 1.5 billion packets collected from a commercial cellular carrier, we found that periodic transfers are very prevalent in today's smartphone traffic. However, they are extremely resource-inefficient for both the network and end-user devices even though they predominantly generate very little traffic. This somewhat counter-intuitive behavior is a direct consequence of the adverse interaction between such periodic transfer patterns and the cellular network radio resource management policy. For example, for popular smartphone applications such as Facebook, periodic transfers account for only 1.7% of the overall traffic volume but contribute to 30% of the total handset radio energy consumption. We found periodic transfers are generated for various reasons such as keep-alive, polling, and user behavior measurements. We further investigate the potential of various traffic shaping and resource control algorithms. Depending on their traffic patterns, applications exhibit disparate responses to optimization strategies. Jointly using several strategies with moderate aggressiveness can eliminate almost all energy impact of periodic transfers for popular applications such as Facebook and Pandora.

Currently the android platform is that the quickest growing hand-held OS package. And it’s become the foremost desirable and viable objective of malevolent applications. Android malware growth has been increasing dramatically in conjunction with increasing the variety and guiltiness of their developing techniques. Mobile malware is therefore pernicious and, on the increase, consequently having a quick and reliable detection system is important for the users. Subtle Android malware use detection shunning techniques so as to cover their malicious activities from analysis tools. During this analysis, a brand-new detection and characterization system for investigation significant deviations within the network behaviour of a smart-phone application is planned. The most goal of the planned system is to guard mobile device users and cellular infrastructure corporations from malicious applications simply nine traffic feature measurements. The planned system isn’t solely ready to observe the malicious or masquerading apps, however may also determine them as general malware or specific malware (i.e. adware) on a mobile device. The planned methodology showed the common accuracy 94% for 5 classifiers namely; Random Forest (RF), K-Nearest Neighbour (KNN), decision Tree (DT), Random Tree (RT) and Regression (R) etc. We have a tendency to conjointly provide a tagged dataset of mobile malware traffic with a lot of applications includes benign and twelve completely different families of each adware and general malware. Recent substantial analysis on machine learning algorithms analyse options from mischievous application and use those preferences to catalogue and discover unknown malicious applications. This study condenses the progression of malware detection techniques supported machine learning algorithms centred on the Android Operating systems.

Personalized applications often provide their functionality by extracting sensitive data from users. Such a strategy brings potential threats to users' privacy because malicious applications may sell users' sensitive data to third-parties for economic interests. The state-of-the-art literature addresses the privacy issue mainly from a technical perspective. In this paper, we take a different angle in which the main players involving privacy leakage are studied from a connected perspective rather than an isolated one. More specifically, we propose the concept of application ecosystem, which consists of user, application, and adversary (malicious third-party) as entities. Our aim is to analyze the tension forces inside the application ecosystem and their impacts on the behavior of each player, which can serve as a theoretical basis for designing effective and efficient privacy preservation solutions from a management level. Another outstanding trait of our analysis is the adoption of quantum game theory to model the application ecosystem, which is suitable because the important property of entanglement in quantum games can be employed to well depict the inner tension forces among the user, application, and adversary. This makes us take an important step towards understanding the complexity of decision-making from rational individuals. To the best of our knowledge, we are the first to quantize the privacy leakage issue. The simulation results quantitatively demonstrate how the mutual restrictions among all players determine their strategies and hence the development of the application ecosystem.