Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Web/Native/Hybrid: A New Taxonomy for Mobile App Development

Currently, mobile operating systems are dominated by the duopoly of iOS and Android. App projects that intend to reach a high number of customers need to target these two platforms foremost. However, iOS and Android do not have an officially supported common development framework. Instead, different development approaches are available for multi-platform development. The standard taxonomy for different development approaches of mobile applications is: Web Apps, Native Apps, Hybrid Apps. While this made perfect sense for iPhone development, it is not accurate for Android or cross-platform development, for example. In this paper, a new taxonomy is proposed. Based on the fundamental difference in the tools and programming languages used for the task, six different categories are proposed for everyday use: Endemic Apps, Web Apps, Hybrid Web Apps, Hybrid Bridged Apps, System Language Apps, and Foreign Language Apps. In addition, when a more precise distinction is necessary, a total of three main categories and seven subcategories are defined. The paper also contains a short overview of the advantages and disadvantages of the approaches mentioned.
Reparameterized Stochastic Block Model Adaptive to Heterogeneous Degree and Block Distributions

The stochastic block model (SBM) has recently gained popularity in network-oriented structure discovery and analysis, and demonstrated great superiority in the aspects of modeling flexibility, mining accuracy, and application simplicity. However, the existing SBMs are lack of adaptability in characterizing various kinds of distributions of node degree and block size commonly existed in real-world networks, because the model itself limits the node degree and block size subject only to uniform and multinomial distributions, respectively, disabling it to capture the heterogeneities of node degree and block size following other distributions. In this paper, a reparameterized SBM, named as RSBM, is proposed with broader and generalized formulation, which is adaptable to various types of networks with heterogeneous block and degree distributions. Moreover, an efficient learning algorithm for the RSBM is proposed, enabling the model to perform the parameter estimation and model selection concurrently, which significantly reduces the time complexity from $O(n^{5})$ to $O(n^{2.5})$ . Extensive experiments on various types of synthetic and real-world networks demonstrate that our proposed method outperforms the state-of-the-art methods in terms of both accuracy and scalability.
Managing Software Products in a Global Context

Follow-the-sun has evolved to follow-the-talent. Managing a product thus has a true global perspective. Products increasingly are developed in virtual teams using agile set-up and gig economy practices. The success of a product or service depends on its product management. Recently software product management has moved closer to software development in its understanding that market decisions have technical impact, and vice versa. This research provides results from an empirical field study with twenty companies on software product management in its global context. The empirical study provides concrete practices to fertilize and evolve software product management in global teams and thus the success of products in terms of predictability, quality and efficiency. We show that with institutionalization of a consistent and empowered product management role, global product development benefits in terms of schedule, quality and duration.
Implementation of a Situation Aware and Real-Time Approach for Decision Support in Online Surgery Scheduling

For decisions on operational business level it is necessary to be aware of and to understand what happens around you and what probably will happen in the near future. E.g. in Operating Room Management, and especially in Online surgery scheduling, a lot of decisions are difficult to handle, since there are high cognitive and communicational efforts to gather all necessary information to oversee the current situation and to derive decisions based on this information. However, the emerging trend of connecting devices and new methods in data analytics, allow new approaches for decision support in these areas. By using this concepts we suggested and implemented a data-driven architecture including several components for data analysis, information generation and visualization. The presented prototype means a proof-of-concept of the idea of a real-time and situation-aware decision support system for operating room managers.
Optimal Service Function Tree Embedding for NFV Enabled Multicast

In network traffic engineering, multicast is designed to deliver the same content from a single source to a group of destinations. Recently, NFV enabled multicast has been developed by deploying virtual network functions (VNFs) over the target network. To fulfill the multicast task with a service function chain (SFC) requirement, a service function tree (SFT) embedded in the shared multicast tree has to be built. Given the huge space of SFT embedding solutions, however, it is extremely hard to find the optimal one such that the total traffic delivery cost is minimized. In this paper, we tackle the optimal SFT embedding problem in NFV enabled multicast task. Specifically, we formally define the problem and formulate it with an integer linear programming (ILP), which turns out to be NP-hard. Then, a two-stage algorithm is proposed to deal with the problem with an approximation ratio of 1+p, where p is the best approximation ratio of Steiner tree and can be as small as 1.39. With extensive experimental evaluations, we demonstrate that by applying our SFT embedding solution, the cost saving of multicast traffic delivery can be up to 22.41%, compared with the random SFT embedding strategy.
Towards Secure and Energy-Efficient CRNs Via Embracing Interference: A Stochastic Geometry Approach

In a cognitive radio network (CRN), the interference caused by secondary users (SUs) is conventionally regarded as an obstacle to the throughput of the primary network. However, when considering security, the interference brings potential benefits to the secrecy throughput of the primary network. For a stand-alone primary network, the secrecy guard zone, including the cooperative mode and non-cooperative mode, has been shown as an efficient method to enhance the network security. The cooperative mode is traditionally thought to outperform the non-cooperative mode due to the jamming effect of the artificial noise generated by primary transmitters on eavesdroppers. However, allowing SUs to access the licensed spectrum, the resulting interference can be used as a source of the artificial noise instead of that generated on purpose by primary transmitters, which in return benefits the energy efficiency of the primary network. Inspired by the two benefits brought by SUs, this paper considers a random underlay CRN with eavesdroppers overhearing primary transmissions. To enhance both the security and energy efficiency of the primary network, we apply a secrecy guard zone around each primary transmitter, and adopt the non-cooperative mode. We exploit the stochastic geometry to model such a random CRN and analyze the connection/secrecy probability of primary links. We propose a criterion for guaranteeing the performance of secure primary network. The main idea is that compared with the primary network with the cooperative mode, the access of SUs should not reduce the secrecy throughput and energy efficiency of the primary network with the non-cooperative mode. Based on the obtained analytical results, we design the optimal secondary link scheduling schemes under the criterion. Both analytical and numerical results show that the interference from SUs can be exploited toward a secure and energy-efficient primary network and provide SUs with extra transmission opportunities.
Automated Detection and Repair of Incompatible Uses of Runtime Permissions in Android Apps

The runtime permission model of Android enhances security yet also constitutes a source of incompatibility issues that impedes the productivity of mobile developers. This paper presents a novel analysis that detects the incompatible permission uses in a given app and repairs them when found, hence automatically adapting the app to the runtime permission model. The key approach is to check and enforce the app's conformance to the runtime permission use protocol through static control flow analysis and bytecode transformation. We implemented our technique as an open-source tool, ARPDroid, and initially evaluated it on 20 incompatible and 3 compatible real-world apps, assisted by manual ground truth and verification. Our results show that ARPDroid achieved 100% detection accuracy, 90% repair success rate, and 91.3% overall adaptation success rate at an average time cost of about two minutes.
Wireless-Powered Two-Way Relaying Protocols for Optimizing Physical Layer Security

This paper considers a two-way relay network, in which two sources exchange data through a relay and a cooperative jammer transmits an artificial noise (AN) while a number of nearby eavesdroppers overhear to recover data from both sources. The relay harvests energy from the two source signals and the AN, and then, uses this harvested energy to forward the received signals to the two sources. Each source eliminates its own signal from the relaying signal by self-cancellation and then decodes the data signal received from the other source. For this wireless-powered two-way relay system, we propose two secure relay protocols based on power splitting and time switching techniques. The two protocols are power splitting-based two-way relaying (PS-TWR) and time switching-based two-way relaying (TS-TWR), in which the relay, respectively, controls the power splitting ratio (<inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>) and time switching ratio (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>), in order to achieve a balance between the data receiving and the energy harvesting. The optimal values of <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> for each protocol are found analytically to maximize the minimum guaranteed secrecy capacity (<inline-formula> <tex-math notation="LaTeX">$C_{S}^{\mathrm{min}}$ </tex-math></inline-formula>) considering multiple eavesdroppers in high signal-to-noise ratio environments. Numerical results show that both the PS-TWR and TS-TWR protocols using the optimized values of <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> achieve the near-optimal <inline-formula> <tex-math notation="LaTeX">$C_{S}^{\mathrm{min}}$ </tex-math></inline-formula> no matter how many eavesdroppers exist anywhere. Comparisons of the two protocols in various scenarios also show that PS-TWR achieves better <inline-formula> <tex-math notation="LaTeX">$C_{S}^{\mathrm{min}}$ </tex-math></inline-formula> than TS-TWR because PS-TWR inherently has a shorter vulnerable time for eavesdropping than TS-TWR.
The Power of One Clean Qubit in Communication Complexity

We study quantum communication protocols, in which the players' storage starts out in a state where one qubit is in a pure state, and all other qubits are totally mixed (i.e. in a random state), and no other storage is available (for messages or internal computations). This restriction on the available quantum memory has been studied extensively in the model of quantum circuits, and it is known that classically simulating quantum circuits operating on such memory is hard when the additive error of the simulation is exponentially small (in the input length), under the assumption that the polynomial hierarchy does not collapse. We study this setting in communication complexity. The goal is to consider larger additive error for simulation-hardness results, and to not use unproven assumptions. We define a complexity measure for this model that takes into account that standard error reduction techniques do not work here. We define a clocked and a semi-unclocked model, and describe efficient simulations between those. We characterize a one-way communication version of the model in terms of weakly unbounded error communication complexity. Our main result is that there is a quantum protocol using one clean qubit only and using $O(\log n)$ qubits of communication, such that any classical protocol simulating the acceptance behaviour of the quantum protocol within additive error $1/poly(n)$ needs communication $\Omega(n)$. We also describe a candidate problem, for which an exponential gap between the one-clean-qubit communication complexity and the randomized complexity is likely to hold, and hence a classical simulation of the one-clean-qubit model within {\em constant} additive error might be hard in communication complexity. We describe a geometrical conjecture that implies the lower bound.
Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data

This paper investigates Online Active Learning (OAL) for imbalanced unlabeled datastream, where only a budget of labels can be queried to optimize some cost-sensitive performance measure. OAL can solve many real-world problems, such as anomaly detection in healthcare, finance and network security. In these problems, there are two key challenges: the query budget is often limited; the ratio between two classes is highly imbalanced. To address these challenges, existing work of OAL adopts either asymmetric losses or queries (an isolated asymmetric strategy) to tackle the imbalance, and uses first-order methods to optimize the cost-sensitive measure. However, they may incur two deficiencies: (1) the poor ability in handling imbalanced data due to the isolated asymmetric strategy; (2) relative slow convergence rate due to the first-order optimization. In this paper, we propose a novel Online Adaptive Asymmetric Active (OA3) learning algorithm, which is based on a new asymmetric strategy (merging both the asymmetric losses and queries strategies), and second-order optimization. We theoretically analyze its bounds, and also empirically evaluate it on four real-world online anomaly detection tasks. Promising results confirm the effectiveness and robustness of the proposed algorithm in various application domains.
QoS Based Routing Algorithm for Advanced Metering Infrastructure in Smart Grid

The two-way and real-time communication network is the key component for the advanced metering infrastructure (AMI) in smart grid. The smart meter is the key component of communication networks of smart grid, which provides efficient route for the information exchange between users and distribution stations. As the "last mile" communication network for AMI, the neighborhood area network (NAN) can realize the real-time transmission of Household electricity information, power quality data and so on, video surveillance and so on. However, with the rapid development of smart grid, the quality of service (QoS) requirements for NAN are becoming more and more stringent. For example, it requires wider coverage and higher communication bandwidth, smaller data transmission delays and lower data packet loss rate. Therefore, a routing algorithm based on improved ant colony optimization is proposed to enhance the QoS performance of latency and reliability. A pheromone emission rule is set to make the ant get efficient guiding information and approach the destination node quickly thus reduce the time delay and packet-loss rate efficiently. Simulation results demonstrated that the proposed algorithm can efficiently improve the QoS performance of communication network for AMI in smart grid.
Design Criteria for Hand Exoskeletons: Measurement of Forces Needed to Assist Finger Extension in Traumatic Brain Injury Patients

The development of powered hand orthoses and exoskeletons for treatment of chronic motor deficits due to stroke, traumatic brain injury, and cerebral palsy has been widely explored by the robotics engineering community. The conditions arising from these disorders take several forms and clinical descriptions of the various deficits are at times difficult to interpret for those with limited interaction with patients. With devices commonly designed and evaluated on their ability to aid hand closure, well-known extension deficits that are present among these patients typically receive only secondary consideration by robotic approaches. Quantitative characterization of these extension deficits in a manner that is easily translated into engineering design criteria would greatly benefit device design and evaluation. Here, we measure the externally applied torques required to assist extension of the index finger and thumb of three patients with increased flexor tone in the hand. The average torque required to fully extend a subject's index finger MCP joint ranged from <inline-formula><tex-math notation="LaTeX">$0.22-0.75\,\text{N}\cdot \text{m}$</tex-math></inline-formula> and the thumb's MCP from <inline-formula><tex-math notation="LaTeX"> $0.29-1.35\,\text{N} \cdot \text{m}$</tex-math></inline-formula>. The highest torques were recorded when subjects were asked to volitionally extend their fingers, causing an increase in flexor tone requiring more assistance to overcome than when they were relaxed.
Enabling IoT Stream Management in Multi-cloud Environment by Orchestration

Every-Day lives are becoming increasingly instrumented by electronic devices and any kind of computer-based (distributed) service. As a result, organizations need to analyse an enormous amounts of data in order to increase their incomings or to improve their services. Anyway, setting-up a private infrastructure to execute analytics over Big Data is still expensive. The exploitation of Cloud infrastructure in IoT Stream management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users' needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different IoT Stream and, in general, Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.
Education Analytics: Challenges and Approaches

We present a survey on the current status of education analytics. Over 80 state-of-the-art research projects and reports, published from 2013 to 2017 and archived in IEEE Xplore, are listed. Focusing on student performance prediction and intervention, we highlight the data sets used, techniques employed, algorithms utilized, and particular information in each paper/project.
A Wirelessly Powered UWB RFID Sensor Tag With Time-Domain Analog-to-Information Interface

This paper presents a wirelessly powered radio frequency identification sensor tag with an analog-to-information interface. A time-domain interface, incorporating an ultra-low-power impulse radio ultra-wideband (IR-UWB) transmitter (TX), is employed. The analog signal from the sensor is compared with a triangular waveform, resulting in a pulse-position modulation signal to trigger UWB pulses. Thanks to the high time-resolution IR-UWB radio, time intervals of the impulses can be used to represent the original input value, which is measured remotely on the reader side by a time-of-arrival estimator. This approach not only eliminates the analog-to-digital converter (ADC) but also significantly reduces the number of bits to be transmitted for power saving. The proposed tag is fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process with an active area of 2.5 mm<sup>2</sup>. The measurement results demonstrate that a 300-kS/s sampling rate with a 6.7-bit effective number of bits (ENOB) is obtained via a UWB receiver with a sensitivity of −93 dBm and an integration window of 10 ns. The ENOB is improved to 7.3 bits when the integration window is reduced to 2 ns. The tag can be powered up by a −18-dBm UHF input signal. The power consumption of the proposed tag is 41.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> yielding a 1.3-pJ/conv.step figure of merit, offering 9<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and 67<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvements compared with the state of the art based on an ADC and a backscattering TX, and the tag based on an ADC and a narrowband TX, respectively.
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget

Iterative algorithms, like gradient descent, are common tools for solving a variety of problems, such as model fitting. For this reason, there is interest in creating differentially private versions of them. However, their conversion to differentially private algorithms is often naive. For instance, a fixed number of iterations are chosen, the privacy budget is split evenly among them, and at each iteration, parameters are updated with a noisy gradient. In this paper, we show that gradient-based algorithms can be improved by a more careful allocation of privacy budget per iteration. Intuitively, at the beginning of the optimization, gradients are expected to be large, so that they do not need to be measured as accurately. However, as the parameters approach their optimal values, the gradients decrease and hence need to be measured more accurately. We add a basic line-search capability that helps the algorithm decide when more accurate gradient measurements are necessary. Our gradient descent algorithm works with the recently introduced zCDP version of differential privacy. It outperforms prior algorithms for model fitting and is competitive with the state-of-the-art for $(ε,δ)$-differential privacy, a strictly weaker definition than zCDP.
MetropolJS: Visualizing and Debugging Large-Scale JavaScript Program Structure with Treemaps

As a result of the large scale and diverse composition of modern compiled JavaScript applications, comprehending overall program structure for debugging is challenging. In this paper we present our solution: MetropolJS. By using a Treemap-based visualization it is possible to get a high level view within limited screen real estate. Previous approaches to Treemaps lacked the fine detail and interactive features to be useful as a debugging tool. This paper introduces an optimized approach for visualizing complex program structure that enables new debugging techniques where the execution of programs can be displayed in real time from a bird's-eye view. The approach facilitates highlighting and visualizing method calls and distinctive code patterns on top of code segments without a high overhead for navigation. Using this approach enables fast analysis of previously difficult-to-comprehend code bases.