Miracle-dz/UniFAR-FTUs huggingface dataset:
Profiling Call Changes Via Motif Mining

Components' interactions in software systems evolve over time increasing in complexity and size. Developers might have hard time to master such complexity during their maintenance activities incrementing the risk to make mistakes. Understanding changes of such interactions helps developer plan their re-factoring activities. In this study, we propose a method to study the occurrence of motifs in call graphs and their role in the evolution of a system. In our settings, motifs are patterns of class calls that can arise for many reasons as, for example, by implementing design choices. By mining motifs of the call graph obtained from each system's release, we were able to profile the evolution of 68 releases of five open source systems and show that 1) systems have common motifs that occur non-randomly and persistently over their releases, 2) motifs can be used to describe the evolution of calls, compare systems and eventually reveal releases that underwent major changes, 3) there are no specific motif types that include design patterns in all systems under study, but each system has motifs that likely include them, motifs that do not include them at all, and motifs that include a design pattern and occur only once in every release. Some of the findings resemble the ones for biological / physical systems and, as such, path the way to study the evolution of call graphs as dynamical systems (i.e., as system regulated by analytic functions).
A Front-End ASIC With High-Voltage Transmit Switching and Receive Digitization for 3-D Forward-Looking Intravascular Ultrasound Imaging

This paper presents an area- and power-efficient application-specified integrated circuit (ASIC) for 3-D forward-looking intravascular ultrasound imaging. The ASIC is intended to be mounted at the tip of a catheter, and has a circular active area with a diameter of 1.5 mm on the top of which a 2-D array of piezoelectric transducer elements is integrated. It requires only four micro-coaxial cables to interface 64 receive (RX) elements and 16 transmit (TX) elements with an imaging system. To do so, it routes high-voltage (HV) pulses generated by the system to selected TX elements using compact HV switch circuits, digitizes the resulting echo signal received by a selected RX element locally, and employs an energy-efficient load-modulation datalink to return the digitized echo signal to the system in a robust manner. A multi-functional command line provides the required sampling clock, configuration data, and supply voltage for the HV switches. The ASIC has been realized in a 0.18- $\mu \text{m}$ HV CMOS technology and consumes only 9.1 mW. Electrical measurements show 28-V HV switching and RX digitization with a 16-MHz bandwidth and 53-dB dynamic range. Acoustical measurements demonstrate successful pulse transmission and reception. Finally, a 3-D ultrasound image of a three-needle phantom is generated to demonstrate the imaging capability.
Descriptive Compound Identifier Names Improve Source Code Comprehension

Reading and understanding source code is a major task in software development. Code comprehension depends on the quality of code, which is impacted by code structure and identifier naming. In this paper we empirically investigated whether longer but more descriptive identifier names improve code comprehension compared to short names, as they represent useful information in more detail. In a web-based study 88 Java developers were asked to locate a semantic defect in source code snippets. With descriptive identifier names, developers spent more time in the lines of code before the actual defect occurred and changed their reading direction less often, finding the semantic defect about 14% faster than with shorter but less descriptive identifier names. These effects disappeared when developers searched for a syntax error, i.e., when no in-depth understanding of the code was required. Interestingly, the style of identifier names had a clear impact on program comprehension for more experienced developers but not for less experienced developers.
Compliance Adherence in Distributed Software Delivery: A Blockchain Approach

In this extended abstract, we propose a conceptual framework that leverages distributed ledger technology and smart contracts to create a decentralized system to capture the occurrence of interesting development activities (e.g., a development build) and associated contextual data, and automatically audit and evaluate compliance to governance policies. Our hypothesis is that such a framework will facilitate easier sharing of information across all participants of a distributed development team, compliance evaluation and early mitigation actions, leading to greater visibility and compliance. Currently, the proof of concept we are working on is focused on sharing and compliance evaluation of the open-source components used in software development.
Online Location Trace Privacy: An Information Theoretic Approach

We consider the problem of protecting individual user’s location privacy at the trace-level and study the privacy-utility trade-off, which has key applications in privacy-preserving location-based service. Existing works on Location Privacy Protection Mechanisms (LPPMs) have mainly focused on protecting single location, without taking into account the temporal correlations among locations within the trace, which can lead to higher privacy leakage when considering the whole trace. However, to date, there lacks a formal framework to quantify the trace-level location privacy leakage, and a practical mechanism to release location traces in an optimal and online manner. In this paper, we endeavor to solve this problem using an information-theoretic approach. We first propose a location trace privacy metric based on the mutual information between the original and released trace in an offline setting, and formulate the optimal location trace release problem that minimizes trace-level privacy leakage given a utility constraint. We also propose a privacy metric to capture trace-level privacy leakage in an online setting. As directly computing these metrics incur exponential complexity w.r. t. the trace length, we obtain upper and lower bounds on the trace-level privacy leakage by exploiting the Markov structure of the temporal location correlations, which are efficiently computable. The proposed upper bounds enable us to derive efficient online solutions (i.e., LPPMs) by modifying Blahut-Arimoto algorithm in rate-distortion theory. Then we validate the proposed upper and lower bounds and the actual leakage of our LPPM through extensive experiments over both synthetic and real-world location data sets. Our results show the superiority of our LPPM over existing LPPMs in terms of trace-level privacy-utility tradeoff, which is more conspicuous when the location trace is more correlated.
Resource Allocation Scheme in 5G Network Slices

5G mobile network is predictable to assist flexible requirements henceforth dynamically allocate network resources according to the demands. Network slicing, where network resources are assigned and packaged in a customize manner to set of users according to their particular requirements, is measured as a key standard to fulfil diversity of requirements. There will obviously be conflicting demands in an allocation of such slices, and the effective provisioning of network slicing poses numerous challenges. Indeed, network slicing has a twofold impact in terms of user/traffic prioritization as it dictates for the simultaneous management of the priority among different slices (i.e., inter-slice) and the priority among the users belonging to the same slice (i.e., intra-slice). In this paper, we propose a novel resource allocation scheme able to dynamically allocate network resources to different slices in order to achieve the satisfaction of the users while guaranteeing to meet the requirements of the slices they belong to.
Localized Fault Tolerant and Connectivity Restoration Algorithms in Mobile Wireless Ad Hoc Network

Due to the dynamic characteristics of mobile wireless ad hoc networks, a node may join or leave/fail at any time, and links are usually intermittently unavailable. The destruction of the network connectivity would dramatically degrade the performance. To resolve this challenge, topology control is an efficient way to maintain network connectivity, which focuses on the adjustment of node transmit power in the past few decades. However, the adjustment of node movement has not been adequately addressed to restore the connectivity in mobile wireless ad hoc networks. In this paper, we propose three hybrid recovering algorithms, namely, cascade movement based on nearest nodes, cascade movement based on connected dominating set, and cascade movement based on critical nodes. With the adoption of Kalman filter, all these schemes can predict the movement of nodes so as to identify and maintain connectivity of mobile wireless ad hoc networks. Meanwhile, once a node fails or a link breaks down, they can also recover the connectivity immediately. We conduct extensive simulations on the metrics of the average number of participating nodes, the deviation from original direction, and the ratio of cascade movement for the three proposed algorithms, and the algorithm of cascade movement based on critical nodes achieves the best results.
What's Inside My App?: Understanding Feature Redundancy in Mobile Apps

As the number of mobile apps increases rapidly, many users may install dozens of, or even hundreds of, apps on a single smartphone. However, many apps on the same phone may contain similar or even the same feature, resulting in feature redundancy. For example, multiple apps may check weather forecast for the user periodically. Feature redundancy may cause many undesirable side-effects such as consuming extra CPU resources and network traffic. This paper proposes a method to identify common features within an app, and evaluated it on over four thousand popular apps. Experiments on a list of apps installed on actual smartphones show that the extent of feature redundancy is very high. We found that more than 85% of user smartphones contain redundant features, while in extreme cases, some smartphones may contain dozens of apps with the same feature. In addition, our user surveys found out that about half of the redundant features are undesirable from the end users' perspective, which indicates that feature redundancy has become an important issue that needs to be investigated further.
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud

The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are offloaded to the cloud. However, revealing data to the cloud leads to potential privacy risk. To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers. A simple data transformation is performed on the mobile device, while the resource-hungry training and the complex inference rely on the cloud data center. To protect the sensitive information, a lightweight privacy-preserving mechanism consisting of arbitrary data nullification and random noise addition is introduced, which provides strong privacy guarantee. A rigorous privacy budget analysis is given. Nonetheless, the private perturbation to the original data inevitably has a negative impact on the performance of further inference on the cloud side. To mitigate this influence, we propose a noisy training method to enhance the cloud-side network robustness to perturbed data. Through the sophisticated design, ARDEN can not only preserve privacy but also improve the inference performance. To validate the proposed ARDEN, a series of experiments based on three image datasets and a real mobile application are conducted. The experimental results demonstrate the effectiveness of ARDEN. Finally, we implement ARDEN on a demo system to verify its practicality.
HtrA of Borrelia burgdorferi Leads to Decreased Swarm Motility and Decreased Production of Pyruvate

ABSTRACT Borrelia burgdorferi HtrA (HtrABb) is a serine protease that targets damaged or improperly folded proteins. In our previous studies, HtrABb specifically degraded basic membrane protein BmpD, chemotaxis phosphatase CheX, and outer membrane protein P66. In addition, HtrABb degrades virulence factor BB0323 and components of the extracellular matrix fibronectin and aggrecan. A proteomics-based analysis (two-dimensional difference gel electrophoresis [2-D DIGE], liquid chromatography-mass spectrometry [LC-MS]) of an HtrABb-overexpressing strain of B. burgdorferi (A3HtrAOE) revealed that protein levels of P66 were reduced in comparison to wild-type B. burgdorferi, confirming its status as an HtrABb substrate. Hbb, a P66-DNA-binding transcription factor, was specifically degraded by HtrABb, providing supportive evidence for a role for both in the regulation of P66. A3HtrAOE exhibited reduced motility in swarm assays, a possible link between overabundance of HtrABb and its enzymatic specificity for P66. However, the ΔP66 strain did not have reduced motility in the swarm assays, negating a role for this protein. The proteomics analyses also identified three enzymes of the glycolytic pathway, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), glycerol-3-phosphate dehydrogenase (GPDH), and glycerol kinase (GK), and one enzyme involved in carbohydrate metabolism, diphosphate-fructose-6-phosphate 1-phosphotransferase, which were reduced in A3HtrAOE. Consistent with its reduced protein levels of these glycolytic enzymes, A3HtrAOE was also deficient in production of pyruvate. We propose a model for a role for HtrABb in contributing to a decrease in metabolic activity of B. burgdorferi. IMPORTANCE Being a vector-borne bacterium, B. burgdorferi must remodel its protein content as it transfers from tick to mammal. Proteolysis is a mechanism whereby remodeling can be accomplished. HtrABb degrades a number of proteins whose disappearance may help in preparing this organism for a stage of low metabolic activity. IMPORTANCE Being a vector-borne bacterium, B. burgdorferi must remodel its protein content as it transfers from tick to mammal. Proteolysis is a mechanism whereby remodeling can be accomplished. HtrABb degrades a number of proteins whose disappearance may help in preparing this organism for a stage of low metabolic activity.
Security: A Critical Quality Attribute in Self-Adaptive Systems

Self-Adaptive Systems (SAS) are revolutionizing many aspects of our society. From server clusters to autonomous vehicles, SAS are becoming more ubiquitous and essential to our world. Security is frequently a priority for these systems as many SAS conduct mission-critical operations, or work with sensitive information. Fortunately, security is being more recognized as an indispensable aspect of virtually all aspects of computing systems, in all phases of software development. Despite the growing prominence in security, from computing education to vulnerability detection systems, it is just another concern of creating good software. Despite how critical security is, it is a quality attribute like other aspects such as reliability, stability, or adaptability in a SAS.
A Spectral Algorithm for Ternary Function Classification

The spectral representation and classification of 2-valued and multiple-valued functions has been previously studied and found to be useful in logic design and testing for conventional circuits. Spectral techniques also have potential application for reversible and quantum circuits. This paper addresses the classification of ternary functions into spectral translation equivalence classes. An efficient algorithm is presented that determines the spectral translations to map a given function to the representative function for the equivalence class containing the given function. Using this algorithm we show that the 2-variable ternary functions partition into 11 equivalence classes. While the number of spectral equivalence classes for ternary functions with 3 or more variables is very large, prohibiting full enumeration, we determine a lower bound of 167,275 classes for 3 variables. The algorithm can be used for a significant number of variables to quickly determine if two functions fall within the same equivalence class and, if they do, to find a sequence of spectral translations to map one to the other. Generalization of the approach to higher radix functions is briefly discussed.
Fault Diagnosis of Rolling Bearing Under Speed Fluctuation Condition Based on Vold-Kalman Filter and RCMFE

Rolling bearings’ operation under variable speed conditions exhibits complex time-varying modulations and spectral structures, resulting in difficulty in the fault diagnosis. In order to effectively remove the influence of the rotational speed and extract the fault characteristics, this paper develops a fault diagnosis scheme based on the Vold–Kalman filter (VKF), refined composite multi-scale fuzzy entropy (RCMFE), and Laplacian score (LS). In the proposed method, the VKF is first adopted to remove the fault-unrelated components and give a clear representation of the fault symptoms. Second, the RCMFE is applied to extract fault features from the denoised vibration signal. Third, the LS approach is introduced to refine the fault features by sorting the scale factors. In the end, the selected features are fed into the logistic regression to automatically complete the fault pattern identifications. The proposed method is experimentally demonstrated to be able to recognize the localized defect on the inner race, outer race, and rolling element under variable speed conditions.
Audience Size Forecasting: Fast and Smart Budget Planning for Media Buyers

A growing proportion of digital advertising slots is purchased through real time bidding auctions, which enables advertisers to impose highly specific criteria on which devices and opportunities to target. Employing sophisticated targeting criteria reliably increases the performance of an ad campaign, however too strict criteria will limit its scale. This raises the need to estimate the number of anticipated ad impressions at a given campaign performance level, thus enabling advertisers to tune the campaign's budget to an optimal performance-scale trade-off. In this paper, we provide a way to estimate campaign impressions given the campaign criteria. There are several challenges to this problem. First, the criteria contain logic to include and exclude combinations of audience segments, making the space of possible criteria exponentially large. Furthermore, it is difficult to validate predictions, because we wish to predict the number of impressions available without budget constraints, a situation we can rarely observe in practice. In our approach, we first treat the audience segment inclusion/exclusion criteria separately as a data compression problem, where we use MinHash "sketches" to estimate audience size. We then model the number of available impressions with a regularized linear regression in log space, using multiplier features motivated by the assumption that some components of the additional campaign criteria are conditionally independent. We construct a validation set by projecting observed RTB data (under real budget constraints) to get impression availability without budget constraints. Using this approach, our average prediction is a factor of 2.2 from the true impression availability, and the deployed product responds to user requests in well under a second, meeting both accuracy and latency requirements for decision making in the execution of advertising campaigns.
A Low Power 10 bit 50-MS/s Sample and Hold OTA Amplifier

This paper presents about the design of a low power 10-bit 50-M sample /sec Sample and Hold amplifier. A sample and hold amplifier (SHA) acts as a front end block for an Analog Digital Circuit (ADC). To realize a low power SHA, a power efficient Operational Tran-conductance Amplifier (OTA) is to be designed and used. In the literature, many topologies of OTA are proposed for low power. In this paper, an Improved Recycling Folded Cascade (IRFC) OTA existing in the literature is used for realizing SHA and is proposed. In addition to this the SHA includes bottom plate sampling and bootstrap switch to reduce the non-linear distortion. The IRFC OTA used in the design of SHA is implemented using TSMC 90 nm Process. It achieves a DC gain of 73 dB, Unity Gain Bandwidth (UGB) of 70 MHz and with a phase margin of 63 degrees. The proposed SHA is designed and simulated using spectre simulator. From the simulation, it is noted that the SHA achieves a Spurious Free Dynamic Range (SFDR) of 63.44dB and SNDR of 60.6dB for a sampling frequency of 50MS/s with a peak-peak voltage of 1.2 Volts. The S/H circuit consumes 0.44mW of power.
Self-Adaptation Made Easy with Blockchains

This position paper describes how blockchains facilitate the implementation of distributed self-adaptive systems. We demonstrate how the master/slave decentralised control pattern for self-adaptive systems, integrated with a permissioned blockchain, can protect nodes of a network against attacks by continuously adapting the membership of an access control list. Whenever malicious behaviour is detected, consensus on an updated access control list is reached, and that node is removed from the network. Using a smart home, as an example, we demonstrate that a permissioned blockchain is able to maintain a consistent view of a network of Internet of Things (IoT) devices in the presence of malicious nodes.
Severe Case of Chronic Pelvic Pain Syndrome: Recovery after Injection of Procaine into the Vesicoprostatic Plexus—Case Report and Discussion of Pathophysiology and Mechanisms of Action

We describe a patient with a 35-year history of a severe chronic pelvic pain syndrome (CPPS) that failed to adequately respond to various drug therapies and other treatments by different specialists. In addition to the ongoing chronic pain, he suffered from week-long episodes of increased pain with no discernible trigger. At the first consultation with us the patient was in a particularly severe pain phase. He was taking four different analgesically effective drugs. In terms of therapeutic local anesthesia (neural therapy), we performed suprapubic injection of procaine 1% with infiltration of the vesicoprostatic plexus. Just a few minutes later, the pain decreased significantly. To maintain and further increase the effect, we performed the injection six more times. The patient gradually reduced and stopped all drugs and remained free of pain and discomfort ever since. This is the first report of a successful therapeutic infiltration of the vesicoprostatic plexus using a local anesthetic (LA) in a patient with CPPS that has been refractory to different treatments for many years. A possible explanation may be that the positive feedback loops maintaining pain and neurogenic inflammation are disrupted by LA infiltration. This can lead to a new organisation (self-organisation) of the pain-processing systems.