Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery

Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.
Climate Event Detection Algorithm Based on Climate Category Word Embedding

Detecting climate events efficiently and accurately is important in traffic warning, disaster warning, and disease prevention. Given that ordinary event detection algorithms are ignored in climate domain's knowledge, the results of climate domain's event detection are not satisfactory. This paper proposes a climate event detection algorithm based on climate category word embedding(CEDCWE). This method combines the climate category word embedding and typical factors of climate events as a document representation model to express detailed information of climate documents and detect climate events efficiently and accurately. Compared with other methods, the CEDCWE algorithm can generate better climate document representations and climate event detection results. In the experiments, we acquire the datasets by a web crawler and evaluate our CEDCWE on real-world climate event detection tasks. Experimental results show that our CEDCWE is effective in climate document representation and outperforms typical methods.
Studying the Impact of Adopting Continuous Integration on the Delivery Time of Pull Requests

Continuous Integration (CI) is a software development practice that leads developers to integrate their work more frequently. Software projects have broadly adopted CI to ship new releases more frequently and to improve code integration. The adoption of CI is motivated by the allure of delivering new functionalities more quickly. However, there is little empirical evidence to support such a claim. Through the analysis of 162,653 pull requests (PRs) of 87 GitHub projects that are implemented in 5 different programming languages, we empirically investigate the impact of adopting CI on the time to deliver merged PRs. Surprisingly, only 51.3% of the projects deliver merged PRs more quickly after adopting CI.We also observe that the large increase of PR submissions after CI is a key reason as to why projects deliver PRs more slowly after adopting CI. To investigate the factors that are related to the time-to-delivery of merged PRs, we train regression models that obtain sound median R-squares of 0.64-0.67. Finally, a deeper analysis of our models indicates that, before the adoption of CI, the integration-load of the development team, i.e., the number of submitted PRs competing for being merged, is the most impactful metric on the time to deliver merged PRs before CI. Our models also reveal that PRs that are merged more recently in a release cycle experience a slower delivery time.
Predicting Antigen Presentation—What Could We Learn From a Million Peptides?

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.
Multi-Class Lesion Diagnosis with Pixel-wise Classification Network

Lesion diagnosis of skin lesions is a very challenging task due to high inter-class similarities and intra-class variations in terms of color, size, site and appearance among different skin lesions. With the emergence of computer vision especially deep learning algorithms, lesion diagnosis is made possible using these algorithms trained on dermoscopic images. Usually, deep classification networks are used for the lesion diagnosis to determine different types of skin lesions. In this work, we used pixel-wise classification network to provide lesion diagnosis rather than classification network. We propose to use DeeplabV3+ for multi-class lesion diagnosis in dermoscopic images of Task 3 of ISIC Challenge 2018. We used various post-processing methods with DeeplabV3+ to determine the lesion diagnosis in this challenge and submitted the test results.
Detecting Network Intrusions Using a Confidence-Based Reward System

Combining multiple intrusion detection technologies into a hybrid system has been recently proposed to improve the comprehensive intrusion detection capability. However, such a hybrid system is not always stronger than its component detectors. Getting different detection technologies to interoperate effectively and efficiently has become a major challenge when building operational intrusion detection systems (IDS's). In this paper, we propose a novel reward system model in order to increase the accuracy and reliability of hybrid IDS's. In particular, the proposed confidence-based reward system built within a reinforcement learning algorithm includes three components. Namely, a relative discount factor, a confidence extraction technique, and a unique reward computing algorithm. The preliminary case studies show that the proposed reward system has a potential to improve the anomaly detection accuracy, decrease false alarm rate, and improve adaptability to new network traffic.
Design of a Small Metamtrial Antenna for Millimetric Wave Applications

In this paper, a new design for metamaterial antennas is developed. For this purpose, metamaterial resonant antennas with a mushroom structure based on the zeroth order resonant characteristic of a composite right/lefthanded transmission line (CRLH TL) are investigated. The electromagnetic behavior of this structure is studied, and analytical equivalent-circuit model is proposed. The antenna is proposed for 28 GHz which can be applied for resonance-based millimeter wave circuits. The simulation results show that the antenna has small size and a good bandwidth with range from 27.05 to 28.25 GHz for return loss S11 less than -10 dB. The overall dimension of the antenna is 20.0*22.0*1.6 mm3 including the antenna substrate.
On the Systematic Exploitation of the Estonian Data Exchange Layer X-Road for Strengthening Public-Private Partnerships

The concept of Public-Private Partnerships has found a large variety of different approaches since the 1980s and each country has found its own interpretation. In later years, Estonia has also strengthened a collaboration between public and private sector, while a data exchange layer X-Road, initially developed for the public data sets to communicate, is now also been open for private entities to use and thus shows the great potential for Public-Private Partnerships to grow further. As there are still quite a small amount of private sector entities attached themselves to the X-Road, there must be the reason, why the private sector has not gone along with the platform. In order to find out how to design a unified platform for public agencies and private entities, authors have conducted face-to-face interviews as well as an online questionnaire among different private sector organizations. As a result, authors will provide various recommendations, in order to make a platform more acceptable for both public as well as private entities and to strengthen the intersectoral collaboration. The research was supported by the Estonian Information System Authority (EISA).
A Search System for Mathematical Expressions on Software Binaries

Developers often ask for libraries that implement specific mathematical expressions. A fundamental bottleneck in building information retrieval (IR) systems to answer such mathematical queries is the inability to detect a given expression in software binaries. While we have a few math IR solutions such as EgoMath2 and Tangent-3 that work over text documents, none exist to search over software binaries. Our vision is to build a search system for binaries to answer queries containing mathematical expressions. A wide variety of compilers and differences in the way they optimize the code, pose difficult challenges to solve this problem. In this work, we discuss our preliminary results in detecting mathematical expressions in software binaries. We use a knowledge base assisted approach to solve this problem. We are able to search mathematical expressions with a precision of 80% and a recall of 53%. This work opens up interesting research opportunities in areas such as software security and performance, to help analysts in identifying and analyzing binaries for implementations of mathematical expressions.
A Plant Extract Acts Both as a Resistance Inducer and an Oomycide Against Grapevine Downy Mildew

Protecting vineyards from cryptogamic diseases such as downy mildew, caused by Plasmopara viticola, generally requires a massive use of phytochemicals. However, the issues on unintentional secondary effects on environment and human health, and the occurrence of P. viticola resistant strains, are leading to the development of alternative strategies, such as the use of biocontrol products. In this paper, we evidenced the ability of a plant extract to protect grapevine from P. viticola. Further experiments carried out both on cell suspensions and on plants revealed that plant extract activates typical defense-related responses such as the production of H2O2, the up-regulation of genes encoding pathogenesis-related proteins and stilbene synthase, as well as the accumulation of resveratrol or its derivative piceid. We also brought to light a strong direct effect of PE on the release and motility of P. viticola zoospores. Furthermore, we found out that PE application left dried residues on leaf surface, impairing zoospores to reach stomata. Altogether, our results highlight the different modes of action of a new biocontrol product able to protect grapevine against downy mildew.
ISO Compliant Evaluation of a Teledermatology Structured Report System for the Brazilian Public Healthcare

DICOM structured reports (DICOM SR) are a set of standard-compliant practices dedicated to provide a pragmatic way to create documents when medical data organization and storage are an issue. In practice, it can be seen as a set of guidelines that can be used by physicians in order to create report templates that can be manipulated, filled and stored as structured data. Nowadays, systems that implements DICOM SR are used to provide physicians with tools that facilitate the report construction by organizing and simplifying their workload. To support scenarios involving features related to the creation and manipulation of structured documents, those systems must have a high-quality degree. While the external quality degree is important to secure a good user perception and adhesion to the system, having a high internal quality degree is a must to ensure external quality, functionality, reliability, maintainability and performance. Aiming to evaluate such scenario, this work presents an ISO compliant internal quality evaluation for the structured report system of the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC), in Brazil. Using the ISO/IEC 9126-3 norm to do the evaluation, we concluded that the system has a high internal quality degree, regarding usability, maintainability, efficiency, reliability and functionality as dictated by the ISO/IEC 9126-1 norm.
Study on Random Dopant Fluctuation in Core–Shell Tunneling Field-Effect Transistors

Random dopant fluctuations (RDF) of conventional and core–shell tunneling field-effect transistors (TFETs) are investigated using 3-D numerical simulations. Sano model was adopted to consider short- and long-range Coulomb potentials separately under drift-diffusion transport. Core–shell TFETs have much smaller average subthreshold swing and greater on-state performance than do conventional TFETs. Conventional TFETs have small tunneling area which is the source/channel interface near the gate, so the device-to-device performance variations are significant. Core–shell TFETs, on the other hand, have wide tunneling area which is the core–shell interface, thus averaging out the performance variations induced by the RDF effects. Longer drain-side extension lengths decrease the ambipolar effects without increasing channel resistance, but changing drain-side extension lengths or/and drain doping concentrations of the conventional TFETs does not reduce the performance variations. Overall, the core–shell TFETs have the variations of threshold voltages and on-state currents which are 34% and 68% smaller than those of the conventional TFETs, respectively. Therefore, the core–shell TFETs are advantageous of dc performance as well as of RDF immunity, promising for nanoscale low-power applications.
Fully Analytical Carrier-Based Charge and Capacitance Model for Hetero-Gate-Dielectric Tunneling Field-Effect Transistors

Based on an analytical surface potential model incorporating the channel inversion carriers, a physics-based terminal capacitance model with closed-form solutions for a hetero-gate-dielectric (HGD) tunnel field-effect transistor (TFET) is developed for the first time. Good agreements between the proposed model and the numerical simulations have been achieved in all operation regimes and for different HGD structures. The developed model without involving any iterative process can be easily applied to the widely used SPICE simulations and would be helpful for the transient performance of TFET-based circuits.
Imprecisions Diagnostic in Source Code Deltas

Beyond a practical use in code review, source code change detection (SCCD) is an important component of many mining software repositories (MSR) approaches. As such, any error or imprecision in the detection may result in a wrong conclusion while mining repositories. We identified, analyzed, and characterized impressions in GumTree, which is the most advanced algorithm for SCCD. After analyzing its detection accuracy over a curated corpus of 107 C# projects, we diagnosed several imprecisions. Many of our findings confirm that a more language-aware perspective of GumTree can be helpful in reporting more precise changes.
Low Latency Edge Rendering Scheme for Interactive 360 Degree Virtual Reality Gaming

This paper describes the core functionality and a proof-of-concept demonstration setup for remote 360 degree stereo virtual reality (VR) gaming. In this end-to-end scheme, the execution of a VR game is off-loaded from an end user device to a cloud edge server in which the executed game is rendered based on user's field of view (FoV) and control actions. Headset and controller feedback is transmitted over the network to the server from which the rendered views of the game are streamed to a user in real-time as encoded HEVC video frames. This approach saves energy and computation load of the end terminals by making use of the latest advancements in network connection speed and quality. In the showcased demonstration, a VR game is run in Unity on a laptop powered by i7 7820HK processor and GTX 1070 GPU. The 360 degree spherical view of the game is rendered and converted to a rectangular frame using equirectangular projection (ERP). The ERP video is sliced vertically and only the FoV is encoded with Kvazaar HEVC encoder in real time and sent over the network in UDP packets. Another laptop is used for playback with a HTC Vive VR headset. Our system can reach an end-to-end latency of 30 ms and bit rate of 20 Mbps for stereo 1080p30 format.
Studying the Potential of Multi-Target Classification on Patient Screening Data to Predict Dropout Cases

Treatment of patients with tinnitus is mainly pursued on the basis of screening data, encompassing answers to questionnaires or audiological examinations. Since tinnitus affects the quality of life of patients and is associated with comorbidities like depression, the screening step involves the acquisition of extensive amounts of information, which may contribute to the design of a personalized treatment. Often times, it can be observed that patients give up their treatment before completion (i.e., they constitute “dropout” cases). In this study, we investigate how multi-target classification on the screening data can contribute to characterize patients that will drop out of the study. For our analysis, we base or considerations on the target variable “tinnitus loudness”, i.e. the subjectively perceived loudness of the phantom signal. Following this, we attempt to identify variables that explain the tinnitus loudness together with the likelihood of interrupting the treatment. To be more precise, we report on results from gathered data of 1419 tinnitus patients from the University Hospital of Regensburg.
Value-driven Synthesis for Neural Network ASICs

In order to enable low power and high performance evaluation of neural network (NN) applications, we investigate new design methodologies for synthesizing neural network ASICs (NN-ASICs). An NN-ASIC takes a trained NN and implements a chip with customized optimization. Knowing the NN topology and weights allows us to develop unique optimization schemes which are not available to regular ASICs. In this work, we investigate two types of value-driven optimized multipliers which exploit the knowledge of synaptic weights and we develop an algorithm to synthesize the multiplication of trained NNs using these special multipliers instead of general ones. The proposed method is evaluated using several Deep Neural Networks. Experimental results demonstrate that compared to traditional NNPs, our proposed NN-ASICs can achieve up to 6.5x and 55x improvement in performance and energy efficiency (i.e. inverse of Energy-Delay-Product), respectively.
Estimating Collective Attention toward a Public Display

Enticing groups of passers-by to focused interaction with a public display requires the display system to take appropriate action that depends on how much attention the group is already paying to the display. In the design of such a system, we might want to present the content so that it indicates that a part of the group that is looking head-on at the display has already been registered and is addressed individually, whereas it simultaneously emits a strong audio signal that makes the inattentive rest of the group turn toward it. The challenge here is to define and delimit adequate mixed attention states for groups of people, allowing for classifying collective attention based on inhomogeneous variants of individual attention, i.e., where some group members might be highly attentive, others even interacting with the public display, and some unperceptive. In this article, we present a model for estimating collective human attention toward a public display and investigate technical methods for practical implementation that employs measurement of physical expressive features of people appearing within the display's field of view (i.e., the basis for deriving a person's attention). We delineate strengths and weaknesses and prove the potentials of our model by experimentally exerting influence on the attention of groups of passers-by in a public gaming scenario.
A Framework for Evaluating Software on Reduced Margins Hardware

To improve power efficiency, researchers are experimenting with dynamically adjusting the voltage and frequency margins of systems to just above the minimum required for reliable operation. Traditionally, manufacturers did not allow reducing these margins. Consequently, existing studies use system simulators, or software fault-injection methodologies, which are slow, inaccurate and cannot be applied on realistic workloads. However recent CPUs allow the operation outside the nominal voltage/frequency envelope. We present eXtended Margins eXperiment Manager (XM^2) which enables the evaluation of software on systems operating outside their nominal margins. It supports both bare-metal and OS-controlled execution using an API to control the fault injection procedure and provides automatic management of experimental campaigns. XM^2 requires, on average, 5.6% extra lines of code and increases the application execution time by 2.5%. To demonstrate the flexibility of XM^2, we perform three case studies: two employing bare-metal execution on a raspberry PI, and one featuring a full-fledged software stack (including OS) on an Intel Skylake Xeon processor.
IT Governance Steering Committee, IT Competence and IT Governance Performance in Ugandan Public Universities

Past research has largely focused on Boards IT governance responsibilities ignoring top management’s involvement in IT governance and how it might influence organizational capabilities and performance outcomes. This paper examined the relationship between IT governance steering committee, IT competence and IT governance performance in Ugandan public Universities. The study adopted a quantitative, descriptive and cross-sectional research designs. Regression analysis was used to analyze the data. The results indicate quantitative evidence of significant positive relationships between IT governance steering committee, IT competence and IT governance performance. Findings also reveal that IT governance steering committee and IT competence are significant predictors of IT governance performance. The results also suggest that IT competence has a stronger influence on IT governance performance than IT governance steering committee influence. Discussions on the findings, implication and limitation of the study were also provided.