Miracle-dz/UniFAR-FTUs huggingface dataset:
Iteration Causes, Impact, and Timing in Software Development Lifecycle: SLR

Context: Iteration—performing an activity once it has already been done—is unavoidable and omnipresent during software development. Management of iteration is a challenging task due to the lack of detailed analysis and use of different terms for the iteration at different places in software engineering. Objective: In order to manage iteration in a better way during software development processes, we investigate different iterative situations, the causes, the stages at which it can occur during the development, and its impacts. Method: We use the systematic literature review (SLR) method to search using six bibliographic databases. The SLR includes 153 primary studies published from 2007 to February 2017. Results: We identify twenty-two leading causes, five stages, and positive and negative impacts of iteration. Then, we develop a lucid taxonomy of iteration perspectives based on the causes and timing at which it occurs during the software development lifecycle. Conclusions: The frequently reported causes of iteration are defects, code smell, and conflicts, whereas the least referenced causes are poor management and different methods followed by teams. The most cited phase at which iteration occurs during the software development is maintenance. The most cited positive consequence of iteration is quality improvement, whereas the negative impacts of iteration are increasing time, effort, and cost. Our study provides a framework to understand the nature of iteration, what sources can lead to which iterative perspective, a particular iterative situation can have what kind of impacts on project milestones, and also provide research directions.
LQR Control of Single-Phase Grid-Tied PUC5 Inverter With LCL Filter

This paper presents the current control design procedure of a single-phase grid-tied five-level packed U-cell inverter (PUC5) with an LCL output filter. The PUC5 inverter is used as an interface of renewable energy sources, such as solar applications. The LCL filter is calculated according to the grid-tied operation and converter ratings. An optimal controller, based on a linear quadratic regulator with integral action, is designed to inject a sinusoidal current with low harmonic distortion at unity power factor. For that design, the PUC5 inverter is modelled in the D–Q frame. The sensorless voltage control is incorporated into the switching technique to balance the PUC5 capacitor voltage and generate a five-level waveform at the output. Experimental tests are performed on a laboratory benchmark to confirm the theoretical design. The results prove the efficiency and accuracy of the adopted control strategy in a steady state and under transients of grid current, grid inductance, ac and dc voltage amplitudes.
Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs

Cancer is the second most common cause of death in the United States, accounting for 602,350 deaths in 2020. Cancer-related death rates have declined by 27% over the past two decades, partially due to the identification of novel anti-cancer drugs. Despite improvements in cancer treatment, newly approved oncology drugs are associated with increased toxicity risk. These toxicities may be mitigated by pharmacokinetic optimization and reductions in off-target interactions. As such, there is a need for early-stage implementation of pharmacokinetic (PK) prediction tools. Several PK prediction platforms exist, including pkCSM, SuperCypsPred, Pred-hERG, Similarity Ensemble Approach (SEA), and SwissADME. These tools can be used in screening hits, allowing for the selection of compounds were reduced toxicity and/or risk of attrition. In this short commentary, we used PK prediction tools in the optimization of mitogen activated extracellular signal-related kinase kinase 1 (MEK1) inhibitors. In doing so, we identified MEK1 inhibitors with retained activity and optimized predictive PK properties, devoid of hERG inhibition. These data support the use of publicly available PK prediction platforms in early-stage drug discovery to design safer drugs.
Simulation of Radio Communication Blackout Mitigation with DGTD Method

When the plasma frequency is greater than the electromagnetic wave frequency, radio communication is impended due to the reflection, refraction, and absorption of the plasma sheath. It will cause considerable interference to the communication between the ground and the aircraft. To make electromagnetic waves effectively pass through the plasma barrier, it has been shown that covering a dielectric layer on the surface of the aircraft is an effective route. This dielectric layer combines the plasma sheath to constitute a resonator. In this paper, the DG-FETD method is used to simulate the electromagnetic waves passing through uniform and nonuniform plasma. Our numerical simulation demonstrates that the electromagnetic wave can penetrate the plasma sheath and is received by the antenna on the aircraft when the incident radiation frequency is consistent with the resonant frequency.
Utilizing Persistence for Post Facto Suppression of Invalid Anomalies Using System Logs

The robustness and availability of cloud services are becoming increasingly important as more applications migrate to the cloud. The operations landscape today is more complex, than ever. Site reliability engineers (SREs) are expected to handle more incidents than ever before with shorter service-level agreements (SLAs). By exploiting log, tracing, metric, and network data, Artificial Intelligence for IT Operations (AIOps) enables detection of faults and anomalous issues of services. A wide variety of anomaly detection techniques have been incorporated in various AIOps platforms (e.g. PCA and autoencoder), but they all suffer from false positives. In this paper, we propose an unsupervised approach for persistent anomaly detection on top of the traditional anomaly detection approaches, with the goal of reducing false positives and providing more trustworthy alerting signals. We test our method on both simulated and real-world datasets. Our technique reduces false positive anomalies by at least 28%, resulting in more reliable and trustworthy notifications. CCS CONCEPTS • Computing methodologies $\rightarrow$ Anomaly detection;. Software and its engineering $\rightarrow$Maintaining software. ACM Reference Format: Dipanwita Guhathakurta, Pooja Aggarwal, Seema Nagar, and Rohan Arora, Bing Zhou. 2022. Utilizing Persistence for Post Facto Suppression of Invalid Anomalies Using System Logs. In New Ideas and Emerging Results (ICSENIER’22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3510455.3512774
Antimicrobial Properties of Metal Nanoparticles and Their Oxide Materials and Their Applications in Oral Biology

Some scholars have shown that metal nanoparticles have excellent antibacterial properties and can be used as a new type of antibacterial agent. In recent years, with the in-depth research on nanomaterials, its applications in the medical field have gradually increased. The oral cavity has a unique anatomical structure, and for oral infections, the current clinically commonly used treatment measures are oral or topical antibiotics. However, due to bacterial resistance and the special structure of dental plaque, the effect of antibiotics is not ideal. Metal and metal oxide nanoparticles have become the research hotspot of new antibacterial materials due to their small particles, large specific surface area, physical, mechanical, and chemical properties, and antibacterial properties. This article describes the antibacterial effect, antibacterial mechanism, biological toxicity, and application progress of metal nanomaterials in the oral cavity.
Team Matus and Francesco @ AutoMin 2021: Towards Neural Summarization of Meetings

As online meetings are becoming increasingly ubiquitous, there is an increasing demand to record the main outcomes of these meetings for future reference. Automatic summarization of meetings is a challenging, yet relatively unexplored natural language processing task with a wide range of potential applications. This paper describes our submission to the First Shared Task on Automatic Minuting at Interspeech 2021. In contrast to previous research focused on the summarization of narrated documents, we examine the specifics of bullet-point spoken language summarization on the AutoMin dataset of online meetings in English. Furthermore, we investigate whether existing abstractive summarization systems can be transferred to this new domain. In this regard, we develop a minuting pipeline based on the state-of-the-art PEGASUS summarization model. This includes pre-processing of conversational data, few-shot transfer learning using reference minutes generated by human annotators, filtering and post-processing of the resulting candidate summaries into a suitable bullet-point minutes format. We conclude by evaluating the completeness and shortening aspects of our system, and discuss its limitations and potential future research directions
SymInfer: Inferring Numerical Invariants using Symbolic States

We demonstrate the implementation and usage of SymInfer, a tool that automatically discovers numerical invariants using concrete and symbolic states collected from dynamic and symbolic executions. SymInfer supports expressive invariants under various forms, including nonlinear equalities, octagonal inequalities, and disjunctive min/max invariants. Experimental results show that SymInfer is effective in generating complex invariants and can often discover unknown, yet useful program properties. Video demo: https://www.youtube. com/watch? v=VEuhJw1RBUE.
A new domain adaption algorithm based on weights adaption from the source domain

Domain adaption is effective for utilizing the knowledge from a source domain to benefit further learning task in a different but related target domain. It mainly reduces the difference across domains by learning a shared feature representation. So, it also could be taken as a feature transfer algorithm. Besides, another transfer technique, named instance transfer, allows the instances from a source domain to be reused in a target domain by reweighting. Each method has two facets. Instance transfer must satisfy the high similarity between the source domain data and the target domain data, while domain adaption ignores the difference of contributions by the different instances. In this paper, a new domain adaption algorithm is proposed to introduce weights adaption into the feature representation. Specifically, it aims to reweight the instances in new feature space and adapt the distributions across the different domains. In distribution adaption, both the marginal distribution and the conditional distribution are simultaneously adjusted in the unified framework. In this way, the new feature representation is effective for the substantial distribution difference as well as instance difference. The experiments on 36 groups of image datasets show that the knowledge of the source domain is efficiently transferred to classify the target domain. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Safety Margin Prediction Algorithms Based on Linear Regression Analysis Estimates

In this paper, we consider the problem of approximating the safety margin of a single instance of a technical system based on inaccurate observations at specified time points. The solution to this problem is based on the selection of a certain set of reference points in time, in a small neighbourhood of which a sufficiently large number of inaccurate measurements are carried out. Analogously with the failure rate, it is assumed that the rate of decrease in the safety margin over time is represented by a polynomial of the fourth degree, and the safety margin itself is a polynomial of the fifth degree. A system of linear algebraic equations is constructed that determine the coefficients of this polynomial through its values and the values of its derivative at reference points in time. These values themselves are estimated by the method of linear regression analysis based on numerous observations in a small neighbourhood of reference points in time. A detailed computational experiment is carried out to verify the accuracy of the approximation of a fifth-degree polynomial and alternative ways of estimating it are constructed in the vicinity of points where the quality of approximation is insufficient.
Class‐index corpus‐index measure: A novel feature selection method for imbalanced text data

In the field of text classification, some of the datasets are unbalanced datasets. In these datasets, feature selection stage is important to increase performance. There are many studies in this area. However, existing methods have been developed based on the document frequency of only intra‐class. In this study, a new method is proposed considering the situation of the feature in class and corpus. A new feature selection method, namely class‐index corpus‐index measure (CiCi) was presented for unbalanced text classification. The CiCi is a probabilistic method which is calculated using feature distribution in both class and corpus. It has shown a higher performance compared to successful methods in the literature. Multinomial Naïve Bayes and support vector machines were used as classifiers in the experiments. Three different unbalanced datasets are used in the experiments. These benchmark datasets are reuters‐21578, ohsumed, and enron1. Experimental results show that the proposed method has more performance in terms of three different success measures.
Identification of Dobrava-Belgrade Virus in Apodemus flavicollis from North-Eastern Italy during Enhanced Mortality

Hantaviruses include several zoonotic pathogens that cause different syndromes in humans, with mortality rates ranging from 12 to 40%. Most commonly, humans get infected through the inhalation of aerosols or dust particles contaminated with virus-containing rodent excreta. Hantaviruses are specifically associated with the host species, and human cases depend on the presence and the dynamics of reservoir hosts. In this letter, we report the identification of Dobrava-Belgrade virus (DOBV) in the yellow-necked mouse (Apodemus flavicollis) from Italy. The virus was detected in the mountainous area of the province of Udine, bordering Austria and Slovenia, during an event of enhanced mortality in wild mice and voles. Despite serological evidence in rodents and humans that suggested the circulation of hantaviruses in Italy since 2000, this is the first virological confirmation of the infection. Phylogenetic analyses across the whole genome of the two detected viruses confirmed the host-specificity of DOBV sub-species and showed the highest identity with viruses identified in Slovenia and Croatia from both A. flavicollis and humans, with no signs of reassortment. These findings highlight the need for ecologists, veterinarians and medical doctors to come together in a coordinated approach in full compliance with the One Health concept.
Interference-Aware Two-Level Differentiated Transmission for Improving Downlink Spatial Reuse in Dense WLANs

In this study, we address the problem of downlink throughput degradation in dense wireless local area networks (WLANs) based on the IEEE 802.11ax standard. We demonstrate that this problem essentially results from the asymmetric characteristic of carrier sense multiple access between downlink and uplink transmissions in infrastructure WLANs, and it is exacerbated by a dynamic sensitivity control algorithm that aims to improve spatial reuse (SR) in IEEE 802.11ax. To solve this problem, we propose the interference-aware two-level differentiation mechanism consisting of the dual channel access (DCA) and supplemental power control (SPC) schemes. The proposed mechanism introduces a new measure called a spatial reusability indicator, which roughly estimates the signal-to-interference ratio from the received signal strength of beacon frames. Based on this measure, stations (STAs) are classified into the following two categories: spatial reusable STAs (SR-STAs) and non-spatial reusable STAs (NSR-STAs). Because SR-STAs are more robust to interference than NSR-STAs, the DCA scheme prioritizes transmissions to SR-STAs over those to NSR-STAs by using differentiated carrier sensing thresholds. Moreover, the SPC scheme selectively increases the transmission power to NSR-STAs to compensate for transmission failure due to interference. By combining the SPC and DCA schemes, the proposed mechanism effectively differentiates the downlink transmissions to SR-STAs and NSR-STAs in terms of channel access and transmission power, and it can boost the possibility of successful SR. The proposed mechanism can be easily implemented in IEEE 802.11ax without any complex calculation or significant signaling overhead. Moreover, we provide a practical guideline to determine appropriate parameter values for use in the proposed mechanism. The extensive simulation results obtained in this study confirm that the proposed mechanism increases the downlink throughput by more than several times without decreasing the overall throughput, compared to the existing mechanisms, and it maintains fairness between SR-STAs and NSR-STAs in terms of the ratio of successful transmission.
Perceived Psychosocial Impact of Lockdown and Life Satisfaction Assessment of Nigerian Working Adults during Coronavirus Pandemic

The study assessed the perceived psychosocial impact of lockdown and life satisfaction of Nigerian working adults during the coronavirus pandemic. A cross-sectional research design was adopted, and 518 working adults participated in the study using the convenience sampling technique. Two sets of questionnaires served as the data collection instruments. Data generated from the study were analyzed using mean, standard deviation, and OLS regression analysis. Results revealed, amongst others, that relationships with family members and neighbors, interaction with office colleagues, social connectedness, the use of social media, and shopping are the major psychosocial domains highly affected by the impact of the lockdown. Also, it was revealed that working adults in Nigeria are slightly satisfied with life during the COVID-19 lockdown. Based on the results, suggestions were made to mitigate the effects of the psychosocial impact of the lockdown and low life satisfaction during and after the lockdown period of the COVID-19 pandemic.
Cylindrical Magnetically Coupled Resonant Wireless Power Transfer System Based on Flexible PCB Coils

In this letter, we design a magnetically coupled resonant wireless power transfer (MCR-WPT) system on the conformal cylindrical surface based on the flexible printed circuit (FPC) coils. The coil has the advantages of small size, lightweight, and high bendability. We combine mathematics and simulations to explore the relationship between the self-inductance/mutual inductance of the flexible coil and the bending radius of the conformal cylindrical surface. Both theoretical and simulation results show that as the bending radius decreases linearly, the self-inductance and mutual inductance of the coil also decrease nonlinearly. In addition, as the bending radius decreases, the transmission efficiency (TE) decreases, and the resonant frequency shifts to a higher range. Finally, measured and simulated results agree well, which further validates the conclusions.
Impact of the Gram-Negative-Selective Inhibitor MAC13243 on In Vitro Simulated Gut Microbiota

New Gram-negative-selective antimicrobials are desirable to avoid perturbations in the gut microbiota leading to antibiotic-induced dysbiosis. We investigated the impact of a prototype drug (MAC13243) interfering with the Gram-negative outer membrane protein LolA on the faecal microbiota. Faecal suspensions from two healthy human donors were exposed to MAC13243 (16, 32, or 64 mg/L) using an in vitro gut model (CoMiniGut). Samples collected 0, 4, and 8 h after exposure were subjected to viable cell counts, 16S rRNA gene quantification and V3-V4 sequencing using the Illumina MiSeq platform. MAC13243 exhibited concentration-dependent killing of coliforms in both donors after 8 h. Concentrations of ≤32 mg/L reduced the growth of aerobic bacteria without influencing the microbiota composition and diversity. An expansion of Firmicutes at the expense of Bacteroidetes and Actinobacteria was observed in the faecal microbiota from one donor following exposure to 64 mg/L of MAC13242. At all concentrations tested, MAC13243 did not lead to the proliferation of Escherichia coli nor a reduced abundance of beneficial bacteria, which are typical changes observed in antibiotic-induced dysbiosis. These results support our hypothesis that a drug interfering with a specific target in Gram-negative bacteria has a low impact on the commensal gut microbiota and, therefore, a low risk of inducing dysbiosis.
The impact of related party sales by listed Chinese firms on earnings informativeness and analysts forecasts

Using a random sample of 140 of China’s listed firms, we show an adverse impact of related party (RP) sales of goods and services on the usefulness of accounting earnings to investors and on the quality of earnings forecasts by financial analysts. Consistent with the contention that RP sales may violate the arm’s-length assumption of regular transactions and consequently impair the representational faithfulness and verifiability of accounting data, we find that earnings of firms engaged in RP sales are at least 33% less informative after controlling for factors known to affect earnings informativeness. We also find that financial analysts are overly credulous in their acceptance of earnings numbers that are contaminated by unreliable RP sales, and provide less accurate and more optimistic earnings forecasts for firms with more RP sales. Overall, our results provide strong empirical evidence on the negative impact of RP transactions on the usefulness of accounting earnings data used by investors and by financial analysts. JEL Classifications: M41, G14, G24
A 10- and 12-Bit Multi-Channel Hybrid Type Successive Approximation Register Analog-to-Digital Converter for Wireless Power Transfer System

This paper presents a successive approximation register (SAR) analog-to-digital converter (ADC) designed for a wireless power transfer system. This is a four–channel SAR ADC structure with 10-bit resolution for each channel, which can also be applied as a single 12-bit ADC. To reduce the area and the number of the required devices in the ADC module, a hybrid-type structure with capacitor and resistor DACs is applied, in which the resistor DAC is shared between channels and determines the seven least significant bits (LSB)s, while the capacitor DAC determines the three most significant bits (MSBs). For the 12-bit operation mode, and to reduce the number of capacitors required in the capacitor DAC, the capacitors of the four channels are shared to determine the five MSBs. A foreground calibration is applied to the capacitor DAC to remedy the gain and offset errors after fabrication. An additional low resistive path is also implemented in the resistor DAC for error correction. The conversion speed for 10- and 12-bit operations reaches up to 1 and 0.5 MS/s, respectively. The prototype ADC is designed in a 180 nm complementary metal-oxide semiconductor (CMOS) process. For 10- and 12-bit operating modes, this ADC module achieves up to 9.71 and 11.76 effective number of bits (ENOBs), respectively.