Miracle-dz/UniFAR-FTUs huggingface dataset:
“Thank you for being nice”: Investigating Perspectives Towards Social Feedback on Stack Overflow

The Stack Overflow Q&A community has been frequently criticized for being a harsh, unfriendly environment. Despite numerous calls by the community to improve in this regard, prior work has shown that negative community dynamics continue to deter women, newcomers, and other marginalized groups from getting engaged. Social feedback can play a significant role in shaping community behaviour through group norm reinforcement and can, therefore, be employed as a tool to create more welcoming environments. With this in mind, in this paper we present the design and evaluation of a visible social feedback mechanism for inclusion in a Q&A platform like Stack Overflow. Through an exploratory interview study with 20 Stack Overflow members (10 men, 10 women), we explore users’ perceptions of the mechanism’s potential benefits and drawbacks. Our findings suggest that compared to the men in our study, the women were more open to additional social feedback on Stack Overflow, finding it a potential solution to make Stack Overflow more welcoming. Our interview findings also suggest that such a tool could be used to encourage newcomers and to allow users to show appreciation for supportive phrasing, complementing Stack Overflow’s existing focus on feedback for technically accurate content. open future research on and that promote diversity.
Mitochondrial DNA heteroplasmy distinguishes disease manifestation in PINK1/PRKN-linked Parkinson’s disease

Biallelic mutations in PINK1 and PRKN cause recessively inherited Parkinson's disease (PD). Though some studies suggest that PINK1/PRKN monoallelic mutations may not contribute to risk, deep phenotyping assessment showed that PINK1 or PRKN monoallelic pathogenic variants were at a significantly higher rate in PD compared to controls. Given the established role of PINK1 and Parkin in regulating mitochondrial dynamics, we explored mitochondrial DNA (mtDNA) integrity and inflammation as potential disease modifiers in carriers of mutations in these genes. MtDNA integrity, global gene expression and serum cytokine levels were investigated in a large collection of biallelic (n=84) and monoallelic (n=170) carriers of PINK1/PRKN mutations, iPD patients (n=67) and controls (n=90). Affected and unaffected PINK1/PRKN monoallelic mutation carriers can be distinguished by heteroplasmic mtDNA variant load (AUC=0.83, CI:0.74-0.93). Biallelic PINK1/PRKN mutation carriers harbor more heteroplasmic mtDNA variants in blood (p=0.0006, Z=3.63) compared to monoallelic mutation carriers. This enrichment was confirmed in iPSC-derived and postmortem midbrain neurons from biallelic PRKN-PD patients. Lastly, the heteroplasmic mtDNA variant load was found to correlate with IL6 levels in PINK1/PRKN mutation carriers (r=0.57, p=0.0074). PINK1/PRKN mutations predispose individuals to mtDNA variant accumulation in a dose- and disease-dependent manner. MtDNA variant load over time is a potential marker of disease manifestation in PINK1/PRKN mutation carriers.
Contributions of distributed generation to electric transmission system

Distributed generation (DG) refers to electricity generating plant that is connected to a distribution network rather than the transmission system. At present, small-scale DGs are mostly treated as ‘negative demand’ to the transmission system. However, if their contribution to transmission levels are fully understood and properly assessed, these generators can make into valuable assets to improve operational efficiency and substitute major infrastructure investment. This PhD research aims to addresses this challenge from three key aspects: 1. On assessment methodologies, our current industrial practices to evaluate DG-to- transmission-contribution reveal inherent defects. The method given in the transmission system (SQSS) is not sufficient to reflect today’s dispersed generation technologies; while the method for the distribution system (P2/6) fails to reflect and discriminate between different characteristics of distribution networks that DGs are connected. Overcoming these drawbacks, enhanced frameworks to evaluate DG contribution have been developed in this research. 2. On generator’s contribution, little attention has been paid to photovoltaic (PV) outputs characterization and their integration to the overall evaluation process. Neither SQSS nor P2/6 pays sufficient attantion to evaluating PV’s contribution to system. In this regard, an approach aiming at characterizing PV seasonal outputs is proposed. Integrating with the proposed frameworks, this part of the research completes the DG contribution evaluation architecture. 3. On commercial arrangements, conventional business models largely rely on network investment to meet customer demand. Earning a fixed rate of return on invested capital, incumbent distribution network operator (DNO) businesses are encouraged to invest in network assets, very little has been done to support third party service providers for more efficient network development. In the third part of this research, alternative DNO business models and market mechanisms are proposed to further unlock the potential of DG, substantially increase the potential of their contributions to the transmission system.
Sequential Organ Failure Assessment (SOFA) score for predicting short-term mortality in patients with sepsis in Vietnamese intensive care units: A multicentre, cross-sectional study

Objectives: To compare the accuracy of the SOFA and APACHE II scores in predicting short-term mortality among ICU patients with sepsis in an LMIC. Design: A multicentre, cross-sectional study. Setting: A total of 15 adult ICUs from 14 hospitals, of which 5 are central hospitals, and 9 are provincial, district, or private hospitals, throughout Vietnam. Participants: We included all patients aged [≥]18 years who were admitted to ICUs for sepsis and who were still in ICUs from 00:00 hour to 23:59 hour of the study days (i.e., 9th January, 3rd April, 3rd July, and 9th October of 2019). Main outcome measures: Short-term mortality was the main outcome, including hospital and ICU mortality. Results: Of 252 patients, 40.1% died in hospitals, and 33.3% died in ICUs. SOFA (cut-off value [≥]7.5; AUROC: 0.688 [95% CI: 0.618-0.758]; p<0.001) and APACHE II score (cut-off value [≥]20.5; AUROC: 0.689 [95% CI: 0.622-0.756]; p<0.001) both had a poor discriminatory ability for predicting hospital mortality. However, the discriminatory ability for predicting ICU mortality of SOFA (cut-off value [≥]9.5; AUROC: 0.713 [95% CI: 0.643-0.783]; p<0.001) was better and greater than that of APACHE II score (cut-off value [≥]18.5; AUROC: 0.672 [95% CI: 0.603-0.742]; p<0.001). A SOFA score [≥]8 (OR: 2.717; 95% CI: 1.371-5.382) and an APACHE II score [≥]21 (OR: 2.668; 95% CI: 1.338- 5.321) were independently associated with an increased risk of hospital mortality. Additionally, a SOFA score [≥]10 (OR: 2.194; 95% CI: 1.017-4.735) was an independent predictor of ICU mortality, in contrast to an APACHE II score [≥]19, for which this role did not. Conclusions: Both SOFA and APACHE II scores were worthwhile in predicting hospital and ICU mortality among ICU patients with sepsis. However, due to good discrimination for predicting ICU mortality, the SOFA was preferable to the APACHE II score in predicting short-term mortality.
Deep Learning based Prefetching for Flash

Prefetching in solid-state drives (SSDs) is a process of predicting future block accesses and loading them into the main memory ahead of time. In this paper, we describe the challenges of prefetching in SSDs, elaborate why prior approaches fail to achieve high accuracy, and present a deep neural network (DNN) based prefetching technique that significantly outperforms the state-of the-art. We address the challenges of prefetching in very large sparse address ranges, as well as prefetching in a timely manner by predicting ahead of time. We show our proposed technique outperforms prior prefetching approaches based on Markov chains by up to 8 × and the existing stride prefetchers by up to 800 × and on real-world applications running on cloud servers.
Deep Odometry Systems on Edge with EKF-LoRa Backend for Real-Time Indoor Positioning

Ubiquitous positioning for pedestrians in adverse environments has been a long standing challenge. Despite dramatic progress made by Deep Learning, multi-sensor deep odometry systems still pose a high computational cost and suffer from cumulative drifting errors over time. Thanks to the increasing computational power of edge devices, we propose a novel ubiquitous positioning solution by integrating state-of-the-art deep odometry models on edge with an EKF (Extended Kalman Filter)-LoRa backend. We carefully select and compare three sensor modalities, i.e., an Inertial Measurement Unit (IMU), a millimetre-wave (mmWave) radar, and a thermal infrared camera, and implement their deep odometry inference engines to run in real-time. A pipeline for deploying deep odometry on edge platforms with different resource constraints is proposed. We design a LoRa link for positional data backhaul and project aggregated positions of deep odometry into the global frame. We find that a simple EKF backend is sufficient for generic odometry calibration with over 34% accuracy gains against any standalone deep odometry system. Extensive tests in different environments validate the efficiency and efficacy of our proposed positioning system.
EBSRMF: Ensemble based similarity-regularized matrix factorization to predict anticancer drug responses

Drug sensitivity prediction to a panel of cancer cell lines using computational approaches has been a challenge for two decades. With the emergence of high-throughput screening technologies, thousands of compounds and cancer cell lines panels with drug sensitivity data are publicly available at various pharmacogenomics databases. Analyzing these data is crucial to improve cancer treatment and develop new anticancer drugs. In this work, we propose EBSRMF: Ensemble Based Similarity-Regularized Matrix Factorization, which is a bagging based framework to improve the drug sensitivity prediction on the Cancer Cell Line Encyclopedia (CCLE) data. Based on the fact that similar drugs and cell lines exhibit similar drug response, we have investigated cell line and drug similarity matrices based on gene expression profiles and chemical structure respectively. The drug sensitivity value is used as outcome values which are the half maximal inhibitory concentrations (IC50). In order to improve the generalization ability of the proposed model, a homogeneous ensemble based bagging learning approach is also investigated where multiple SRMF models are used to train N subsets of the input data. The outcome of each training algorithm is aggregated using the averaging method to predict the outcome. Experiments are conducted on two benchmark datasets: CCLE and GDSC. The proposed model is compared with state-of-the-art models using multiple evaluation metrics including Root Means Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The proposed model is quite promising and achieves better performance on CCLE dataset when compared with the existing approaches.
Graph-Based Model Predictive Visual Imitation Learning

One problem of modern robotics is that teaching a robot to perform a particular task requires expert knowledge, time, and many physical trials within its workplace. We as humans can learn a new skill much more efficiently by simply watching another human once. The aim of this thesis is to introduce a complete pipeline from data collection to execution, which enables training a robot with the information encoded in a video of a single human demonstration. Previous methods rely on large amounts of prior training of complex visual detection networks or access to many optimal executions for meta learning. However, we propose a one-shot approach that lies in the domain of visual imitation learning . To this end, we introduce a method for efficient data extraction from RGBD video, a graph-based task-representation that allows for robust generalization, and finally, a model predictive control algorithm using a composition of task-related costs as well as functions enforcing auxiliary requirements of robot manipulation. We present solutions to problems arising from visual inference, the human-robot dynamics gap, inaccuracies in the system model, and executions within novel environments. We also study our results on a Pybullet simulation of the Franka Panda robot with demonstration videos captured in the real world that revolved around the interesting task domain of the social game “Ubungo”.
Larvicidal Activity of Momordica foetida (Cucurbitaceae), Gnidia glauca (Thymelaeaceae) and Vepris soyauxii (Rutaceae) Extracts on Anopheles gambiae Mosquitoes and their Acute Toxicity on Rats

on their Abstract Background: The need to apply alternative strategies to control African malaria vectors such as Anopheles gambiae s.l. is extremely relevant. This study aimed at determining the larvicidal activity of 03 plants against Anopheles gambiae s.l and their acute oral toxicity. Methods: Bioassays were carried out with third-instar larvae of Anopheles gambiae and Anopheles coluzzii mosquitoes adapted to laboratory to check for LC50 and LC90 and mortality rates, 24 and 48 hours post-exposure to aqueous and methanol extracts from Momordica foetida , Gnidia glauca and Vepris soyauxii plants. The testing concentrations ranged from 100 to 450 ppm. The toxicity of their methanol extracts was also evaluated using Wistar rats. Results: Methanol extracts showed high lethal activity against Anopheles gambiae and Anopheles coluzzii larvae as compared to aqueous extracts, with variations by plant and Anopheles species as well as duration of exposure. LC50 and LC90 values recorded upon 24h exposure to Vepris soyauxii methanol-based extracts were 203.92 ppm and 241.46 ppm in Anopheles gambiae and 215.01 ppm and 270.87 ppm in Anopheles coluzzii , respectively. These respective LC50 and LC90 values increased with Gnidia glauca and Momordica foetida extracts. As for the toxicity, the highest concentrations of 750 ppm did not show any symptoms of toxicities and death on Wistar rats. Conclusion: V. soyauxii and other study plant extracts showed promising biological responses Anopheles gambiae and Anopheles coluzzii , and may serve as potential alternative and eco-friendly tool for larval control in African endemic countries.
TenderTee: Secure Tendermint

—Blockchain and distributed ledger technologies have emerged as one of the most revolutionary distributed systems, with the goal of eliminating centralised intermediaries and installing distributed trusted services. They facilitate trustworthy trades and exchanges over the Internet, power cryptocurrencies, ensure transparency for documents, and much more. Committee based-blockchains are considered today as a viable alternative to the original proof-of-work paradigm, since they offer strong consistency and are energy efficient. One of the most popular committee based-blockchain is Tendermint used as core by several popular blockchains such Tezos, Binance Smart Chain or Cosmos. Interestingly, Tendermint as many other committee based-blockchains is designed to tolerate one third of Byzantine nodes. In this paper we propose TenderTee, an enhanced version of Tendermint, able to tolerate one half of Byzantine nodes. The resilience improvement is due to the use of a trusted abstraction, a light version of attested append-only memory, which makes the protocol immune to equivocation (i.e behavior of a faulty node when it sends different faulty messages to different nodes). Furthermore, we prove the correctness of TenderTee for both one-shot and repeated consensus specifications.
Optimization of the ore flow quality characteristics in the quarry in road-rail transport

The article presents the results of science-based problem-solving to improve and create a technology of automated homogenization asbestos ore through the block-oriented approach to the formation of the ore pile. Using the results given in the work provides a solution to important applied tasks such as operational management of the quality of the ore fed to the benefication plant and solving of the important challenges in the field of rational development of the subsoil.
Towards a TEE-based V2V Protocol for Connected and Autonomous Vehicles

—Being safer, cleaner, and more efficient, connected and autonomous vehicles (CAVs) are expected to be the dominant vehicles of future transportation systems. However, there are enormous security and privacy challenges while also considering the efficiency and and scalability. One key challenge is how to effi- ciently authenticate a vehicle in the ad-hoc CAV network and ensure its tamper-resistance, accountability, and non-repudiation. In this paper, we present the design and implementation of Vehicle-to-Vehicle (V2V) protocol by leveraging trusted execution envi- ronment (TEE), and show how this TEE-based protocol achieves the objective of authentication, privacy, accountability and revo- cation as well as the scalability and efficiency. We hope that our TEE-based V2V protocol can inspire further research into CAV security and privacy, particularly how to leverage TEE to solve some of the hard problems and make CAV closer to practice.
Preventing Kernel Hacks with HAKC

—Commodity operating system kernels remain mono-lithic for practical and historical reasons. All kernel code shares a single address space, executes with elevated processor privileges, and has largely unhindered access to all data, including data irrelevant to the completion of a specific task. Applying the principle of least privilege, which limits available resources only to those needed to perform a particular task, to com-partmentalize the kernel would realize major security gains, similar to microkernels yet without the major redesign effort. Here, we introduce a compartmentalization design, called a Hardware-Assisted Kernel Compartmentalization (HAKC), that approximates least privilege separation, while minimizing both developer effort and performance overhead. HAKC divides code and data into separate partitions, and specifies an access policy for each partition. Data is owned by a single partition, and a partition’s access-control policy is enforced at runtime, preventing unauthorized data access. When a partition needs to transfer control flow to outside itself, data ownership is transferred to the target, and transferred back upon return. The HAKC design allows for isolating code and data from the rest of the kernel, without utilizing any additional Trusted Computing Base while compartmentalized code is executing. Instead, HAKC relies on hardware for enforcement.
FDTD Scheme for Interfaces Formed by Space-Time Modulations

Emerging metamaterials formed by space-time modulation involve interfaces that conjunctly move in space and time. This creates a fundamental discretization issue in numerical methods such as the Finite Difference Time Domain (FDTD) method. This paper highlights this issue and resolves it using a numerical frame-hopping scheme. This scheme is validated by comparison with exact analytical results from previous works.
An Exploratory Study of Analyzing JavaScript Online Code Clones

Online code clones occur due to reusing code snippets in software repositories from online resources such as GitHub and Stack Overflow. Previous works have shown that snippets from Stack Overflow are reused in other open-source projects and vice versa. Analysis of online code reusing patterns could identify outdated code, understand developers' practices, and help to design new code search engines. This study analyzed JavaScript online code clones between Stack Overflow and GitHub repositories. We first developed a JavaScript code corpus to search online clones. The clone search results reported 12,579 online clones between 276,547 non-trivial syntactically validated Stack Overflow snippets and 292 GitHub repositories. We manually classified the top 10% (1257) pairs of clones in seven online clone patterns. We observed that around 70% of JavaScript snippets in Stack Overflow posts are copied from GitHub repositories or from other external sources. Moreover, only 30.59% of JavaScript Snippets in Stack Overflow accepted answers could be considered as reusable snippets.
ON RIEMANN ZEROS AND WEIL CONJECTURES

The article aims to motivate the study of the relations between the Riemann zeros, and the zeros of the Weil polynomial of a hyper-elliptic curve over finite fields, beyond the well-known formal analogy. The non-trivial distribution of the p-sectors of the Riemann spectrum recently studied by various authors, represent evidence of a yet unknown algebraic structure exhibited by the Riemann spectrum, supporting the above investigations. This preparatory article consists essentially in a review of the topics involved, and the “maize” of relationships to be clarified subsequently. Examples are provided and further directions of investigation are suggested. It is, if successful, a viable, possibly new approach to proving the Riemann Hypothesis, with hindsight from the proof in finite characteristic and function fields.
Socially Intelligent Genetic Agents for the Emergence of Explicit Norms

Norms help regulate a society. Norms may be explicit (represented in structured form) or implicit. We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations in deciding sanctions and identifying alternative norms. These agents use a genetic algorithm to produce norms and reinforcement learning to learn the values of these norms. We find that applying explanations leads to norms that provide better cohesion and goal satisfaction for the agents. Our results are stable for societies with differing attitudes of generosity.
Thinking outside the bag: Worker-led speculation and the future of gig economy delivery platforms

: Gig economy is presented as disruptive, technologically driven, and forward thinking. Design is explicit in this framing, through use of slick apps to reduce friction and simplify experience for customer and worker. However, this framing is often driven by the platforms, and does not fully recognize the actual experience of work. In this paper we report on a collaborative design process on developing concepts for the future of gig work from a worker-centric perspective. This explicitly does not involve the platforms as stakeholders and uses design fiction as a tool for workers to express fears, joys, and the aspects of their work that are nuanced, reflective and surprising. We reflect on the designs created through this process, the tensions, and opportunities with working with gig working couriers, and issues around power and representation when designing with and for this community.
Demo: Remote Adversarial Attack on Automated Lane Centering

—Advanced driver-assistance systems (ADAS) are widely used by modern vehicle manufacturers to automate, adapt and enhance vehicle technology for safety and better driving. In this work, we design a practical attack against automated lane centering (ALC), a crucial functionality of ADAS, with remote adversarial patches. We identify that the back of a vehicle is an effective attack vector and improve the attack robustness by considering various input frames. The demo includes videos that show our attack can divert victim vehicle out of lane on a representative ADAS, Openpilot, in a simulator.