Miracle-dz/UniFAR-FTUs huggingface dataset:
MidSecThings: Assurance Solution for Security Smart Homes in IoT

The interest over building security-based solutions to reduce the vulnerability exploits and mitigate the risks associated with smart homes in IoT is growing. However, our investigation identified to architect and implement distributed security mechanisms is still a challenge because is necessary to handle security and privacy in IoT middleware with a strong focus. Our investigation, it was identified the significant proportion of the systems that did not address security and did not describe the security approach in any meaningful detail. The idea proposed in this work is to provide middleware aim to implement security mechanisms in smart home and contribute as how guide to beginner developers’ IoT middleware. The advantages of using MidSecThings are to avoid leakage data, unavailable service, unidentification action and not authorized access over IoT devices in smart home.
Performance Optimization of Large Files Writes to Ceph Based on Multiple Pipelines Algorithm

As a cloud storage platform software, Ceph can be used to obtain petabyte-scale storage system built from commodity hardware [1] [2] [3]. In the previous work, we optimized the performance of reads/writes from/to the Ceph storage cluster based on multi-threaded algorithms [4]. Experiment results indicate that the performance of small files reads/writes and large files reads algorithms improve obviously, but the performance of the large files writes to Ceph by the single pipeline algorithm has no evident improvement. To address this problem, we use multiple pipelines algorithm to optimize the performance of the large files writes to the Ceph storage cluster. The experiment results show that when the size of the data block is set to 10MB, the maximal performance improvement percentage of the multiple pipelines algorithm running on the two logical CPUs machine is 100.70%. At the same time, limited by the multi-threaded mechanism of Python language, such as Python GIL and threads thrashing, the performance of the multiple pipelines algorithm running on multiple cores machines does not increase linearly. We intend to optimize the performance of large files writes algorithm using C++ version application program interface of Ceph for the future work.
Ftree-CDN: Hybrid CDN and P2P Architecture for Efficient Content Distribution

In this paper, we propose Ftree-CDN, a hybrid CDN and P2P architecture based on Ftree, a name resolution system for Information-Centric Networking. Using Ftree, contents can be downloaded from the closest CDN servers and peers, and are assigned ISP-aware identifiers for policy-based filtering. In addition, contents are split into chunks in order to foster the storage and downloading processes. Simulation results are reported to show the efficiency of the proposed algorithms according to the splitting degree and content replication.
Dynamic Data Histogram Publishing Based on Differential Privacy

Differential privacy, due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics on privacy protection. In the process of its continuous development, many data publishing algorithms that satisfy the differential privacy histogram are proposed. However, most of these algorithms are focused on the release of static data and less research on dynamic data release. A direct way of publishing dynamic data is to publish a histogram that satisfies the differential privacy at every time point, but this method can lead to high cumulative error and reduce the utility of datasets. In order to solve these problems, we propose a histogram publishing algorithm for differential privacy dynamic data based on Kullback-Leibler(KL) divergence. The algorithm uses KL divergence to calculate the difference between two adjacent data updates. At the same time, for the different values calculated by KL divergence, we adopt three strategies for dynamic data publishing. Extensive experiments on real datasets demonstrate that our algorithm can reduce noise errors and achieves better utility than existing state-of-the-art algorithms.
Digital Divine: Technology use by Indian Spiritual Sects

Spirituality-based organizations in India, centered around a set of beliefs and practices, with a charismatic guru figure at their head, have embraced the information age enthusiastically, and have come to the fore as key players in the national narrative around social welfare and development in recent years. We conducted a qualitative study of four Hinduism-oriented Spirituality-based Organizations (SBOs) in India using interviews, on-site observations, and in-depth examination of their online outreach material to understand the ways in which technology impacts and advances their core functions. We examine five core ways which technology plays a critical role in these SBO - community-building, dissemination of core practices, self-fashioning, philanthropic outreach, and organizational growth -- all of which inform these organizations' influence in society beyond the confines of their adherents. We find that all these functions are enabled in different ways by digital technologies, which have organizational value in and of themselves, but also play an equally important role in helping extend these organizations' public image as modern, innovative organizations aligned with broader aspirations of national development and social welfare.
Understanding Engagement of Parents In Online Health Communities for Early Childhood

Online health communities provide a means for parents to engage with each other and seek and offer support. The parents' conversations in online communities also provide a lens into the parenting practices, challenges, and what role technology can play. Previous studies discuss the engagement of parents on online platforms related to specific childcare concerns. To understand the experiences of parents with online health communities, during the initial years of parenting, we conducted a study on the BabyCenter platform. It is a web forum that provides information on different aspects of early childhood development and offers peer-to-peer interaction. In this paper, we investigate the patterns of users' engagement for early childhood queries. Based on qualitative analysis of publicly available data on BabyCenter, 390 posts from January 2019 to June 2019, we find that the users primarily construct their knowledge, and engage in social interactions on the platform. We also find limited multimedia usage for expressing health concerns. We discuss design implications for parenting forums that meet the multi-faceted needs of parents during the early years of parenting.
Making "fetch" happen: The influence of social and linguistic context on the success of lexical innovations

In an online community, new words come and go: today's "lol" may be replaced by tomorrow's "lulz. " Changes in online writing are usually studied as a social process, with lexical innovations diffusing through a network of individuals in a speech community. But unlike other types of innovation, language change is shaped and constrained by the system in which it takes part. To investigate the links between social and structural factors in language change, we undertake a large-scale analysis of lexical innovation in the online community Reddit. We find that dissemination across many linguistic contexts is a sign of success: words that appear in more linguistic contexts grow faster and live longer. Furthermore, social and context dissemination are complementary. By combining them, it is possible to predict which innovations will stick, and to forecast when the others will begin to decline.
Polymorphisms rs6313 and rs6314 in Serotonin Receptor Gene (HTR2A) and Serotonin Concentration in Autistic Children

Objective Genetic polymorphisms and mutations in candidate genes are considered important in the etiology of autism, and particular interest is focussed on the serotonin system. Here, we used SNP analysis in the serotonin receptor gene to identify differences between autistic and healthy control children. Methods Genetic association of rs6313 (T102C) and rs6314 (C1354T) polymorphisms in HTR2A gene with susceptibility to the development of autism in children were investigated using PCRRFLP, and correlated serotonin levels in blood serum using ELISA method. Results We uniquely found an association between the presence of the T allele at the position rs6313 (OR=2.00, 95%CI: 1.23-3.26, p=0.005), and between the presence of the C allele at the position rs6314 (OR=2.24, 95% CI: 1.47-3.42, p=0.0002) of the serotonin receptor gene under conditions of a decreased ASD incidence. We also noted that T allele at the position rs6313, and C allele at the position rs6314 was 3 times more frequent in Control males than in ASD males. We found no statistical significant correlation between HTR2A SNPs and the blood serum level of serotonin between autistic and control children. Conclusion This study shows the involvement of HTR2A, rather than the concentration of serotonin, in the development of autism, and provides a rationale for future design of therapeutic modalities based on the serotonin system for childhood autism.
Frankenstein: Advanced Wireless Fuzzing to Exploit New Bluetooth Escalation Targets

Wireless communication standards and implementations have a troubled history regarding security. Since most implementations and firmwares are closed-source, fuzzing remains one of the main methods to uncover Remote Code Execution (RCE) vulnerabilities in deployed systems. Generic over-the-air fuzzing suffers from several shortcomings, such as constrained speed, limited repeatability, and restricted ability to debug. In this paper, we present Frankenstein, a fuzzing framework based on advanced firmware emulation, which addresses these shortcomings. Frankenstein brings firmware dumps "back to life", and provides fuzzed input to the chip's virtual modem. The speed-up of our new fuzzing method is sufficient to maintain interoperability with the attached operating system, hence triggering realistic full-stack behavior. We demonstrate the potential of Frankenstein by finding three zero-click vulnerabilities in the Broadcom and Cypress Bluetooth stack, which is used in most Apple devices, many Samsung smartphones, the Raspberry Pis, and many others. Given RCE on a Bluetooth chip, attackers may escalate their privileges beyond the chip's boundary. We uncover a Wi-Fi/Bluetooth coexistence issue that crashes multiple operating system kernels and a design flaw in the Bluetooth 5.2 specification that allows link key extraction from the host. Turning off Bluetooth will not fully disable the chip, making it hard to defend against RCE attacks. Moreover, when testing our chip-based vulnerabilities on those devices, we find BlueFrag, a chip-independent Android RCE.
Mycobacterium abscessus: Environmental Bacterium Turned Clinical Nightmare

Mycobacteria are a large family of over 100 species, most of which do not cause diseases in humans. The majority of the mycobacterial species are referred to as nontuberculous mycobacteria (NTM), meaning they are not the causative agent of tuberculous (TB) or leprosy, i.e., Mycobacterium tuberculous complex and Mycobacterium leprae, respectively. The latter group is undoubtedly the most infamous, with TB infecting an estimated 10 million people and causing over 1.2 million deaths in 2017 alone TB and leprosy also differ from NTM in that they are only transmitted from person to person and have no environmental reservoir, whereas NTM infections are commonly acquired from the environment. It took until the 1950′s for NTM to be recognised as a potential lung pathogen in people with underlying pulmonary disease and another three decades for NTM to be widely regarded by the medical community when Mycobacterium avium complex was identified as the most common group of opportunistic pathogens in AIDS patients. This review focuses on an emerging NTM called Mycobacterium abscessus (M. abs). M. abs is a rapidly growing NTM that is responsible for opportunistic pulmonary infections in patients with structural lung disorders such as cystic fibrosis and bronchiectasis, as well as a wide range of skin and soft tissue infections in humans. In this review, we discuss how we came to understand the pathogen, how it is currently treated and examine drug resistance mechanisms and novel treatments currently in development. We highlight the urgent need for new and effective treatments for M. abs infection as well as improved in vivo methods of efficacy testing.
Towards a Positive Theory of Regulatory Enforcement of Financial Reporting ∗

This paper develops a theory of regulatory enforcement in a multi-firm setting where enforcement and investments are jointly determined by economic fundamentals. Enforcement disciplines firms’ misreporting of investment outcomes, which reduces the reporting bias as well as the market discount, and increases each firm’s stand-alone investment efficiency. However, the regulator is unable to commit to any long-term enforcement policy ex-ante but chooses the enforcement intensity after the investments are observed. Anticipating the ex-post optimal enforcement policy, firms face a coordination problem because it is the aggregate investment that determines the ex-post social benefit of enforcement. Hence the well-known positive correlation between regulatory enforcement intensity of securities laws and capital market development can be the manifestation of a two-way relationship between these two endogenous objects. As a consequence, the market can be over-sized and over-regulated or under-sized and under-regulated. The model also explains why regulatory enforcement intensity varies across markets and over time, as well as suggesting that aggregate investment (market-size) may not be a good measure of aggregate efficiency to evaluate alternative accounting policies.
Breaking Type Safety in Go: An Empirical Study on the Usage of the unsafe Package

A decade after its first release, the Go language has become a major programming language in the development landscape. While praised for its clean syntax and C-like performance, Go also contains a strong static type-system that prevents arbitrary type casting and memory access, making the language type-safe by design. However, to give developers the possibility of implementing low-level code, Go ships with a special package called unsafe that offers developers a way around the type safety of Go programs. The package gives greater flexibility to developers but comes at a higher risk of runtime errors, chances of non-portability, and the loss of compatibility guarantees for future versions of Go. In this paper, we present the first large-scale study on the usage of the unsafe package in 2,438 popular Go projects. Our investigation shows that unsafe is used in 24 percent of Go projects, motivated primarily by communicating with operating systems and C code, but is also commonly used as a means of performance optimization. Developers are willing to use unsafe to break language specifications (e.g., string immutability) for better performance and 6 percent of the analyzed projects that use unsafe perform risky pointer conversions that can lead to program crashes and unexpected behavior. Furthermore, we report a series of real issues faced by projects that use unsafe, from crashing errors and non-deterministic behavior to having their deployment restricted from certain popular environments. Our findings can be used to understand how and why developers break type safety in Go, and help motivate further tools and language development that could make the usage of unsafe in Go even safer.
New production channels for light dark matter in hadronic showers

Hadronic showers transfer a relevant amount of their energy to electromagnetic subshowers. We show that the generation of "secondary" dark photons in these sub-showers is significant and typically dominates the production at low dark photon masses. The resulting dark photons are however substantially less energetic than the ones originating from mesons decay. We illustrate this point both semi-analytically and through Monte Carlo simulations. Existing limits on vector-mediator scenarios for light dark matter are updated with the inclusion of the new production processes.
A Lightweight, Robust Exploitation System for Temporal Stacks of UAS Data: Use Case for Forward-Deployed Military or Emergency Responders

The availability and precision of unmanned aerial systems (UAS) permit the repeated collection of very-high quality three-dimensional (3D) data to monitor high-interest areas, such as dams, urban areas, or erosion-prone coastlines. However, challenges exist in the temporal analysis of this data, specifically in conducting change-detection analysis on the high-quality point cloud data. These files are very large in size and contain points in varying locations that do not align between scenes. These large file sizes also limit the use of this data for individuals with low computational resources, such as first responders or forward-deployed soldiers. In response, this manuscript presents an approach that aggregates data spatially into voxels to provide the user with a lightweight, web-based exploitation system coupled with a flexible backend database. The system creates a robust set of tools to analyze large temporal stacks of 3D data and reduces data size by 78%, all while being able to query the original point cloud data. This approach offers a solution for organizations analyzing high-resolution, temporal point-clouds, as well as a possible solution for operations in areas with poor computational and connectivity resources requiring high-quality, 3D data for decision support and planning.
Visual Attention for Musical Instrument Recognition

In the field of music information retrieval, the task of simultaneously identifying the presence or absence of multiple musical instruments in a polyphonic recording remains a hard problem. Previous works have seen some success in improving instrument classification by applying temporal attention in a multi-instance multi-label setting, while another series of work has also suggested the role of pitch and timbre in improving instrument recognition performance. In this project, we further explore the use of attention mechanism in a timbral-temporal sense, a la visual attention, to improve the performance of musical instrument recognition using weakly-labeled data. Two approaches to this task have been explored. The first approach applies attention mechanism to the sliding-window paradigm, where a prediction based on each timbral-temporal `instance' is given an attention weight, before aggregation to produce the final prediction. The second approach is based on a recurrent model of visual attention where the network only attends to parts of the spectrogram and decide where to attend to next, given a limited number of `glimpses'.
Warm Conveyor Belts in the ERA-Interim Dataset (1979–2010): Part I: Climatology and Potential Vorticity Evolution

AbstractA global climatology of warm conveyor belts (WCBs) is presented for the years 1979–2010, based on trajectories calculated with Interim ECMWF Re-Analysis (ERA-Interim) data. WCB trajectories are identified as strongly ascending air parcels (600 hPa in 2 days) near extratropical cyclones. Corroborating earlier studies, WCBs are more frequent during winter than summer and they ascend preferentially in the western ocean basins between 25° and 50° latitude. Before ascending, WCB trajectories typically approach from the subtropics in summer and from more midlatitude regions in winter. Considering humidity, cloud water, and potential temperature along WCBs confirms that they experience strong condensation and integrated latent heating during the ascent (typically >20 K). Liquid and ice water contents along WCBs peak at about 700 and 550 hPa, respectively. The mean potential vorticity (PV) evolution shows typical tropospheric values near 900 hPa, followed by an increase to almost 1 potential vorticity uni.
Cluster Diffusing Shuffles

Unbiased shuffling algorithms, such as the Fisher-Yates shuffle, are often used for shuffle play in media players. These algorithms treat all items being shuffled equally regardless of how similar the items are to each other. While this may be desirable for many applications, this is problematic for shuffle play due to the clustering illusion, which is the tendency for humans to erroneously consider 'streaks' or 'clusters' that may arise from samplings of random distributions to be non-random. This thesis attempts to address this issue with a family of biased shuffling algorithms called cluster diffusing (CD) shuffles which are based on disordered hyperuniform systems such as the distribution of cone cells in chicken eyes, the energy levels of heavy atomic nuclei, the eigenvalue distributions of various types of random matrices, and many others which appear in a variety of biological, chemical, physical, and mathematical settings. These systems suppress density fluctuations at large length scales without appearing ordered like lattices, making them ideal for shuffle play. The CD shuffles range from a random matrix based shuffle which takes $O(n^3)$ time and $O(n^2)$ space to more efficient approximations which take $O(n)$ time and $O(n)$ space.
Did State-sponsored Trolls Shape the US Presidential Election Discourse? Quantifying Influence on Twitter

It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called "troll" accounts were able to manipulate public opinion is still in question. Here, we aim to quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets from 9.9 million users, including 822 troll accounts. The data collected during the US election campaign, contain original troll tweets. From these data, we constructed a very large interaction graph; a directed graph of 9.3 million nodes and 169.9 million edges. Recently, Twitter released datasets on the misinformation campaigns of 8,275 state-sponsored accounts linked to Russia, Iran and Venezuela. These data serve as a ground-truth identifier of troll users in our dataset. Using graph analysis techniques along with a game-theoretic centrality measure, we quantify the influence of all Twitter accounts (authentic users and trolls) on the overall information exchange as is defined by the retweet cascades. Then, we provide a global influence ranking of all Twitter accounts and we find that only four troll accounts appear in the top-1000 and only one in the top-100. This along with other findings presents evidence that the authentic users were the driving force of virality and influence in the network.