Miracle-dz/UniFAR-FTUs huggingface dataset:
Guided Multi-Branch Learning Systems for Sound Event Detection with Sound Separation

In this paper, we describe in detail our systems for DCASE 2020 Task 4. The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level pooling module and a semi-supervised learning approach named guided learning. This year, we incorporate multi-branch learning (MBL) into the original system to further improve its performance. MBL uses different branches with different pooling strategies (including instance-level and embedding-level strategies) and different pooling modules (including attention pooling, global max pooling or global average pooling modules), which share the same feature encoder of the model. Therefore, multiple branches pursuing different purposes and focusing on different characteristics of the data can help the feature encoder model the feature space better and avoid over-fitting. To better exploit the strongly-labeled synthetic data, inspired by multi-task learning, we also employ a sound event detection branch. To combine sound separation (SS) with sound event detection (SED), we fuse the results of SED systems with SS-SED systems which are trained using separated sound output by an SS system. The experimental results prove that MBL can improve the model performance and using SS has great potential to improve the performance of SED ensemble system.
Contrast media in patients with kidney disease: An update

Concern for contrast-induced acute kidney injury (CI-AKI) or nephrogenic systemic fibrosis may lead to withholding important studies from patients with kidney disease. However, the actual risk or even the existence of these conditions has recently been called into question. The truth probably lies somewhere in the middle. The risk of acute kidney injury is real, but lower than initially thought.
eNNclave: Offline Inference with Model Confidentiality

Outsourcing machine learning inference creates a confidentiality dilemma: either the client has to trust the server with potentially sensitive input data, or the server has to share his commercially valuable model. Known remedies include homomorphic encryption, multi-party computation, or placing the entire model in a trusted enclave. None of these are suitable for large models. For two relevant use cases, we show that it is possible to keep all confidential model parameters in the last (dense) layers of deep neural networks. This allows us to split the model such that the confidential parts fit into a trusted enclave on the client side. We present the eNNclave toolchain to cut TensorFlow models at any layer, splitting them into public and enclaved layers. This preserves TensorFlow's performance optimizations and hardware support for public layers, while keeping the parameters of the enclaved layers private. Evaluations on several machine learning tasks spanning multiple domains show that fast inference is possible while keeping the sensitive model parameters confidential. Accuracy results are close to the baseline where all layers carry sensitive information and confirm our approach is practical.
Children's Interaction with Motion-Based Touchless Games: Kinecting Effectiveness and Efficiency

Leveraging movement data to support children's learning is appealing and technically challenging. However, there is limited knowledge about exploiting the complete design potential of bodily interplay in learning games. We conducted an in-the-wild study with 8 children, with special educational needs, playing a language based educational motion-based touchless game. We collected children's interaction data (correctness and reaction time), and data regarding the different design elements (game settings) implemented in 90 game sessions. Our analysis shows that number of items on-screen, selection gestures, and time to select items, impact the effectiveness (correctness) and efficiency (reaction time) of the children. We highlight the value of interaction analytics and quantify the relationship between different game design elements and children? s efficiency and effectiveness. Our findings help shape the future of learning research by emphasising the substantial benefits of collecting movement data during children's interaction with learning games.
Prophylactic role of lactic acid prepared from lactobacillus acidophilus on cryptosporidiosis

Cryptosporidium has considered as a prominent enteropathogen of humans and animals. Until recently, there is no totally effective therapy other than a healthy intact immune system. Probiotics reported to stimulate both innate and acquired immunity at mucosal and systemic levels. It has been suggested that probiotics inhibit infection by excretion of substances harmful to one of the parasite’s developmental stages and possibly offer new therapeutic factors for the treatment of cryptosporidiosis . The aim of the present study was to detect the prophylactic effect of lactic acid prepared by Lactobacilli acidophillus against the infection with Cryptosporidium in an rabbits model, by estimating hematological and immunological criteria. The results showed that the using of probiotics induced significant reduction in parasite burden, lymphocyte numbers were increased significantly in infected and treated animals. There is a significant increase in the level of IgG ,IgM , C3 and C4 in infected animals.
Playful User-Generated Treatment: Expert Perspectives on Opportunities and Design Challenges

Virtual reality exposure therapy (VRET) is a promising approach in treating phobias such as fear of heights (acrophobia). VRET provides an effective, cost-efficient, scalable and individually adaptable alternative to traditional exposure therapy. To further foster the potential of VRET, a novel concept called Playful User-generated Treatment (PUT) was derived from expert interviews and literature review. In this paper, we provide additional insights regarding the applicability of PUT in real therapy scenarios. For that purpose, practicing psychotherapists (n=13) participated in an online survey and shared their assessments regarding PUT. By conducting qualitative content analysis (inductive category formation), we identified opportunities and challenges that should be considered for the design of playful VRET systems. Opportunities were seen for preparatory habituation, increased control and self-efficacy, improved interaction, economic usage and a realistic display of anxiety-inducing environments. Challenges included lack of direct communication and realism as well as pseudo-habituation to virtual environments.
The Cookie Hunter: Automated Black-box Auditing for Web Authentication and Authorization Flaws

In this paper, we focus on authentication and authorization flaws in web apps that enable partial or full access to user accounts. Specifically, we develop a novel fully automated black-box auditing framework that analyzes web apps by exploring their susceptibility to various cookie-hijacking attacks while also assessing their deployment of pertinent security mechanisms (e.g., HSTS). Our modular framework is driven by a custom browser automation tool developed to transparently offer fault-tolerance during extended interactions with web apps. We use our framework to conduct the first automated large-scale study of cookie-based account hijacking in the wild. As our framework handles every step of the auditing process in a completely automated manner, including the challenging process of account creation, we are able to fully audit 25K domains. Our framework detects more than 10K domains that expose authentication cookies over unencrypted connections, and over 5K domains that do not protect authentication cookies from JavaScript access while also embedding third party scripts that execute in the first party's origin. Our system also automatically identifies the privacy loss caused by exposed cookies and detects 9,324 domains where sensitive user data can be accessed by attackers (e.g., address, phone number, password). Overall, our study demonstrates that cookie-hijacking is a severe and prevalent threat, as deployment of even basic countermeasures (e.g., cookie security flags) is absent or incomplete, while developers struggle to correctly deploy more demanding mechanisms.
Knowing When to Self-Eat – Fine-Tuning Autophagy Through ATG8 Iso-forms in Plants

Autophagy is a catabolic process that takes place under both normal and adverse conditions and is important for the degradation of various organelles and proteins that are no longer needed. Thus, it can be viewed as both a constitutive recycling machinery and an adaptation mechanism. Increase in the activity of autophagy can be caused by multiple biotic and abiotic stress factors. Though intensive research in the past decade has elucidated many molecular details of plant autophagy, the mechanisms of induction and regulation of the process remain understudied. Here, we discuss the role of ATG8 proteins in autophagic signaling and regulation with an emphasis on the significance of ATG8 diversification for adapting autophagy to the changing needs of plants.
AnonFACES: Anonymizing Faces Adjusted to Constraints on Efficacy and Security

Image data analysis techniques such as facial recognition can threaten individuals' privacy. Whereas privacy risks often can be reduced by adding noise to the data, this approach reduces the utility of the images. For this reason, image de-identification techniques typically replace directly identifying features (e.g., faces, car number plates) present in the data with synthesized features, while still preserving other non-identifying features. As of today, existing techniques mostly focus on improving the naturalness of the generated synthesized images, without quantifying their impact on privacy. In this paper, we propose the first methodology and system design to quantify, improve, and tune the privacy-utility trade-off, while simultaneously also improving the naturalness of the generated images. The system design is broken down into three components that address separate but complementing challenges. This includes a two-step cluster analysis component to extract low-dimensional feature vectors representing the images (embedding) and to cluster the images into fixed-sized clusters. While the importance of good clustering mostly has been neglected in previous work, we find that our novel approach of using low-dimensional feature vectors can improve the privacy-utility trade-off by better clustering similar images. The use of these embeddings has been found particularly useful when wanting to ensure high naturalness and utility of the synthetically generated images. By combining improved clustering and incorporating StyleGAN, a state-of-the-art Generative Neural Network, into our solution, we produce more realistic synthesized faces than prior works, while also better preserving properties such as age, gender, skin tone, or even emotional expressions. Finally, our iterative tuning method exploits non-linear relations between privacy and utility to identify good privacy-utility trade-offs. We note that an example benefit of these improvements is that our solution allows car manufacturers to train their autonomous vehicles while complying with privacy laws.
iDEA: Static Analysis on the Security of Apple Kernel Drivers

Drivers on Apple OSes (e.g., iOS, tvOS, iPadOS, macOS, etc.) run in the kernel space and driver vulnerabilities can incur serious security consequences. A recent report from Google Project Zero shows that driver vulnerabilities on Apple OSes have been actively exploited in the wild. Also, we observed that driver vulnerabilities have accounted for one-third of kernel bugs in recent iOS versions based on Apple's security updates. Despite the serious security implications, systematic static analysis on Apple drivers for finding security vulnerabilities has never been done before, not to mention any large-scale study of Apple drivers. In this paper, we developed the first automatic, static analysis tool iDEA for finding bugs in Apple driver binaries, which is applicable to major Apple OSes (iOS, macOS, tvOS, iPadOS). We summarized and tackled a set of Apple-unique challenges: for example, we show that prior C++ binary analysis techniques are ineffective (i.e., failing to recover C++ classes and resolve indirect calls) on Apple platform due to Apple's unique programming model. To solve the challenges, we found a reliable information source from Apple's driver programming and management model to recover classes, and identified the unique paradigms through which Apple drivers interact with user-space programs. iDEA supports customized, pluggable security policy checkers for its security analysis. Enabled by iDEA, we performed the first large-scale study of 3,400 Apple driver binaries across major Apple OSes and 15 OS versions with respect to two common types of security risks - race condition and out-of-bound read/write, and discovered 35 zero-day bugs. We developed PoC and end-to-end attacks to demonstrate the practical impacts of our findings. A portion of the bugs have been patched by recent Apple security updates or are scheduled to be fixed; others are going through Apple's internal investigation procedure. Our evaluation showed that iDEA incurs a low false-positive rate and time overhead.
RTFM! Automatic Assumption Discovery and Verification Derivation from Library Document for API Misuse Detection

To use library APIs, a developer is supposed to follow guidance and respect some constraints, which we call integration assumptions (IAs). Violations of these assumptions can have serious consequences, introducing security-critical flaws such as use-after-free, NULL-dereference, and authentication errors. Analyzing a program for compliance with IAs involves significant effort and needs to be automated. A promising direction is to automatically recover IAs from a library document using Natural Language Processing (NLP) and then verify their consistency with the ways APIs are used in a program through code analysis. However, a practical solution along this line needs to overcome several key challenges, particularly the discovery of IAs from loosely formatted documents and interpretation of their informal descriptions to identify complicated constraints (e.g., data-/control-flow relations between different APIs). In this paper, we present a new technique for automated assumption discovery and verification derivation from library documents. Our approach, called Advance, utilizes a suite of innovations to address those challenges. More specifically, we leverage the observation that IAs tend to express a strong sentiment in emphasizing the importance of a constraint, particularly those security-critical, and utilize a new sentiment analysis model to accurately recover them from loosely formatted documents. These IAs are further processed to identify hidden references to APIs and parameters, through an embedding model, to identify the information-flow relations expected to be followed. Then our approach runs frequent subtree mining to discover the grammatical units in IA sentences that tend to indicate some categories of constraints that could have security implications. These components are mapped to verification code snippets organized in line with the IA sentence's grammatical structure, and can be assembled into verification code executed through CodeQL to discover misuses inside a program. We implemented this design and evaluated it on 5 popular libraries (OpenSSL, SQLite, libpcap, libdbus and libxml2) and 39 real-world applications. Our analysis discovered 193 API misuses, including 139 flaws never reported before.
Development and Validation of a Disease-Specific Oromandibular Dystonia Rating Scale (OMDRS)

Background: Oromandibular dystonia manifests with sustained or task-specific contractions of the masticatory, tongue, and/or other muscles in the stomatognathic system. Since its symptoms can vary, it has been difficult to objectively measure disease severity and post-treatment changes. Objective: To develop and validate a comprehensive measurement tool for oromandibular dystonia. Methods: An examiner-rated scale included three subscales for severity, disability, and pain, modified specifically for oromandibular dystonia from the Toronto Western Spasmodic Torticollis Rating Scale-2. To evaluate the severity of each subtype of oromandibular dystonia, four of the six items were selected according to the subtype (jaw closing dystonia, tongue dystonia, jaw opening dystonia, jaw deviation [protrusion] dystonia, and lip dystonia). A patient-administered questionnaire based on clinical features and other relevant aspects associated with oromandibular dystonia was developed, which included five subscales: general, eating, speech, cosmetic, and social/family life. The questionnaire, examiner-rated scale, and four subscales (sleep, annoyance, mood, and psychosocial functioning) of the Cervical Dystonia Impact Profile-58 were combined to construct the oromandibular dystonia rating scale (OMDRS). The reliability and validity of the scale were assessed using clinimetric testing. Results: Six hundred and eighteen patients with oromandibular dystonia (394 women and 224 men; mean age, 51.7 years) were evaluated by the OMDRS. The overall OMDRS showed high-level internal consistency measured by Cronbach's alpha (0.95) with a logical factor structure. Cronbach's alpha for the subscales was satisfactory to excellent, ranging from 0.72 to 0.94. All items revealed acceptable inter-rater reliability (kappa > 0.4, interclass correlation coefficient > 0.6). Repeated ratings of videotapes revealed acceptable intra-rater reliability for all items (kappa > 0.76, interclass correlation coefficient > 0.86). Test-retest reliability showed a significant (p < 0.001) correlation efficiency. The OMDRS showed significant (p < 0.001) convergent and discriminant validity and significant (p < 0.001) sensitivity to changes after botulinum toxin therapy. Conclusion: The OMDRS can be useful for the comprehensive evaluation of disease severity, disability, psychosocial functioning, and impact on the quality of life as well as therapeutic changes in patients with oromandibular dystonia.
Examining Mirai's Battle over the Internet of Things

Using hundreds of thousands of compromised IoT devices, the Mirai botnet emerged in late 2016 as a game changing threat actor, capable of temporarily taking down major Internet service providers and Internet infrastructure. Since then, dozens of variants of IoT-based botnets have sprung up, and in today's Internet distributed denial-of-service attacks from IoT devices have become a major attack vector. This proliferation was significantly driven by the public distribution of the Mirai source code, which other actors used to create their own, customized version of the original Mirai botnet. In this paper we provide a comprehensive view into the ongoing battle over the Internet of Things fought by Mirai and its many siblings. Using 7,500 IoT honeypots, we show that we can use 300,000,000 compromisation attempts from infected IoT devices as well as a design flaw in Mirai's random number generator to obtain insights into Mirai infections worldwide. We find that networks and the particular malware strains that plague them are tightly connected, and malware authors over time take over strategies from their competitors. The most surprising finding is that epidemiologically, IoT botnets are not self-sustaining: were it not for continuous pushes from bootstrapping, Mirai and its variants would die out.
The Large Communal Nests of Sociable Weavers Provide Year-Round Insulated Refuge for Weavers and Pygmy Falcons

The primary role of nests as structural support for eggs, nestlings, and incubating parents is well established, but our understanding of their secondary roles and their adaptive features is still limited. Nests can serve a particularly important role in protecting or buffering birds from weather. In hot, arid environments, maximum daily temperatures can exceed a species’ upper critical temperature threshold and during the non-breeding season temperatures may also drop below freezing. Nest structures that help buffer against extreme temperatures may play a crucial role in managing the costs of thermoregulation, especially those nests that are used and maintained year-round. We use extensive year-round data to investigate the thermal benefits of massive colonial structures built by sociable weavers in the arid savannahs of the Kalahari, South Africa. These colonies consist of multiple nesting chambers and are used as roosts when birds are not breeding. We explored whether these structures provide thermal buffering throughout the year and how individual chamber placement within the colony and features of the chambers influenced their thermal buffering capacity. We also investigated whether nest chambers occupied and modified by an obligate nest parasite, the African pygmy falcon provided additional thermal buffering. Our results show that sociable weaver colonies provide thermal benefits throughout the year, buffering both hot and cold ambient extremes. Chambers with longer entrance tunnels provided better insulation than chambers with shorter entrance tunnels, and chambers located toward the center of a colony provided greater insulation than chambers at the edge. Chambers occupied by falcons did not display additional thermal benefits, which may be due to falcons choosing chambers with shorter entrance tunnels. Because falcons are larger than weavers, they may find it harder to enter chambers with longer entrance tunnels, and/or because weavers cease maintenance of those chambers. In conclusion, the communal nests of sociable weaver provide thermal benefits to weavers and heterospecifics alike, creating a more optimal environment for breeding, roosting and reducing thermal stress. In a landscape that is becoming increasingly harsh under climate change, the importance of these structures to the local animal communities may also increase.
Persuasion Strategies Using a Social Robot in an Interactive Storytelling Scenario

The behaviour of a person in a given situation can be explained understanding his personality traits. In this sense, the identification of these traits can be a great value to achieve personalised social influence. Although there are several models of persuasion, few of them take into account the person's personality traits. For this reason, this work describes a persuasion study that takes into account a person's personality. We develop a storytelling decision-making scenario, where the participant receives influencing messages to follow a pattern of behaviour determined by a persuasive agent (an autonomous social robot with assertive behaviour). From the study, we find evidence that the model used within the proposed scenario managed to make participants more engaged in the activity. We found pieces of evidence that the levels of assertiveness of a person can influence their attitude and perception by an agent. Also, we identify that persuasion strategy which uses persuasive arguments are more efficient than strategies that have no arguments. Finally, our proposed persuasion strategies have achieved a good level of successful influence.