Miracle-dz/UniFAR-FTUs huggingface dataset:
You Can Use But Cannot Recognize: Preserving Visual Privacy in Deep Neural Networks

—Image data have been extensively used in Deep Neural Network (DNN) tasks in various scenarios, e.g., autonomous driving and medical image analysis, which incurs significant privacy concerns. Existing privacy protection techniques are unable to efficiently protect such data. For example, Differential Privacy (DP) that is an emerging technique protects data with strong privacy guarantee cannot effectively protect visual features of exposed image dataset. In this paper, we propose a novel privacy-preserving framework VisualMixer that protects the training data of visual DNN tasks by pixel shuffling, while not injecting any noises. VisualMixer utilizes a new privacy metric called Visual Feature Entropy (VFE) to effectively quantify the visual features of an image from both biological and machine vision aspects. In VisualMixer , we devise a task-agnostic image obfuscation method to protect the visual privacy of data for DNN training and inference. For each image, it determines regions for pixel shuffling in the image and the sizes of these regions according to the desired VFE. It shuffles pixels both in the spatial domain and in the chromatic channel space in the regions without injecting noises so that it can prevent visual features from being discerned and recognized, while incurring negligible accuracy loss. Extensive experiments on real-world datasets demonstrate that VisualMixer can effectively preserve the visual privacy with negligible accuracy loss, i.e., at average 2.35 percentage points of model accuracy loss, and almost no performance degradation on model training.
BARRIERS OF FALL RISK ASSESSMENT AND PREVENTION IMPLEMENTATION IN HOSPITAL SETTING

Falls are the most worrying incidence of patient safety concern that have an impact on injury and death. 1.9 to 3 percent of patients fall in the hospital, with injuries in 6 to 44 percent. Hospitals are making efforts to reduce those fall rates, but its implementation are hindered by a variety of barriers. This study aims to identify the barriers of fall risk assessment and prevention in the hospital. The research method is systematic review using PRISMA, with 15 included studies. Identified barriers includes insufficient knowledge, lack of motivation, absence of champion, lack of resources, inadequate communication, unsustainable program, and support and access shortage. To successfully implement fall prevention programs in hospitals requires a multifaceted, planned approach that includes: regular education and training for staff and patients; provision of equipment; audit, reminders and feedback; leadership and champions; simple programs; and a framework and time for adaptation in the hospital. Keywords: barriers, fall risk assessment, fall prevention, hospital
Separation is Good: A Faster Order-Fairness Byzantine Consensus

—Order-fairness has been introduced recently as a new property for Byzantine Fault-Tolerant (BFT) consensus protocol to prevent unilaterally deciding the final order of transactions, which allows mitigating the threat of adversarial transaction order manipulation attacks (e.g., front-running) in blockchain networks and decentralized finance (DeFi). However, existing leader-based order-fairness protocols (which do not rely on synchronized clocks) still suffer from poor performance since they strongly couple fair ordering with consensus processes. In this paper, we propose SpeedyFair , a high-performance order-fairness consensus protocol, which is motivated by our insight that the ordering of transactions does not rely on the execution results of transactions in previous proposals (after consensus). SpeedyFair achieves its efficiency through a decoupled design that performs fair ordering individually and consecutively, sepa-rating from consensus. In addition, by decoupling fair ordering from consensus, SpeedyFair enables parallelizing the order/verify mode that was originally executed serially in the consensus process, which further speeds up the performance. We implement a prototype of SpeedyFair on the top of the Hotstuff protocol. Extensive experimental results demonstrate that SpeedyFair significantly outperforms the state-of-the-art order-fairness protocol (i.e., Themis), which achieves a throughput of 1.5 × -2.45 × greater than Themis while reducing latency by 35%-59%.
basin, NE Mexico: Comment and Reply : COMMENT Basinward transport of Chicxulub ejecta by tsunami-induced backflow, La Popa

doi: 10.1130/0091-7613-33.1. e86 2005;33;e86 Geology Wolfgang Stinnesbeck, Armin Schafhauser and Stefan Götz basin, NE Mexico: Comment and Reply: COMMENT Basinward transport of Chicxulub ejecta by tsunami-induced backflow, La Popa Email alerting services cite this article to receive free e-mail alerts when new articles www.gsapubs. org/cgi/alerts click Subscribe to subscribe to Geology www.gsapubs. org/subscriptions/ click Permission request to contact GSA http://www.geosociety. org/pubs/copyrt.htm#gsa click viewpoint. Opinions presented in this publication do not reflect official positions of the Society. positions by scientists worldwide, regardless of their race, citizenship, gender, religion, or political article's full citation. GSA provides this and other forums for the presentation of diverse opinions and articles on their own or their organization's Web site providing the posting includes a reference to the science. This file may not be posted to any Web site, but authors may post the abstracts only of their unlimited copies of items in GSA's journals for noncommercial use in classrooms to further education and to use a single figure, a single table, and/or a brief paragraph of text in subsequent works and to make GSA, employment. Individual scientists are hereby granted permission, without fees or further requests to Copyright not claimed on content prepared wholly by U.S. government employees within scope of their
ReplicaWatcher: Training-less Anomaly Detection in Containerized Microservices

—Despite its detection capabilities against previously unseen threats, anomaly detection suffers from critical limitations, which often prevent its deployment in real-world settings. In fact, anomaly-based intrusion detection systems rely on comprehensive pre-established baselines for effectively identifying suspicious activities. Unfortunately, prior research showed that these baselines age and gradually lose their effectiveness over time, especially in dynamic deployments such as microservices-based environments, where the concept of “normality” is frequently redefined due to shifting operational conditions. This scenario reinforces the need for periodic retraining to uphold optimal performance — a process that proves challenging, particularly in the context of security applications. We propose a novel, training-less approach to monitoring microservices-based environments. Our system, R EPLI - CA W ATCHER , observes the behavior of identical container instances (i.e., replicas ) and detects anomalies without requiring prior training. Our key insight is that replicas, adopted for fault tolerance or scalability reasons, execute analogous tasks and exhibit similar behavioral patterns, which allow anomalous containers to stand out as a notable deviation from their corresponding replicas, thereby serving as a crucial indicator of security threats. The results of our experimental evaluation show that our approach is resilient against normality shifts and maintains its effectiveness without the necessity for retraining. Besides, despite not relying on a training phase, R EPLICA W ATCHER performs comparably to state-of-the-art, training-based solutions, achieving an average precision of 91.08% and recall of 98.35%.
Climate change impacts on marine ecosystems and resources

The Mediterranean Sea is one of the biggest reservoirs of biodiversity in the world. Millions of people directly or indirectly depend on the ecosystem services it provides, in particular the provisioning of fisheries resources. Rather than a hot-spot of biodiversity, the Mediterranean Sea has now become a hot-spot of global change where climate change and other anthropogenic pressures (e.g., overfishing, pollution, habitat destruction) operate independently or synergistically to shape an altered Mediterranean Sea that may shift from the today picture. The set of physical-chemical changes triggered by climate change may disrupt the functioning of the biological components of ecosystems, from the individual up to the ecosystem scale, from the basis of the food webs (macrophytes, phytoplankton) up to the higher trophic levels (e.g., predator fish). Current research shows that the physiology and fish life history traits have changed, that fish distribution areas are moving northward and eastward, thus modifying the structure and the species composition of the communities. The dynamics of populations and food webs are modified as well, and species invasions increase at an IRD_SYN_HC_Q_COP22_Part_2. indd 207 06/10/2016 16:22:27 corp-COP22 HD Page 207 Cyan Magenta Yellow Black The Mediterranean Region under Climate Change 208 unprecedented rate. Combined with fishing, climate change renders marine ecosystems more vulnerable to anthropogenic pressures, to natural hazards and to invasions by non-indigenous species. Although uncertainties remain with regard to the magnitude of expected ecological changes, the projections based on IPCC scenarios all confirm that climate change is a serious threat for the biodiversity and the sustainable exploitation of fishing resources in the Mediterranean Sea. This calls for the reinforcement of innovative and integrated research and assessment capacities to support an ecosystem-based management at the scale of the Mediterranean basin.
Ubiquitous influence of waves on tropical high cirrus clouds

Cirrus clouds in the tropical tropopause layer (TTL) and water vapor transported into the stratosphere have significant impacts on the global radiation budget and circulation patterns. Climate models, however, have large uncertainties in representing dehydration and cloud processes in the TTL, and thus their feedback on surface climate, prohibiting an accurate projection of future global and regional climate changes. Here we use unprecedented airborne measurements over the Pacific to reveal atmospheric waves as a strong modulator of ice clouds in the TTL. Wave‐induced cold and/or cooling conditions are shown to exert a nearly ubiquitous influence on cirrus cloud occurrence at altitudes of 14–18 km, except when air was very recently influenced by convective hydration. We further observe that various vertical scales of cloud layers are associated with various vertical scales of waves, suggesting the importance of representing TTL waves in models.
Influence of particle size and origin of field peas on apparent ileal digestibility of starch and amino acids and standardized ileal digestibility of amino acids when fed to growing pigs

Abstract The objective was to test the hypothesis that particle size and origin of field peas influence the apparent ileal digestibility (AID) of starch, crude protein (CP), and amino acids (AA) and the standardized ileal digestibility (SID) of AA. Three sources of field peas were procured. One source was from the United States and two sources were from Canada. The U.S. source and one of the sources from Canada (i.e., Canada 1) were each divided into two batches and ground to achieve a target particle size of 250 or 450 µm, whereas the other source from Canada (i.e., Canada 2) was only ground to a target particle size of 250 µm. Each batch of field peas was included in one diet as the only source of AA. An N-free diet was used to determine the basal endogenous losses of CP and AA. Six barrows (initial weight: 50.5 kg; SD = 3.7) that had a T-cannula installed in the distal ileum were randomly allotted to a 6 × 6 Latin square design with six diets and six 7-d periods. Ileal digesta from pigs were collected for 2 d after 5 d of adaptation. Data were analyzed using a statistical model that included batch of field peas as the fixed effect and animal and period as the random effects. Contrast statements were used to analyze the effects of particle size, origin, and the interaction between particle size and origin. Results indicated that the AID of starch was increased by reducing the particle size in the U.S. source of field peas, but that was not the case for the Canada 1 source (interaction; P < 0.001). Particle size did not influence the AID of CP or AA, but the Canada 2 source of field peas had greater (P < 0.05) AID of Trp, Ala, Cys, Gly, and Tyr than the field peas from the United States. The SID of CP and AA was also not affected by the particle size of field peas. The SID of CP and Trp was greater (P < 0.05), and the SID of His, Ile, and Thr tended (P < 0.10) to be greater in the Canada 2 source compared with the U.S. source, but no differences between the two Canada sources were observed. In conclusion, a few differences in SID of AA between field peas produced in the United States and peas produced in Canada were observed, but there was no effect on SID of AA of reducing the particle size of field peas from 450 to 250 µm, whereas the AID of starch increased by reducing the particle size only in the field peas from the United States.
IDA: Hybrid Attestation with Support for Interrupts and TOCTOU

—Remote attestation has received much attention recently due to the proliferation of embedded and IoT devices. Among various solutions, methods based on hardware-software co-design (hybrid) are particularly popular due to their low overhead yet effective approaches. Despite their usefulness, hybrid methods still suffer from multiple limitations such as strict protections required for the attestation keys and restrictive operation and threat models such as disabling interrupts and neglecting time-of-check-time-of-use (TOCTOU) attacks. In this paper, we propose a new hybrid attestation method called IDA , which removes the requirement for disabling in-terrupts and restrictive access control for the secret key and attestation code, thus improving the system’s overall security and flexibility. Rather than making use of a secret key to calculate the response, IDA verifies the attestation process with trusted hardware monitoring and certifies its authenticity only if it was followed precisely. Further, to prevent TOCTOU attacks and handle interrupts, we propose IDA+ , which monitors program memory between attestation requests or during interrupts and informs the verifier of changes to the program memory. We implement and evaluate IDA and IDA+ on open-source MSP430 architecture, showing a reasonable overhead in terms of runtime, memory footprint, and hardware overhead while being robust against various attack scenarios. Comparing our method with the state-of-the-art, we show that it has minimal overhead while achieving important new properties such as support for interrupts and DMA requests and detecting TOCTOU attacks.
Pesticide Residues in Fish

The synthetic pesticides had a glorious past and did a yeoman’s service to mankind in increasing agricultural production and improving food and health quality. Before World War II, pesticides in common use were predominantly inorganic materials, such as sulfur, lead, copper, arsenic, boron, and mercury, as well as botanical ones, such as nicotine, pyrethrum, and rotenone. The advent of organic insecticides really began with the discovery of DDT’s insecticidal properties (1939–1942) by Paul Muller. The use of DDT in controlling mosquito vectors saved millions of lives in the Indian subcontinent by preventing malaria. Its astonishing efficacy led to the development of a variety of other synthetic organic pesticides such as aldrin, dieldrin, toxaphene, and endosulfan followed by organophosphates (malathion, diazinon, and chlorpyrifos), carbamates (carbaryl, carbofuran, and Baygon), and synthetic pyrethroids, the second most significant group after organophosphorus compounds.
DeGPT: Optimizing Decompiler Output with LLM

—Reverse engineering is essential in malware analysis, vulnerability discovery, etc. Decompilers assist the reverse engineers by lifting the assembly to the high-level programming language, which highly boosts binary comprehension. However, decompilers suffer from problems such as meaningless variable names, redundant variables, and lacking comments describing the purpose of the code. Previous studies have shown promising performance in refining the decompiler output by training the models with huge datasets containing various decompiler outputs. However, even datasets that take much time to construct cover limited binaries in the real world. The performance degrades severely facing the binary migration. In this paper, we present DeGPT, an end-to-end framework aiming to optimize the decompiler output to improve its readability and simplicity and further assist the reverse engineers in understanding the binaries better. The Large Language Model (LLM) can mitigate performance degradation with its extraordinary ability endowed by large model size and training set containing rich multi-modal data. However, its potential is difficult to unlock through one-shot use. Thus, we propose the three-role mechanism, which includes referee (R_ref), advisor (R_adv), and operator (R_ope), to adapt the LLM to our optimization tasks. Specifically, R_ref provides the optimization scheme for the target decompiler output, while R_adv gives the rectification measures based on the scheme, and R_ope inspects whether the optimization changes the original function semantics and concludes the final verdict about whether to accept the optimizations. We evaluate DeGPT on the datasets containing decompiler outputs of various software, such as the practical command line tools, malware, a library for audio processing, and implementations of algorithms. The experimental results show that even on the output of the current top-level decompiler (Ghidra), DeGPT can achieve 24.4% reduction in the cognitive burden of understanding the decompiler outputs and provide comments of which 62.9% can provide practical semantics for the reverse engineers to help the understanding of binaries. Our user surveys also show that the optimizations can significantly simplify the code and add helpful semantic information (variable names and comments), facilitating a quick and accurate understanding of the binary.
PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

—Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint.
Marine reclamation: recovery of the sea floor community following mine tailing discharge

Introduction The majority of North American mining-related disturbances occur in terrestrial environments. The variable nature of impacts associated with these perturbations has initiated extensive research, the progression of which can be divided into three stages: i) the initial impacts were investigated ii) recovery processes in habitats disturbed by mining were compared to natural recovery processes iii) information resulting from stages I and 2 were used to develop and implement reclamation and mitigation practices.
Exploiting Sequence Number Leakage: TCP Hijacking in NAT-Enabled Wi-Fi Networks

—In this paper, we uncover a new side-channel vulnerability in the widely used NAT port preservation strategy and an insufficient reverse path validation strategy of Wi-Fi routers, which allows an off-path attacker to infer if there is one victim client in the same network communicating with another host on the Internet using TCP. After detecting the presence of TCP connections between the victim client and the server, the attacker can evict the original NAT mapping and reconstruct a new mapping at the router by sending fake TCP packets due to the routers’ vulnerability of disabling TCP window tracking strategy, which has been faithfully implemented in most of the routers for years. In this way, the attacker can intercept TCP packets from the server and obtain the current sequence and acknowledgment numbers, which in turn allows the attacker to forcibly close the connection, poison the traffic in plain text, or reroute the server’s incoming packets to the attacker. We test 67 widely used routers from 30 vendors and discover that 52 of them are affected by this attack. Also, we conduct an extensive measurement study on 93 real-world Wi-Fi networks. The experimental results show that 75 of these evaluated Wi-Fi networks (81%) are fully vulnerable to our attack. Our case study shows that it takes about 17.5, 19.4, and 54.5 seconds on average to terminate an SSH connection, download private files from FTP servers, and inject fake HTTP response packets with success rates of 87.4%, 82.6%, and 76.1%. We responsibly disclose the vulnerability and suggest mitigation strategies to all affected vendors and have received positive feedback, including acknowledgments, CVEs, rewards, and adoption of our suggestions.
The Dark Side of E-Commerce: Dropshipping Abuse as a Business Model

—The impact of e-commerce on today’s society is a global phenomenon. Given the increased demand for online purchases of items, e-commerce platforms often defer item sales to third-party sellers. A number of these sellers are dropshippers , sellers acting as middlemen who fulfill their customers’ orders through third-party suppliers. While this allows customers to access more products on e-commerce sites, we uncover that abusive dropshippers, who exploit the standard permitted dropshipping model, exist, deceiving customers, and damaging other e-commerce sellers. In this paper, we present the first comprehensive study on the characterization of abusive dropshippers and uncover harmful strategies they use to list items and evade account suspension on e-commerce marketplaces. We crawled the web to discover online forums, instructional material, and software used by the abusive dropshipping community. We inductively code forum threads and instructional material and read software documentation, installing when possible, to create an end-to-end lifecycle of this abuse. We also identify exploitative strategies abusive dropshippers use to ensure persistence on platforms. We then interviewed six individuals experienced in e-commerce (legal consultants and sellers) and developed an understanding of how abusive dropshipping harms customers and sellers. Through this, we present five characteristics that warrant future investigation into automated detection of abusive dropshippers on e-commerce platforms. Our efforts present a comprehensive view of how abusive dropshippers operate and how sellers and consumers interact with them, providing a framework to motivate future investigations into countering these harmful operations.