Miracle-dz/UniFAR-FTUs huggingface dataset:
CryptoShield - Automatic On-Device Mitigation for Crypto API Misuse in Android Applications

Misuse of cryptographic APIs remains one of the most common flaws in Android applications. The complexity of cryptographic APIs frequently overwhelms developers. This can lead to mistakes that leak sensitive user data to trivial attacks. Despite herculean efforts by platform provider Google, countermeasures introduced so far were not successful in preventing these flaws. Users remain at risk until an effective systemic mitigation has been found. In this paper, we propose a practical solution that mitigates crypto API misuse in compiled Android applications. It enables users to protect themselves against misuse exploitation until the research community has identified an effective long-term solution. CryptoShield consists of generic mitigation procedures for the most critical crypto API misuse scenarios and an implementation that autonomously extends protection onto all applications on an unrooted Android device. Our on-device CryptoShield Agent injects an instrumentation module into application packages, where it can intercept crypto API calls for detecting misuse and applying mitigations. Our solution was designed for real-world applicability. It retains the update flow through Google Play and can be integrated into existing MDM infrastructure. As a demonstration of CryptoShield’s efficiency and efficacy, we conduct automated (1604 apps) and manual (99 apps) analyses on the most popular applications from Google Play, as well as measurements on synthetic benchmarks. Our solution mitigates crypto API misuse in 96 % of all vulnerable apps, while retaining full functionality for 92 % of all apps. On-device instrumentation takes roughly 11 seconds per application package on average, with minimal impact on package size (5 %) and negligible runtime overhead (571 ms on average app launches, 101 ms worst-case mitigation overhead per crypto API call).
WinkFuzz: Model-based Script Synthesis for Fuzzing

Kernel fuzzing is important for finding critical kernel vulnerabilities. Close-source (e.g., Windows) operating system kernel fuzzing is even more challenging due to the lack of source code. Existing approaches fuzz the kernel by modeling syscall sequences from traces or static analysis of system codes. However, a common limitation is that they do not learn and mutate the syscall sequences to reach different kernel states, which can potentially result in more bugs or crashes. In this paper, we propose WinkFuzz, an approach to learn and mutate traced syscall sequences in order to reach different kernel states. WinkFuzz learns syscall dependencies from the trace, identifies potential syscalls in the trace that can have dependent subsequent syscalls, and applies the dependencies to insert more syscalls while preserving the dependencies into the trace. Then WinkFuzz fuzzes the synthesized new syscall sequence to find system crashes. We applied WinkFuzz to four seed applications and found a total increase in syscall number of 70.8%, with a success rate of 61%, within three insert levels. The average time for tracing, dependency analysis, recovering model script, and synthesizing script was 600, 39, 34, and 129 seconds respectively. The instant fuzzing rate is 3742 syscall executions per second. However, the average fuzz efficiency dropped to 155 syscall executions per second when the initializing time, waiting time, and other factors were taken into account. We fuzzed each seed application for 24 seconds and, on average, obtained 12.25 crashes within that time frame.
A Trade-off SVP-solving Strategy based on a Sharper pnj-BKZ Simulator

The lattice-based cryptography is one of the most promising candidates in the era of post-quantum cryptography. It is necessary to precisely choose the practical parameters by evaluating the hardness of the underlying hard mathematical problems, such as the shortest vector problem (SVP). Currently, there are two state-of-the-art strategies for solving (approximate) SVP. One is the SVP-solving strategy proposed in G6K[5], which has the least solving time cost but high memory cost requirements; another is to execute progressive BKZ (pBKZ)[8] for pre-processing at first and call the high-dimensional SVP-oracle to find the short vector on the original lattice. Due to the strong pre-processing on the lattice basis, the memory cost of the latter strategy is usually smaller than that of the former strategy, while the time cost of pre-processing is relatively costly. In this paper, we first optimize the pnj-BKZ simulator when the jump value is quite large by giving a refined dimension for free (d4f) estimation. Then, based on our optimized pnj-BKZ simulator, we show a more accurate hardness estimation of LWE by considering technologies such as progressive BKZ pre-processing technology, jump strategy, and d4f technology. Furthermore, based on the sharper pnj-BKZ simulator, we propose an SVP-solving strategy trade-off between G6K and pBKZ, which derives less time cost than pBKZ within less memory compared with G6K. Experimental results show that when solving the TU Darmstadt SVP challenge, our algorithm can save 50%-66% of memory compared with G6K’s default SVP-solving strategy. Moreover, our algorithm speeds up the pre-processing stage by 7-30 times, saving the time cost by 4-6 times compared with the pBKZ default SVP-solving strategy. Using our proposed strategy, we solved the 170-dimensional TU Darmstadt SVP challenge and up to the 176-dimensional ideal lattice challenge.
Blockchain-Based and Fuzzy Logic-Enabled False Data Discovery for the Intelligent Autonomous Vehicular System

Since the beginning of this decade, several incidents report that false data injection attacks targeting intelligent connected vehicles cause huge industrial damage and loss of lives. Data Theft, Flooding, Fuzzing, Hijacking, Malware Spoofing and Advanced Persistent Threats have been immensely growing attack that leads to end-user conflict by abolishing trust on autonomous vehicle. Looking after those sensitive data that contributes to measure the localisation factors of the vehicle, conventional centralised techniques can be misused to update the legitimate vehicular status maliciously. As investigated, the existing centralized false data detection approach based on state and likelihood estimation has a reprehensible trade-off in terms of accuracy, trust, cost, and efficiency. Blockchain with Fuzzy-logic Intelligence has shown its potential to solve localisation issues, trust and false data detection challenges encountered by today’s autonomous vehicular system. The proposed Blockchain-based fuzzy solution demonstrates a novel false data detection and reputation preservation technique. The illustrated proposed model filters false and anomalous data based on the vehicles’ rules and behaviours. Besides improving the detection accuracy and eliminating the single point of failure, the contributions include appropriating fuzzy AI functions within the Road-side Unit node before authorizing status data by a Blockchain network. Finally, thorough experimental evaluation validates the effectiveness of the proposed model.
Duty-cycle dependence of the filamentation effect in gas devices for high repetition rate pulsed x-ray FELs

Time-dependent simulations were carried out to study the duty-cycle dependence of the density depression effect in gas attenuators and gas intensity monitors servicing a high repetition rate pulsed Free-electron laser beam. The evolution of the temperature/density gradients in-between the pulses in the entire gas volume, especially during the on-cycle, were obtained to evaluate the performance of any given pulse. It was found that the actual achieved attenuation in the attenuator or the intensity measured by the gas monitor deviates from the asymptotic value expected for a uniformly spaced pulse train after reaching a steady state, becoming progressively more significant as the duty-cycle tends lower.
The Interval Scoring Rule (Preliminary - check for updated versions)

We present a simple scoring rule which can be used to elicit several characteristics of interest of the subjective beliefs of an agent about a random variable. The agent has to choose an interval. If the realization of the random variable falls inside the specified interval she receives a reward, which is decreasing in the width of the interval. If the belief distribution is single peaked, then the optimal interval will cover the mode and the median. Moreover, the optimal interval widens when beliefs of the agent become more dispersed. In contrast to other scoring rules, these results
Semi-automated retrieval of chemical and phylogenetic information from natural products literature

Natural products (NPs) are metabolites of great importance due to their fundamental biological role in performing specialized activities, ranging from basic cellular functions to complex ecological interactions. These metabolites have contributed to innovating fields such as agriculture and medicine due to their optimized biological activities, a consequence of evolution. A key factor in ensuring that isolated NPs are novel is to search scientific literature and compare pre-existing chemical entities with the new isolate. Unfortunately, articles are typically not machine-readable, a problem that hinders efficient searching and increases the chances of unintended rediscovery. In addition, the time required to add new compound discoveries to compound databases hinders computational studies on cell metabolism and Quantitative Structure-Activity Relationships (QSAR). Here, we present a modularized tool that uses text mining techniques to retrieve chemical entities and taxonomic mentions present in scientific literature, called NPMINE (Natural Products MINIng). We were able to analyze 55,382 scientific articles from some of the most important applied chemistry journals from Brazil and the world, consistently recovering the expected taxonomic and structural information. This processing resulted in 120,970 unique InChI Keys potentially associated with 21,526 unique species mentioned. Using the PubChem BioAssay database we show how QSAR models can be used to mine active leads. The results indicate that NPMINE not only facilitates natural products cataloging, but also assists in biological source assignment and structure-activity relationships, a time-consuming task, typically performed in low throughput.
Non-covalent Linking Antibody to Yeast Using the Cell Surface Display of Staphylococcal Protein A

Yeast surface display represents a commonly used platform suitable for the generation and screening of antibodies as well as the selection of high-producer clones. The methods of yeast display rely on the genetic fusion of recombinant antibodies to an abundant cell wall protein of yeast. Here, the study of proof of concept showed that the conventional strategy of expression of fusion antibodies was replaced by non-covalent binding to antibodies for yeast surface display. The use of cell surface display of an epitope tag will endow the cells with new arms for immobilizing, absorbing or targeting the proteins. Staphylococcal protein A, which is characterized by its ability to bind selectively to the Fc region of IgG, was examined to be expressed on the surface of Saccharomyces cerevisiae using a secretion signal of Rhizopus oryzae glucoamylase and C-terminal half of α−agglutinin including glycosylphosphatidylinositol (GPI) anchor attachment signal under the control of the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) promoter. On the other hand, an Fc-fused enzyme was created to construct a molecular fusion of Rhizopus oryzae Lipase with the spacer and the Fc region of IgG heavy chain. The secretion of fusion protein was carried out using pre-α-factor leader region as secretion signal under the control of the 5’-upstream region of the Candida tropicalis isocitrate lyase gene (UPR-ICL) in S. cerevisiae. The Fc-fused lipase was captured by Staphylococcal protein A as an adaptor protein displayed on the surface of yeast cells. The method of this switchable yeast display takes advantage of the “secretion-and-capture” strategy and can be applied to improve the efficiency of yeast display of full-length IgG. graphical abstract Staphylococcal protein A, which has the ability to binding to the Fc region of IgG and leaving the antigen combing site free, has been assembled on the surface of yeast cells to target antibodies or enzymes with Fc fusion.
Tetraose steroidal glycoalkaloids from potato provide resistance against Alternaria solani and Colorado potato beetle

Plants with innate disease and pest resistance can contribute to more sustainable agriculture. Natural defence compounds produced by plants have the potential to provide a general protective effect against pathogens and pests, but they are not a primary target in resistance breeding. Here, we identified a wild relative of potato, Solanum commersonii, that provides us with unique insight in the role of glycoalkaloids in plant immunity. We cloned two atypical resistance genes that provide resistance to Alternaria solani and Colorado potato beetle through the production of tetraose steroidal glycoalkaloids. Moreover, we provide in vitro evidence to show that these compounds have potential against a range of different (potato pathogenic) fungi. This research links structural variation in steroidal glycoalkaloids to resistance against potato diseases and pests. Further research on the biosynthesis of plant defence compounds in different tissues, their toxicity, and the mechanisms for detoxification, can aid the effective use of such compounds to improve sustainability of our food production.
Host infection dynamics and disease induced mortality modify species contributions to the environmental reservoir

Environmental pathogen reservoirs exist for many globally important diseases and can fuel epidemics, influence pathogen evolution, and increase the threat of host extinction. Species composition can be an important factor that shapes reservoir dynamics and ultimately determines the outcome of a disease outbreak. However, disease induced mortality can change species communities, indicating that species responsible for environmental reservoir maintenance may change over time. Here we examine reservoir dynamics of Pseudogymnoascus destructans, the fungal pathogen that causes white-nose syndrome in bats. We quantified changes in pathogen shedding, infection prevalence and intensity, host abundance, and the subsequent propagule pressure imposed by each species over time. We find that highly shedding species are important during pathogen invasion, but contribute less over time to environmental contamination as they also suffer the greatest declines. Less infected species remain more abundant, resulting in equivalent or higher propagule pressure. More broadly, we demonstrate that high infection intensity and subsequent mortality during disease progression can reduce the contributions of high shedding species to long-term pathogen maintenance.
Predicting non-response to ketamine for depression: a symptom-level analysis of real-world data

Background Ketamine helps some patients with treatment resistant depression (TRD), but reliable methods for predicting which patients will, or will not, respond to treatment are lacking. Methods This is a retrospective analysis of PHQ-9 item response data from 120 military veterans with TRD who received repeated doses of intravenous racemic ketamine or intranasal eskatamine in a real-world clinic. Regression models were fit to individual patients' symptom trajectories and model parameters were analyzed to characterize how different symptoms responded to treatment. Logistic regression classifiers were used to predict treatment response using patients' baseline depression symptoms alone. Finally, by parametrically adjusting the classifier decision thresholds, the full space of models was searched to identify the best models for predicting non-response with very high negative predictive value. Results Model slopes indicated progressive improvement on all nine symptoms, but the symptom of depressed mood improved faster than the symptom of low energy. The first principal component (PC) represented a data-driven measurement of overall treatment response, while the second PC divided the symptoms into affective and somatic subdomains. Logistic regression classifiers predicted response better than chance using baseline symptoms, but these models achieved only 60.2% predictive value. Using threshold tuning, we identified models that can predict non-response with a negative predictive value of 96.4%, while retaining a specificity of 22.1%, suggesting we could successfully identify 22% of individuals who would not respond purely based on baseline symptom scores. Conclusions We developed an approach for identifying a subset of patients with TRD who will likely not respond to ketamine. This could inform rational treatment recommendations to avoid additional treatment failures.
Design and analysis of a photonic crystal-based biosensor for the detection of chikungunya virus

A two-dimensional photonic crystal biosensor is theoretically demonstrated and investigated based on refractive index sensing. It detects the chikungunya virus (CHIKV) in the various blood components with high sensitivity of 421.5 nm/RIU. Simulation and various analyses are done with both normal and infected blood constituents (uric acid, platelets and plasma) to detect CHIKV. The sensor operates within the wavelength range of 1230–1560 nm. The important optical parameters such as sensitivity and quality factor are analyzed and numerical investigations are done with the finite difference time domain method. The sensor is built with a circular array of holes etched on a silicon slab in a hexagonal lattice pattern. A nanohole resonant sensing cavity is placed at the center of two waveguides, enhancing the light–matter interaction of the analyte and trapping the optical mode at the nanohole of the sensor. When the sensor is completely submerged in blood constituents infected with CHIKV (plasma, platelets and uric acid), the resonating modes undergo a shift as the refractive index of each analyte is unique. The sensor has a very high sensitivity of 421.5 nm RIU−1, and a good quality factor of 229.68 for the different blood constituents is obtained. A maximum transmission of 89.25% with a detection limit of 0.01 are obtained and reported in this work.
Foliar application of glycine betaine to ameliorate lead toxicity in barley plants by modulating antioxidant enzyme activity and biochemical parameters

Lead (Pb) toxicity is a major problem in agricultural soil that negatively affects plant growth and development. Glycine betaine (GB) is an effective compatible solute that resists abiotic stress and plays an important role to mitigate various stresses. The present study is the first of its kind on the application of GB to mitigate Pb toxicity on barley cultivars. To elucidate the role of GB in mitigating Pb toxicity of three concentrations (15 mM, 25 mM, and 35 mM) in two barley varieties (BH-959 and BH-946) with and without foliar application of GB (2 mM) was examined. The study found that increasing Pb concentration significantly (p < 0.05) reduced the level of primary metabolites viz. photosynthetic pigments, protein, and carbohydrates in both cultivars upto 42.4%, 38.4% and 39% respectively. However malondialdehyde content, proline content, and antioxidant enzyme activity (SOD, CAT, and POX) were found to increased significantly (p < 0.05) as compared to control treatment upto 83.4%, 83.2% and 51% respectively. In contrast, the application of GB led to significantly (p < 0.05) improved physio-biochemical parameters as well as antioxidant enzyme activity (53%) and reduced oxidative stress along with malondialdehyde content (14.42%) in both varieties. An increment in these parameters revealed that exogenous application of GB (2 mM) significantly improves Pb (up to 35 mM) toxicity in barley plants and its use may be beneficial for crops susceptible to Pb toxicity to improve growth and yield.
Indoor level of BTEX and health risk assessment at science laboratories in a university

The levels of volatile organic compounds (VOCs) such as Benzene, Toluene, Ethylbenzene, and Xylene isomers (BTEX) are important parameters for an indoor air quality assessment. Exposure to BTEX in science laboratories in a university can potentially cause health risks to workers. Active sampling was carried out using Tenax® GR sorbent tubes and analyzed with thermal desorption gas chromatography-mass spectrometry (TD-GCMS) to measure the BTEX levels at selected indoor areas. The average concentration of individual BTEX at the laboratories complex were as follows: Benzene (1.76 ± 0.44 μg/m3), Toluene (8.15 ± 2.99 μg/m3), Ethylbenzene (3.75 ± 0.69 μg/m3), m, p-Xylene (6.07 ± 1.19 μg/m3) and o-Xylene (4.84 ± 0.43 μg/m3). The distribution of the BTEX components varied depending on the floor level, laboratory activities, laboratory size, and interior furniture. Non-cancer risk estimates based on hazard quotient value for BTEX were < 1, indicating unlikely non-cancer risk. Lifetime cancer risk estimates for Benzene were in the range of 1.16 × 10-6 – 2.54 × 10-6 indicating possible cancer risk at the sampled indoor locations. Therefore, science laboratories can potentially pose a health risk to workers from long-term exposure to BTEX.
Design of Pairing Mechanisms for the Device-to-Device Communication System

The potential of device-to-device (D2D) communication lies in improving system performance and user experience. Thus, D2D communication is widely concerned. For the D2D communication system, this work investigates the D2D pairing problem to maximize the sum rate of all D2D pairs. To comprehensively compare the performance of different algorithms, three pairing mechanisms are proposed based on the graph theory and the matching theory. The simulation results show the impact of the network scale on the optimized results. And for small and large network scales, the Hungary algorithm and the minimum distance matching algorithm are selected as the D2D pairing mechanism, respectively.
Political Plants: Art, Design, and Plant Sentience

Abstract:This essay considers a series of examples of contemporary and early twentieth-century artistic projects done in collaboration and conversation with plant scientists around the theme of plant sentience. In particular, it zooms in on the work of the Indian biophysicist Jadagish Chandra Bose and the Indian artist Gaganendranath Tagore in the 1920s and the Italian plant scientist Stephano Mancuso and German artist Carsten Höller in the 2020s. The essay has four interconnected aims. The first is to investigate how and why plant sentience is visually and spatially represented by artists. The second is to show through two broad examples how plant science can be and has been co-opted to serve different political, economic, and ideological positions. The third and broader aim of this essay is to counter a widespread ethical assertion in environmental humanities and animal studies that destabilizing human-nonhuman binaries intrinsically lends itself to projects of environmental justice by encouraging humans to coexist more equitably with other species. In other words, we should not assume that artistic production is spontaneously aligned to ethics of multispecies justice. The fourth and concluding aim is to make the related argument that plant sentience and other ways of knowing and relating across species need to be understood within the context of colonial and extractive histories.