Miracle-dz/UniFAR-FTUs huggingface dataset:
Lattice Reduction Meets Key-Mismatch: New Misuse Attack on Lattice-Based NIST Candidate KEMs

Resistance to key misuse attacks is a vital property for key encapsulation mechanisms(KEMs) in NIST-PQC standardization process. In key mismatch attack, the adversary recovers reused secret key with the help of an oracle O that indicates whether the shared key matches or not. Key mismatch attack is more powerful when fewer oracle queries are required. A series of works tried to reduce query times, Qin et al. [AISACRYPT 2021] gave a systematic approach to finding lower bound of oracle queries for a category of KEMs, including NIST’s third-round candidate Kyber and Saber. In this paper, we found the aforementioned bound can be bypassed by combining Qin et al. (AISACRYPT 2021)’s key mismatch attack with a standard lattice attack. In particular, we explicitly build the relationship between the number of queries to the oracle and the bit security of the lattice-based KEMs. Our attack is inspired by the fact that each oracle query reveals partial information of reused secrets, and affects the mean and the covariance parameter of secrets, making the attack on lattice easier. In addition, We quantify such an effect in theory and estimate the security loss for all NIST second-round candidate KEMs.Specifically, Our improved attack reduces the number of queries for Kyber512 by 34% from 1312 queries with bit security 107 to 865 with bit security 32. For Kyber768 and Kyber1024, our improved attack reduces the number of queries by 29% and 27% with bit security is 32.
23 shades of self-admitted technical debt: an empirical study on machine learning software

In software development, the term “technical debt” (TD) is used to characterize short-term solutions and workarounds implemented in source code which may incur a long-term cost. Technical debt has a variety of forms and can thus affect multiple qualities of software including but not limited to its legibility, performance, and structure. In this paper, we have conducted a comprehensive study on the technical debts in machine learning (ML) based software. TD can appear differently in ML software by infecting the data that ML models are trained on, thus affecting the functional behavior of ML systems. The growing inclusion of ML components in modern software systems have introduced a new set of TDs. Does ML software have similar TDs to traditional software? If not, what are the new types of ML specific TDs? Which ML pipeline stages do these debts appear? Do these debts differ in ML tools and applications and when they get removed? Currently, we do not know the state of the ML TDs in the wild. To address these questions, we mined 68,820 self-admitted technical debts (SATD) from all the revisions of a curated dataset consisting of 2,641 popular ML repositories from GitHub, along with their introduction and removal. By applying an open-coding scheme and following upon prior works, we provide a comprehensive taxonomy of ML SATDs. Our study analyzes ML SATD type organizations, their frequencies within stages of ML software, the differences between ML SATDs in applications and tools, and quantifies the removal of ML SATDs. The findings discovered suggest implications for ML developers and researchers to create maintainable ML systems.
Diamond-based microwave quantum amplifier

Amplification of weak microwave signals with minimal added noise is of importance to science and technology. Artificial quantum systems, based on superconducting circuits, can now amplify and detect even single microwave photons. However, this requires operating at millikelvin temperatures. Natural quantum systems can also be used for low-noise microwave amplification using stimulated emission effects; however, they generate a higher noise, especially when operating above ~1 K. Here, we demonstrate the use of electron spins in diamond as a quantum microwave amplifier operating with quantum-limited internal noise, even above liquid nitrogen temperatures. We report on the amplifier’s design, gain, bandwidth, saturation power, and noise. This capability can lead the way to previously unavailable quantum science, engineering, and physics applications.
Data-Driven Equations for Coevolving Network Systems

The framework of coevolving networks is a tool to model large-scale interacting dynamical systems undergoing change in connective structure, describing phenomena such as epidemic spreading on social networks with individuals changing their connections to avoid infection. Along with direct computational simulation, coevolving network systems are often formulated in terms of systems of ordinary differential equations in their descriptive statistics. The equation approach reduces the computational burden of analyzing the systems, but deriving equations becomes difficult as the underlying model becomes more complicated and as the desire for accuracy increases. We present an approach to construct equations for coevolving network systems automatically, using data from computational simulations and a formulation of sparse model identification. Using this approach we construct a data-driven system of equations for a coevolving SIS model that reproduces system behavior in both temporal evolution and dependence of steady states on system parameters.
Development of black ice prediction model using GIS-based multi-sensor model validation

Abstract. Fog, freezing rain, and snow (melt) quickly condense on road surfaces, forming black ice that is difficult to identify and causes major accidents on highways. As a countermeasure to prevent icing car accidents, it is necessary to predict the amount and location of black ice. This study advanced previous models through machine learning and multi-sensor-verified results. Using spatial (hill shade, river system, bridge, and highway) and meteorological (air temperature, cloudiness, vapour pressure, wind speed, precipitation, snow cover, specific heat, latent heat, and solar radiation energy) data from the study area (Suncheon–Wanju Highway in Gurye-gun, Jeollanam-do, South Korea), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with a geographic information system in units of square metres. The intermediate factors calculated as input factors were road temperature and road moisture, modelled using a deep neural network (DNN) and numerical methods. Considering the results of the DNN, the root mean square error was improved by 148.6 % and reliability by 11.43 % compared to a previous study (linear regression). Based on the model results, multiple sensors were buried at four selected points in the study area. The model was compared with sensor data and verified with the upper-tailed test (with a significance level of 0.05) and fast Fourier transform (freezing does not occur when frequency = 0.00001 Hz). Results of the verified simulation can provide valuable data for government agencies like road traffic authorities to prevent traffic accidents caused by black ice.
Strengthening Amplitude and Impact of the Paciﬁc Meridional Mode on ENSO in the Warming Climate Depicted by CMIP6 Models

: The Paci ﬁ c meridional mode (PMM) has been suggested to play an important role in modulating the development of El Ni ˜ no – Southern Oscillation (ENSO). In this study, we examine the projected changes in the PMM and its impact on ENSO under greenhouse gas forcing using the models of phase 6 of the Coupled Model Intercomparison Pro- ject. These models can properly reproduce the characteristics of PMM patterns but reveal discrepant PMM – ENSO relationships owing to different wind – evaporation – sea surface temperature (SST) (WES) feedback ef ﬁ ciency and different magnitude of atmospheric convection response to SST anomalies. We select the models that show good performance in simulating the PMM and its impact on ENSO for investigation of future projections. Results show potential increases in both PMM amplitude and its impact on ENSO under the shared socioeconomic pathway (SSP) 585 (SSP585) warming sce- nario with great intermodel consensus. Diagnosis of the WES feedback indicates increasing sensitivity of latent heat ﬂ ux to zonal wind speed in a warming climate, which seems to be the main reason for the projected strengthening PMM and its impact on ENSO. In addition, a slightly intensi ﬁ ed response of atmospheric convection to SST anomalies in the subtropical Paci ﬁ c may partially contribute to a stronger PMM – ENSO relationship. The results from this study highlight the increasing importance of the PMM for ENSO development, which calls for more attention to be paid to the PMM for ENSO prediction. SIGNIFICANCE STATEMENT: Variability of the sea surface temperature in the equatorial Paci ﬁ c related to El Ni ˜ no – Southern Oscillation (ENSO) can exert a great impact on global climate. The development of ENSO is partially modulated by the dominant mode of ocean – atmospheric variation in the subtropical North Paci ﬁ c, namely, the Paci ﬁ c meridional mode (PMM). This study is aimed to understand the change in the amplitude of the PMM and its impact on ENSO due to climate change. Multimodel projections suggest that the PMM will likely become stronger and exert a greater impact on ENSO since the future warmer climate is favorable for the growth of the PMM. These results call for more attention to be paid to the PMM for ENSO prediction.
Query Attribute Recommendation at Amazon Search

Query understanding models extract attributes from search queries, like color, product type, brand, etc. Search engines rely on these attributes for ranking, advertising, and recommendation, etc. However, product search queries are usually short, three or four words on average. This information shortage limits the search engine’s power to provide high-quality services. In this talk, we would like to share our year-long journey in solving the information shortage problem and introduce an end-to-end system for attribute recommendation at Amazon Search. We showcase how the system works and how the system contributes to the long-term user experience through offline and online experiments at Amazon Search. We hope this talk can inspire more follow-up works in understanding and improving attribute recommendations in product search.
How does the social structure of the Rakhine state shape the fertility behavior of Rohingyas?

The purpose of the study to discover that how the fertility behavior of the Rohingya community is shaped by the social structure in Rakhine state. The average number of the fertility of Rohingyas is higher than other similar traditional agrarian society like Bangladesh. Generally, it seems that religion is the main factor for the fertility behavior of Rohingya. But the study found, that religion plays an indirect role in the fertility behavior of the Rohingya community. Survival from extinction, longer family time, culture, security, early marriage, gender discrimination, resistance to contraceptives plays a very important role and have direct impacts on the fertility behavior of Rohingyas. Manipulative use of religion, lack of education, less demand from life, living with less supports the above characteristics. But the vital reason is the social structure of Rohingyas in the Rakhine state, which shapes the all characteristics that are responsible for the fertility behavior of Rohingyas. Oppressive government, highly traditional agrarian society are the two major elements that create this different social structure of Rohingyas in Rakhine. Therefore, the traditional agrarian social structure of Rohingyas are quite different from other traditional agrarian society. This structure also prevents the social factors which are responsible for decreased fertility, like, the emergence of the middle class. The study is guided by a qualitative research method with a macro-sociological perspective. This research was conducted based on both primary and secondary data. Case studies and FGD are used to collect data.
Evaluation of changes in teenage driver exposure

Reports have shown that fewer teenagers have been getting licensed. Consistent with those reports, the level of insured teenagers has declined between 2006 and 2012. Results suggest that a key factor in the decline has been the recession and the extremely high levels of teen unemployment. Graduated driver licensing laws were shown not to have had a large impact on the recent decline.
High-current Measurement of FDD Layouts

The transformer equipment is sensitive to the effects of high-frequency surges of lightning and switching nature. One of the promising ways to protect against high-frequency surges is to use the protective equipment operating on the principle of the skin effect. An example of such equipment is a frequency dependent device. The work is a development of earlier work with low-voltage testing of prototypes of frequency-dependent devices. The article describes the experimental technique, and also the schemes and layouts of devices. This paper shows experiments with a new promising conductor design, which is a flat conductive bus with a deposited layer of ferromagnetic material. Shown are high-current experiments with models of frequency-dependent devices with round and flat single-layer conductors. The obtained results are analyzed, which show the effect of increasing the current strength on the resistance of the layouts, the dependence of the change in resistance with increasing signal frequency is shown.
International Survey of Pediatric Oncologists' Beliefs and Communication Practices Regarding Symptom Self-Monitoring by Childhood Cancer Survivors

PURPOSE: Childhood cancer survivors report self-monitoring for and worrying about symptoms of disease recurrence and secondary cancers, although symptom-related worry is associated with poorer health-related quality of life. This survey captured pediatric oncologists' beliefs and communication practices regarding symptom self-monitoring for childhood cancer survivors. METHODS: Using a closed-loop snowball sampling technique, pediatric oncologists completed an online survey regarding the importance of symptom self-monitoring for off-therapy patients, the degree to which symptom self-monitoring was perceived to cause stress and worry, and communication practices. RESULTS: 196 pediatric oncologists (White [78%]; female [64%]; Mage = 47 years) from every continent except Antarctica participated. Oncologists believed it is important for off-therapy patients to self-monitor for symptoms of cancer recurrence (90%) and treatment late effects (94%), although some noted that recurrence (30%) and late effects (55%) are typically detected by routine surveillance before symptoms appear. Oncologists varied in their beliefs that off-therapy patients do (31%) or do not (31%) worry unnecessarily about symptoms of recurrence. Two thirds (62%) of oncologists reported often/always discussing with off-therapy patients which symptoms could indicate cancer recurrence, whereas fewer than half (43%) often/always discussed which symptoms were unlikely to indicate recurrence. Oncologists identified a need for education regarding how to communicate around symptom self-monitoring and the potential utility of a screening tool to identify those who worry excessively. CONCLUSION: Despite nearly universal belief that their off-therapy patients should self-monitor for symptoms of disease recurrence and late effects, a substantial proportion of pediatric oncologists do not counsel patients on symptom self-monitoring. Since nearly one-third believe that off-therapy patients worry unnecessarily about symptoms of recurrence, improving patient education regarding which symptoms are and are not medically concerning could decrease stress and improve health-related quality of life for pediatric cancer survivors.
Extraction of glucocorticoids from water into acetonitrile using polyol induced extraction with ultra performance liquid chromatography and triple quadrupole mass spectrometry (PIE-UPLC-MS-MS)

Abstract Polyol-induced Extraction (PIE) was used for the extraction and UPLC-MS-MS was used for the analysis of glucocorticoids in water. The focus of this work was to demonstrate PIE as an effective technique that allows extraction of solutes between miscible solutes using a phase separating agent, with an example analysis of glucocorticoids. In this work glycerol is used as the mass separating agent in the extraction of glucocorticoids from water into acetonitrile. Eight glucocorticoids were extracted by PIE and analyzed by UPLC-MS-MS. Extraction conditions were optimized, and extraction results were compared to non-extracted glucocorticoid standards and to literature methods. Extraction was optimized at 0 °C, in 10 mL of a 1:1 ACN/Water mixture 2 mL of glycerol added as phase separating agent. Calibration curves showed overall correlation of r 2 > 0.99. Precent relative standard deviation below 4%was obtained for all analytes. Limits of detection were obtained between 3–4 ng/mL by the IUPAC method and 40–200 ng/mL by a propagation of errors method. PIE is demonstrated to be a simple and quick method for extracting glucocorticoids from water into a miscible polar solvent, acetonitrile. Graphical Abstract
Lacosamide effects on placental carriers of essential compounds in comparison with valproate: Studies in perfused human placentas

Lacosamide is increasingly being prescribed to pregnant women, although its effects on the developing fetus have not been fully clarified yet. Previously, we have shown that several antiseizure medications, particularly valproate, can affect the expression of carriers of essential compounds in placental cells. Here, our aim was to assess the effect of short ex vivo exposure of human placentas to lacosamide on the expression of carriers of essential nutrients required by the human fetus.
Dual-Factor WBAN Enhanced Authentication System Based on Iris and ECG Descriptors

At present, COVID-19 is still spreading and affecting millions of people worldwide. Minimizing the need for travel can significantly reduce the probability of infection and improve patients’ quality of life. The wireless body area network (WBAN) transmits the patients’ physiological data to the doctor remotely through the sensors in a way that minimizes physical contact with others. However, existing WBAN security authentication schemes have a core limitation that includes weak authentication performance and overconsumption of resources that precludes their widespread adoption in practical applications. Therefore, in this article, an enhanced dual-factor authentication system that addresses the mentioned drawbacks is proposed for securing WBAN resources. By combining iris and electrocardiogram (ECG) features, users would be required to pass the first-level iris authentication before performing the second-level ECG authentication, thus enhancing the overall security scheme of a WBAN system. Furthermore, we examined the existing inter-pulse-interval (IPI) encoding methods and propose a more efficient ECG IPI encoding algorithm, which can effectively shorten the encoding time without affecting the overall encoding performance. Finally, extensive experiments were performed to verify the performance of the proposed dual-factor iris and ECG-based WBAN authentication system using public iris and ECG databases. The experimental results show that the false acceptance rate (FAR) is close to 0.0%, and the false rejection rate (FRR) is close to 3.2%. Findings from this study suggest that the proposed dual-factor authentication scheme could aid the adequate deployment of security schemes to protect WBAN resources in practical applications.
Real-time Occupancy Detection Of On-street Parking Spaces Based On An Edge Device

We developed a system that can detect on-street parking spaces in real-time. The system uses a positioning module, a camera, and NVIDIA Jetson Xavier NX to build the device components on which pruned You Only Look Once v5, Simple Online and Realtime Tracking and counting algorithm are deployed to complete the collection of occupancy, geographic location, and other information of urban on-street parking spaces. Our device can be integrated into the intelligent dashboard cameras onboard taxis or online cars to form a monitoring network. Such a network enables a dynamic update of urban on-street parking occupancy information. More importantly, a long-term collection of this information allows for an analysis of the occupancy rate which is invaluable in terms of on-street parking space planning.
Deep Orientation-Guided Gender Recognition from Face Images

In the recent decade, gender recognition and face analysis has been one of the most researched issues in computer vision. Although several solutions have been provided to the problem of gender recognition from face images, nonetheless, it is regarded as a difficult issue. Deep learning has been proven to solve challenging problems. On the other hand, several existing works have proven their ability to accurately predict the head orientation angles. The remaining error in gender prediction models requires novel solutions to try to improve it further. In this work, we present a novel deep learning-based method to predict gender using both the face image and the head orientation angles. We show that the use of head orientation information consistently boosts the accuracy of gender prediction models. We achieve this by increasing the representational power of deep neural networks by introducing a head orientation adapter. It takes the head angles as input and outputs a vector that is used to recalibrate the deep learning neural networks. The proposed method was tested on a large-scale dataset called AutoPOSE, which has sub-millimeter-accurate head orientation angles. We show that using the head orientation adapter consistently boosts the gender prediction models' accuracy, and reduces the error by 20%.
DMEF: Multi-Exposure Image Fusion Based on a Novel Deep Decomposition Method

In this paper, we propose a novel deep decomposition approach based on Retinex theory for multi-exposure image fusion, termed as DMEF. According to the assumption of Retinex theory, we firstly decompose the source images into illumination and reflection maps by the data-driven decomposition network, among which we introduce the pathwise interaction block that reactivates the deep features lost in one path and embeds them into another path. Therefore, loss of illumination and reflection features during decomposition can be effectively suppressed. And then the high dynamic range illumination map could be obtained by fusing the separated illumination maps in the fusion network. Thus, the reconstructed details in under-exposed and over-exposed regions will be clearer with the help of the fused reflection map which contains complete high-frequency scene information. Finally, the fused illumination and reflection maps are multiplied pixel-by-pixel to obtain the final fused image. Moreover, to retain the discontinuity in the illumination map where gradient of reflection map changes steeply, we introduce the structure-preservation smoothness loss function to retain the structure information and eliminate visual artifacts in these regions. The superiority of our proposed network is demonstrated by applying extensive experiments compared with other state-of-the-art fusion methods subjectively and objectively.