Miracle-dz/UniFAR-FTUs huggingface dataset:
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images

Weird, unusual, and uncanny images pique the curiosity of observers because they challenge commonsense. For example, an image released during the 2022 world cup depicts the famous soccer stars Lionel Messi and Cristiano Ronaldo playing chess, which playfully violates our expectation that their competition should occur on the football field.1 Humans can easily recognize and interpret these unconventional images, but can AI models do the same? We introduce WHOOPS! , a new dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. We consider several tasks posed over the dataset. In addition to image captioning, cross-modal matching, and visual question answering, we introduce a difficult explanation generation task, where models must identify and explain why a given image is unusual. Our results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS! We hope our dataset will inspire the development of AI models with stronger visual commonsense reasoning abilities. 2
n-Step Temporal Difference Learning with Optimal n

We consider the problem of finding the optimal value of n in the n-step temporal difference (TD) learning algorithm. We find the optimal n by resorting to a model-free optimization technique involving a one-simulation simultaneous perturbation stochastic approximation (SPSA) based procedure that we adopt to the discrete optimization setting by using a random projection approach. We prove the convergence of our proposed algorithm, SDPSA, using a differential inclusions approach and show that it finds the optimal value of n in n-step TD. Through experiments, we show that the optimal value of n is achieved with SDPSA for arbitrary initial values.
Range-Doppler Spoofing in OFDM Signals for Preventing Wireless Passive Emitter Tracking

Passive emitter tracking (PET) algorithms can estimate both the range and Doppler of a wireless emitter when it uses orthogonal frequency division multiplexing (OFDM). In this paper we are interested to prevent this from happening by an unauthorized receiver (URx). To accomplish that we introduce in the transmitted signal a spoofing signal that varies across subcarriers and successive OFDM symbols. With this technique the emitter is not only able to spoof its actual range and Doppler (allowing covert communication in terms of these two parameters), but is also capable of producing additional false emitter signatures to further confuse the URx. To evaluate the performance of our approach we calculate the range-Doppler response at the URx for different system configurations of an 802.11-based system.
Actor-Critic learning for mean-field control in continuous time

We study policy gradient for mean-field control in continuous time in a reinforcement learning setting. By considering randomised policies with entropy regularisation, we derive a gradient expectation representation of the value function, which is amenable to actor-critic type algorithms, where the value functions and the policies are learnt alternately based on observation samples of the state and model-free estimation of the population state distribution, either by offline or online learning. In the linear-quadratic mean-field framework, we obtain an exact parametrisation of the actor and critic functions defined on the Wasserstein space. Finally, we illustrate the results of our algorithms with some numerical experiments on concrete examples.
Automatic Identification and Extraction of Self-Claimed Assumptions on GitHub

—In software development, due to the lack of knowledge or information, time pressure, complex context, and many other factors, various uncertainties emerge during the development process, leading to assumptions scattered in projects. Being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures). The prerequisite of analyzing and understanding assumptions in software development is to identify and extract those assumptions with acceptable effort. In this paper, we proposed a tool (i.e., SCAMiner) to automatically identify and extract self-claimed assumptions (SCAs) on GitHub projects. To evaluate the applicability of SCAMiner, we ﬁrst presented an example of using the tool to mine SCAs from one large and popular deep learning framework project: the TensorFlow project on GitHub. We then conducted an evaluation of the tool. The results show that SCAMiner can effectively identify and extract SCAs from the repositories on
AidUI: Toward Automated Recognition of Dark Patterns in User Interfaces

Past studies have illustrated the prevalence of UI dark patterns, or user interfaces that can lead end-users toward (unknowingly) taking actions that they may not have intended. Such deceptive UI designs can be either intentional (to benefit an online service) or unintentional (through complicit design practices) and can result in adverse effects on end users, such as oversharing personal information or financial loss. While significant research progress has been made toward the development of dark pattern taxonomies across different software domains, developers and users currently lack guidance to help recognize, avoid, and navigate these often subtle design motifs. However, automated recognition of dark patterns is a challenging task, as the instantiation of a single type of pattern can take many forms, leading to significant variability. In this paper, we take the first step toward understanding the extent to which common UI dark patterns can be automatically recognized in modern software applications. To do this, we introduce AidUI, a novel automated approach that uses computer vision and natural language processing techniques to recognize a set of visual and textual cues in application screenshots that signify the presence of ten unique UI dark patterns, allowing for their detection, classification, and localization. To evaluate our approach, we have constructed ContextDP, the current largest dataset of fully-localized UI dark patterns that spans 175 mobile and 83 web UI screenshots containing 301 dark pattern instances. The results of our evaluation illustrate that AidUI achieves an overall precision of 0.66, recall of 0.67, F1-score of 0.65 in detecting dark pattern instances, reports few false positives, and is able to localize detected patterns with an IoU score of 0.84. Furthermore, a significant subset of our studied dark patterns can be detected quite reliably (F1 score of over 0.82), and future research directions may allow for improved detection of additional patterns. This work demonstrates the plausibility of developing tools to aid developers in recognizing and appropriately rectifying deceptive UI patterns.
PARASOL: Parametric Style Control for Diffusion Image Synthesis

We propose PARASOL, a multi-modal synthesis model that enables disentangled, parametric control of the visual style of the image by jointly conditioning synthesis on both content and a fine-grained visual style embedding. We train a latent diffusion model (LDM) using specific losses for each modality and adapt the classifier-free guidance for encouraging disentangled control over independent content and style modalities at inference time. We leverage auxiliary semantic and style-based search to create training triplets for supervision of the LDM, ensuring complementarity of content and style cues. PARASOL shows promise for enabling nuanced control over visual style in diffusion models for image creation and stylization, as well as generative search where text-based search results may be adapted to more closely match user intent by interpolating both content and style descriptors.
Learning Transductions and Alignments with RNN Seq2seq Models

The paper studies the capabilities of Recurrent-Neural-Network sequence to sequence (RNN seq2seq) models in learning four transduction tasks: identity, reversal, total reduplication, and quadratic copying. These transductions are traditionally well studied under finite state transducers and attributed with increasing complexity. We find that RNN seq2seq models are only able to approximate a mapping that fits the training or in-distribution data, instead of learning the underlying functions. Although attention makes learning more efficient and robust, it does not overcome the out-of-distribution generalization limitation. We establish a novel complexity hierarchy for learning the four tasks for attention-less RNN seq2seq models, which may be understood in terms of the complexity hierarchy of formal languages, instead of string transductions. RNN variants also play a role in the results. In particular, we show that Simple RNN seq2seq models cannot count the input length.
Non-equilibrium steady states as saddle points and EDP-convergence for slow-fast gradient systems

The theory of slow-fast gradient systems leads in a natural way to non-equilibrium steady states, because on the slow time scale the fast subsystem stays in steady states that are controlled by the interaction with the slow system. Using the theory of convergence of gradient systems depending on a small parameter ɛ (here the ratio between the slow and the fast time scale) in the sense of the energy-dissipation principle shows that there is a natural characterization of these non-equilibrium steady states as saddle points of a so-called B-function where the slow variables are fixed. We give applications to slow-fast reaction-diffusion systems based on the so-called cosh-type gradient structure for reactions. It is shown that two binary reactions give rise to a ternary reaction with a state-dependent reaction coefficient. Moreover, we show that a reaction-diffusion equation with a thin membrane-like layer convergences to a transmission condition, where the formerly quadratic dissipation potential for diffusion convergences to a cosh-type dissipation potential for the transmission in the membrane limit.
An extension of the approximate component mode synthesis method to the heterogeneous Helmholtz equation

In this work we propose and analyze an extension of the approximate component mode synthesis (ACMS) method to the heterogeneous Helmholtz equation. The ACMS method has originally been introduced by Hetmaniuk and Lehoucq as a multiscale method to solve elliptic partial differential equations. The ACMS method uses a domain decomposition to separate the numerical approximation by splitting the variational problem into two independent parts: local Helmholtz problems and a global interface problem. While the former are naturally local and decoupled such that they can be easily solved in parallel, the latter requires the construction of suitable local basis functions relying on local eigenmodes and suitable extensions. We carry out a full error analysis of this approach focusing on the case where the domain decomposition is kept fixed, but the number of eigenfunctions is increased. The theoretical results in this work are supported by numerical experiments verifying algebraic convergence for the method. In certain, practically relevant cases, even exponential convergence for the local Helmholtz problems can be achieved without oversampling.
Papaya: Federated Learning, but Fully Decentralized

Federated Learning systems use a centralized server to aggregate model updates. This is a bandwidth and resource-heavy constraint and exposes the system to privacy concerns. We instead implement a peer to peer learning system in which nodes train on their own data and periodically perform a weighted average of their parameters with that of their peers according to a learned trust matrix. So far, we have created a model client framework and have been using this to run experiments on the proposed system using multiple virtual nodes which in reality exist on the same computer. We used this strategy as stated in Iteration 1 of our proposal to prove the concept of peer to peer learning with shared parameters. We now hope to run more experiments and build a more deployable real world system for the same.
Relative risk estimation under multiple matching

Relative risk estimation from matched pairs has been the subject of several papers (Mantel & Haenszel, 1959; Miettinen, 1970; Gart, 1971; Ejigou & McHugh, 1977); see also the Pittsburgh thesis of A. S. Kraus. In these investigations, the disease under study is assumed to be of low incidence and the relative risk is assumed constant over the levels of the covariates under consideration. The extension from a matched pair to a multiple matching design has also been considered. Mantel & Haenszel (1959) did not employ maximum likelihood estimation but rather used subclassification on the covariates in order to obtain subsamples each of which contributed to the overall estimator of relative risk by a weighting of the estimator from each subclassification approximately according to precision. Miettinen (1970) obtained a maximum likelihood estimator of relative risk using a conditional likelihood function. An iterative solution is generally required. The method presented here uses an unconditional approach for maximum likelihood estimation of relative risk in multiple matching, generalizing results for one-to-one matching of Ejigou & McHugh (1977). In addition, the unconditional model is used to obtain a new estimator of relative risk for application to large samples. This estimator is noniterative. Although simpler computationally than the maximum likelihood estimator, this new estimator is equivalent in asymptotic efficiency to maximum likelihood estimation.
Measurement and Prediction of Dielectric Properties of Biscuit Dough at 27 Mhz

Dielectric properties of baked dough samples of various moisture contents, temperatures and densities were measured at 27 MHz with a parallel plate capacitor connected to an Impedance Analyzer (HP 4194A). As a basis for the dielectric prediction model, statistical equations were developed from measurements of the solid density and the solid dielectric properties of flour-water mixtures at various moistures between 2% and 100% moisture. A square root dielectric mixture equation was selected and combined with the regression equations to give modified prediction equations for the dielectric properties of baked dough as functions of moisture content and bulk density at room temperature. These prediction models were further developed by introducing temperature correction terms. These developed models successfully predicted the dielectric properties ofbaked dough at different moisture content, density and high temperature.
Bioregulator application enhances yield by modulating antioxidant efficiency of rainfed cluster bean [Cyamopsis tetragonoloba L. (Taub.)] in the hot arid region of India

Summary Water deficiency is one of the most severe abiotic stresses in rainfed dry lands and limits crop productivity. Exogenous applications of salicylic acid (SA) have been applied to mitigate the adverse effects of water-deficit stresses, but the relative efficacy of different derivatives of SA in enhancing water-deficit tolerance along with the underlying physio-biochemical mechanism and yield of crops is not well documented. Field experiments were conducted to ascertain the relative efficacy of exogenous application of three plant bioregulators (PBRs) [SA, thiosalicylic acid and 5-sulfosalicylic acid (SSA)], each at three concentrations (0.5, 1.0 and 1.5 mM), on the growth, physio-biochemical characteristics and yield of cluster bean under rainfed conditions. Based on a 2-year field experiment, the application of PBRs enhanced yield (from 8 to 16%). The yield enhancement with the application of PBRs was associated with elevated water content (from 9 to 17%), membrane stability (from 12 to 18%) and antioxidant enzyme activity (from 12 to 33%) and reduced lipid peroxidation (from −15 to −34%) in leaves. The effects of PBRs were conditionally type and concentration dependent. The application of SSA at a rate of 1 mM was more effective in enhancing water-deficit tolerance and improving the yield of cluster bean under water shortage conditions. This study provides empirical evidence of the potential for the application of SA and its derivatives to enhance crop yields under drought conditions. The results have direct implications for sustainable crop production for similar regions of the world facing water deficits.
A Generalized Estimating Equations Approach to Investigate Predictors of Teacher Candidates’ Views of Coding

This article reports the analysis of data from five different studies to identify predictors of preservice, early childhood teachers’ views of (a) the nature of coding, (b) integration of coding into preschool classrooms, and (c) relation of coding to fields other than computer science (CS). Significant changes in views of coding were predicted by time, prior robot programming experience, and perceptions of the value of coding. Notably, prior programming knowledge and positive perceptions of mathematics predicted decreases in views of coding from pre- to post-survey.
Dragon Hunter: Loss Aversion for Increasing Physical Activity in AR Exergames

Exergames, the combination of exercises with computer games, are a promising concept to motivate sedentary users to become physically more active. Many researchers have demonstrated that exergames can be more motivational than traditional exercises, but achieving a sufficiently high exercise intensity and long term adherence is still a problem. Loss aversion is a motivational concept which has been shown to be more effective than reward and penalty systems in many real-world applications. In this paper we explore how loss aversion can be implemented within AR exergames and whether loss aversion is more effective than a traditional reward system. A user study (n=17) shows that loss aversion results in higher intensity exercises than a reward system and that for the majority of users it motivated them more to exercise, but users also felt more pressured. Both motivational strategies were similarly enjoyable but for some users loss aversion can evoke strong negative feelings. Our results suggest that loss aversion is a promising strategy within exergames to elicit a higher exercise response. More research needs to be done to investigate effects on long term adherence.
A Comparative Study on Design and Usability of Cryptographic Libraries

Cryptographic misuse such as incorrect use of cipher, key, and other security-related parameters in software products can lead to devastating consequences. While for many developers, the lack of prior experience in applied cryptography could be the cause of crypto misuses, the complexity of a crypto library, bad API design, and the lack of proper documentation and assistant tools are the factors that lead to misuses. In this paper, we conduct a comparative study on cryptographic libraries with regard to their design and usability. We choose nine libraries written in three programming languages as the candidate for the usability study. We pay attention to the design and usability of symmetric encryption APIs with the help of a series of tasks designed to evaluate potential causes of crypto misuses. The experimental results grant us new insights as to what improvements can be made to mitigate crypto misuses and our results serve as a roadmap for library designers to avoid common pitfalls when designing a crypto library in the future.
Electrical and spectral property of cold arc plasma at atmospheric pressure1

Abstract An atmospheric pressure plasma jet is generated with a cold arc discharge in ambient air. The current-voltage characteristics and optical emission spectra of plasma discharges are investigated. The molecular nitrogen (N2), hydroxyl radical (OH), and oxygen atom (O) are observed and analyzed. Based on the best fit of the simulated spectra of N2 (C3∏u+ − B3∏g+) band and OH (A2∑+ − X2∏) band transition and the experimentally recorded spectra, the rotational temperature and the vibrational temperature of atmospheric pressure cold arc plasma jet (APCAPJ) are estimated.
First Bite/Chew: distinguish typical allergic food by two IMUs

Eating or overtaking allergic foods may cause fatal symptoms or even death for people with food allergies. Most current food intake tracking methods are camera-based, on-body sensor-based, microphone based, and self-reported. However, challenges that remain are allergic food detection, social acceptance, lightweight, easy to use, and inexpensive. Our approach leverages the first bite/chew and the corresponding hand movement as an indicator to distinguish typical types of the allergic food. Our initial feasibility study shows that our approach can distinguish six types of food at an accuracy of 89.7% over all four participants’ mixed data. Particularly, our method successfully detected and distinguished typical allergic foods such as burgers (wheat), instant noodles (wheat), peanuts, egg fried rice, and edamame, which can be expected to contribute to not only personal use but also medical usage.
ANDROIDGYNY: Reviewing clustering techniques for Android malware family classification

Thousands of malicious applications (apps) are daily created, modified with the aid of automation tools, and released on the World Wide Web. Several techniques have been applied over the years to identify whether an APK is malicious or not. The use of these techniques intends to identify unknown malware mainly by calculating the similarity of a sample with previously grouped, already known families of malicious apps. Thus, high rates of accuracy would enable several countermeasures: from further quick detection to the development of vaccines and aid for reverse engineering new variants. However, most of the literature consists of limited experiments—either short-term and offline or based exclusively on well-known malicious apps’ families. In this paper, we explore the use of malware phylogeny, a term borrowed from biology, consisting of the genealogical study of the relationship between elements and families. Also, we investigate the literature on clustering techniques applied to mobile malware classification and discuss how researchers have been setting up their experiments.
USB Proxy

Universal Serial Bus (USB) is the de facto standard for computer-peripheral interconnect, however over the course of its development little consideration has been given to security. This has led to multiple examples demonstrating how USB can be leveraged to undermine the security of computer systems across different layers of its interface. User-friendly, holistic security solutions have proven challenging to develop, especially given they must be retrofitted to existing systems as add-on elements. We have built a prototype USB Proxy device which brings together several protective functions for securing USB. The device serves as a lightweight mediator for USB connections. With minimal cost to performance, it enforces canonicalisation of data transferred between a device and host, allowing only known-good behaviour. The connection separation prevents malicious behaviour, such as bus-sniffing exploits. It also anonymises both ends of a connection, providing confidentiality against profiling from either side.