Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Detection and Prevention of Web Vulnerabilities

The Internet provides a vast range of benefits to society and empowers the users in a variety of ways to use web applications. Simply, the internet has become the most transformative and fast-growing technology ever built, but it also brings new security challenges to web services in internet applications because of the scattered and open nature of the internet. A simple vulnerability in the program code could favor/benefit an attacker to obtain unauthorized access and perform adversary actions. Hence, the security of web applications from a hacking attempt is of paramount importance. This paper focuses on a literature survey recapitulating security solutions and major vulnerabilities to promote further research by systemizing the existing methods, on a bigger horizon. The data is collected from an absolute of 86 primary studies that are taken from well-known digital libraries. Different methods comprising secure programming, static, Dynamic, Hybrid analysis, and machine learning classify the data from articles. The quantity of references or the significance of a developing strategy is kept in account while selecting articles. Overall, our survey suggests that there is no way to alleviate all the web vulnerabilities therefore more studies is desirable in the area of web information security. All methods’ complexity is addressed and some recommendations regarding when to use the application of given methods are provided. Finally, we typify the experience gained and examine future research openings in web application security.
High intensity versus low intensity family-based childhood obesity treatment programs: Effectiveness, engagement and implementation outcomes from a randomized controlled trial in medically underserved region

Background Access to evidence- and family-based childhood obesity (FBCO) treatment interventions in the US is a challenge, especially in medically underserved regions where disparities in childhood obesity persist. Methods A comparative effectiveness RCT of two 6-month FBCO treatment interventions varied in structure and contact hours—iChoose (high intensity) and Family Connections (low intensity)—in a medically underserved region. Guided by a systems-based and community-based participatory research approach and the RE-AIM planning and evaluation framework, this paper reports on effectiveness (BMI z-scores) and implementation outcomes. Changes in parent outcomes, secondary outcomes (e.g. QOL, nutrition, physical activity), engagement, and implementation costs are also reported. Analyses included descriptive statistics and intention-to-treat Heckman treatment effect models with cluster robust inference adjustment. Results Enrolled children (n = 139, mean age 10.1 ± 1.7 years, 30% overweight, 70% obese, 45% black, 63% on Medicaid) were randomly assigned to iChoose (n = 70) or Family Connections (n = 69). Retention rates were 63% for iChoose and 84% for Family Connection. Among children, 6-month BMI z-score changes were not statistically significant within iChoose [BMI z-score 0.03 (95% CI = -0.13, 0.19)] or Family Connections [BMI z-score 0.00 (95% CI = -0.16, 0.16)]. Likewise, relative between condition effects were not statistically significant and similar null effects were found in parents’ BMI changes. Yet relative to iChoose, Family Connections parents had significant improvement in quality of life (p = 0.03). Both programs were delivered with high fidelity (77–100%). iChoose engagement was 30% family classes, 36% IVR calls, and 25% physical activity classes; Family Connections engagement was 52% parent classes and 61% IVR calls. Implementations costs per children with improved BMI z-score was $2,841 for iChoose and $955 for Family Connections. Conclusions Both FBCO treatment interventions were delivered with high fidelity; yet neither yielded significant improvements in child or parent BMI. Relative to iChoose, descriptive data indicated higher retention, better engagement, and lower costs for Family Connections—suggesting that a lower intensity program may better fit the intended audience’s context. Future research efforts need to address lack of program effects and further explore strategies to leverage higher engagement in lower intensity programs for populations in medically underserved areas. Trial registration: Clincialtrials.gov: NCT03245775. Registered 10 August 2017. https://clinicaltrials.gov/ct2/show/NCT03245775? term=NCT03245775&draw=2&rank=1
Rock Phosphate-enriched Pressmud Compost: Direct Effect in Pearl Millet (Pennisetum glaucum L.) and Residual Effect in Mustard (Brassica juncea) in a Typic Haplustept

Rock phosphate-enriched pressmud compost (RPEPMC) was prepared by composting fresh pressmud and rock phosphate mixture in the ratio of 20:1 (w:w basis) in cylindrical plastic container with a composite microbial culture at 0.1% (w:w) level. A field experiment was conducted during rainy season (kharif) followed by winter (rabi) season of 2006–07 to evaluate the RPEPMC as partial substitute of fertilizer phosphorus (P) in terms of direct and residual P uptake and P use efficiency (PUE) by crops to study the changes in available P at 60 days after sowing (DAS) as well as after harvest of both the crops and various inorganic P fractions in soil after cropping. Results showed that pearl millet responded up to 26.1 kg ha−1 of P application. Application of diammonium phosphate (DAP) proved superior to RPEPMC with respect to grain and stover yield, P derived from P sources, total P uptake and PUE by pearl millet at 34.8 kg P ha−1 level. Among the various ratios of RPEPMC and DAP (3:1, 1:1 and 1:3) tried at 34.8 kg P ha−1 level, the 1:3 ratio showed best performance followed by 1:1 and 3:1 ratios. Phosphorus applied to pearl millet had considerable residual effect on succeeding mustard, the effect being more pronounced at higher levels of P application. The residual effect of P applied at 34.8 kg P ha−1 through RPEPMC and DAP produced 74.3 and 59.6% relative grain yield and 74.6 and 56.2% relative total P uptake, respectively at maturity against fresh application of 34.8 kg P ha−1 through DAP in mustard. The relative total P uptake by mustard at 34.8 kg P ha−1 through the three ratios was in the order of 3:1>1:1>1:3. Residual effect of RPEPMC and DAP in combination applied at 34.8 kg P ha−1 in a 3:1 ratio showed higher available P at 60 DAS as well as after harvest of mustard. Moreover, the application of RPEPMC in soils of medium available P status (Typic Haplustepts) in pearl millet-mustard sequence replaced about half to two-third of P requirement of crops from fertilizer with no appreciable change in soil P fertility.
Populism and the First Wave of Globalization: Evidence from the 1892 Us Presidential Election

The reasons for the famous agrarian unrest in the United States between 1870 and 1900 remain debated. We argue that they are, at least in part, consistent with a simple economic explanation. Falling transportation costs allowed for the extension of the frontier, where farmers received the world price minus the transaction costs involved in getting their produce to market. Many perceived these costs to be unfairly large, owing to the perceived market power of rail firms and the discriminatory practices of middlemen, with farmers closer to the frontier most affected. Consistent with this, we find that the protest, as measured by vote shares for the Populists in the 1892 Presidential elections, is negatively related to wheat prices, transportation costs, and rail network density.
Remote Controlled Mapping

: Three-dimensional mapping is the process of gathering location information so that the locations and possibly the attributes of the object can be presented in a form that has 3 dimensions (XYZ) that can interpret by a user. It is a technology used to turn objects, often irregularly shaped, into a display surface. These objects may be complex industrial landscapes, such as buildings, small indoor objects or theatrical stages. It helps obtain a precise picture of terrain – including all of the buildings and natural components of the terrain giving it a sense of size and depth combined with other information related to the environment mapped. Here, we intend to propose a secure remote-controlled three-dimensional mapping robot using an ultrasonic transducer. The robot is controlled wirelessly using Bluetooth to the desired area that needs to be mapped. The ultrasonic sensor then measures the physical parameters of the desired location, which is used to make a 3D map of the area by feeding them into specialized software. The main motivation behind this work is to address the problem of remote terrain mapping of places that are inaccessible to humans. The proposed solution can be used to investigate pipelines and explore riverbeds. It can also be used by the country's security forces to detect intruders and for probing for signs of life in case of natural disasters. It helps understand the environment under study and this information can be further stored and analyzed at times of need.
Suppression of Mexican Bean Beetle on Soybeans with Annual Inoculative Releases of Pediobius foveolatus

Pediobius foveolatus (Crawford) was released annually in soybeans during 1972 and 1973 in preliminary attempts to suppress Epilachna varivestis Mulsant. The results led to an area-wide suppression during 1974, utilizing nurse crops of snap beans for early inoculation of E. varivestis larval populations, and widespread releases of P. foveolatus directly into soybean fields. The results indicate a promising means of biological control for areas where E. varivestis is economically important.
Long-Term Map Maintenance Pipeline for Autonomous Vehicles

For autonomous vehicles to operate persistently in a typical urban environment, it is essential to have high accuracy position information. This requires a mapping and localisation system that can adapt to changes over time. A localisation approach based on a single-survey map will not be suitable for long-term operation as it does not incorporate variations in the environment. In this paper, we present new algorithms to maintain a featured-based map. A map maintenance pipeline is proposed that can continuously update a map with the most relevant features taking advantage of the changes in the surroundings. Our pipeline detects and removes transient features based on their geometrical relationships with the vehicle’s pose. Newly identified features became part of a new feature map and are assessed by the pipeline as candidates for the localisation map. By purging out-of-date features and adding newly detected features, we continually update the prior map to more accurately represent the most recent environment. We have validated our approach using the USyd Campus Dataset, which includes more than 18 months of data. The results presented demonstrate that our maintenance pipeline produces a resilient map which can provide sustained localisation performance over time.
Gravitational waves from dark sectors, oscillating inflatons, and mass boosted dark matter

Gravitational wave signatures from dynamical scalar field configurations provide a compelling observational window on the early universe. Here we identify intriguing connections between dark matter and scalars fields that emit gravitational waves, either through a first order phase transition or oscillating after inflation. To study gravitational waves from first order phase transitions, we investigate a simplified model consisting of a heavy scalar coupled to a vector and fermion field. We then compute gravitational wave spectra sourced by inflaton field configurations oscillating after E-Model and T-Model inflation. Some of these gravitational wave signatures can be uncovered by the future Big Bang Observatory, although in general we find that MHz-GHz frequency gravitational wave sensitivity will be critical for discovering the heaviest dark sectors. Intriguingly, we find that scalars undergoing phase transitions, along with E-Model and T-Model potentials, can impel a late-time dark matter mass boost and generate up to Planck mass dark matter. For phase transitions and oscillating inflatons, the largest dark matter mass boosts correspond to higher amplitude stochastic gravitational wave backgrounds.
DNNExplorer: A Framework for Modeling and Exploring a Novel Paradigm of FPGA-based DNN Accelerator

Existing FPGA-based DNN accelerators typically fall into two design paradigms. Either they adopt a generic reusable architecture to support different DNN networks but leave some performance and efficiency on the table because of the sacrifice of design specificity. Or they apply a layer-wise tailor-made architecture to optimize layer-specific demands for computation and resources but loose the scalability of adaptation to a wide range of DNN networks. To overcome these drawbacks, this paper proposes a novel FPGA-based DNN accelerator design paradigm and its automation tool, called DNNExplorer, to enable fast exploration of various accelerator designs under the proposed paradigm and deliver optimized accelerator architectures for existing and emerging DNN networks. Three key techniques are essential for DNNExplorer's improved performance, better specificity, and scalability, including (1) a unique accelerator design paradigm with both high-dimensional design space support and fine-grained adjustability, (2) a dynamic design space to accommodate different combinations of DNN workloads and targeted FPGAs, and (3) a design space exploration (DSE) engine to generate optimized accelerator architectures following the proposed paradigm by simultaneously considering both FPGAs' computation and memory resources and DNN networks' layer-wise characteristics and overall complexity. Experimental results show that, for the same FPGAs, accelerators generated by DNNExplorer can deliver up to 4.2x higher performances (GOP/s) than the state-of-the-art layer-wise pipelined solutions generated by DNNBuilder [1] for VGG-like DNN with 38 CONV layers. Compared to accelerators with generic reusable computation units, DNNExplorer achieves up to 2.0x and 4.4x DSP efficiency improvement than a recently published accelerator design from academia (HybridDNN [2]) and a commercial DNN accelerator IP (Xilinx DPU [3]), respectively.
Smoothly Navigating between Functional Reactive Programming and Actors

We formally define an elegant multi-paradigm unification of Functional Reactive Programming, Actor Systems, and Object-Oriented Programming. This enables an intuitive form of declarative programming, harvesting the power of concurrency while maintaining safety. We use object and reference capabilities to highlight and tame imperative features: reference capabilities track aliasing and mutability, and object capabilities track I/O. Formally, our type system limits the scope, impact and interactions of impure code. - Scope: Expressions whose input is pure will behave deterministically. - Impact: Data-races and synchronisation issues are avoided. The only way for an actor to behave nondeterministically, is by mutating its state based on message delivery order. - Interactions: Signals provide a functional boundary between imperative and functional code, preventing impure code from invalidating functional assumptions.
More light on Higgs flavor at the LHC: Higgs boson couplings to light quarks through 
h+γ
 production

Higgs production in association with a photon at hadron colliders is a rare process, not yet observed at the LHC. We show that this process is sensitive to significant deviations of Higgs couplings to first and second generation SM quarks (particularly the up-type) from their SM values, and use a multivariate neural network analysis to derive the prospects of the High Luminosity LHC to probe deviations in the up and charm Higgs Yukawa couplings through $h + \gamma$ production.
Evaluating moments of length of Pitman partition

Introduced in a seminal paper by Jim Pitman in 1995, the Pitman sampling formula has been intensively studied as a distribution of random partitions. One of the objects of interest is the length $K (= K_{n,\theta,\alpha})$ of a random partition that follows the Pitman sampling formula, where $n\in\mathbb{N}$, $\alpha\in(0,\infty)$ and $\theta > -\alpha$ are parameters. This paper presents asymptotic evaluations of $\mathsf{E}[K^r]$ ($r=1,2,\ldots$) under two asymptotic regimes. In particular, the goals of this study are to provide a finer approximate evaluation of $\mathsf{E}[K^r]$ as $n\to\infty$ than has previously been developed and to provide an approximate evaluation of $\mathsf{E}[K^r]$ as the parameters $n$ and $\theta$ simultaneously tend to infinity with $\theta/n \to 0$. The results presented in this paper will provide a more accurate understanding of the asymptotic behavior of $K$.
Pixel-Face: A Large-Scale, High-Resolution Benchmark for 3D Face Reconstruction

3D face reconstruction is a fundamental task that can facilitate numerous applications such as robust facial analysis and augmented reality. It is also a challenging task due to the lack of high-quality datasets that can fuel current deep learning-based methods. However, existing datasets are limited in quantity, realisticity and diversity. To circumvent these hurdles, we introduce Pixel-Face, a large-scale, high-resolution and diverse 3D face dataset with massive annotations. Specifically, Pixel-Face contains 855 subjects aging from 18 to 80. Each subject has more than 20 samples with various expressions. Each sample is composed of high-resolution multi-view RGB images and 3D meshes with various expressions. Moreover, we collect precise landmarks annotation and 3D registration result for each data. To demonstrate the advantages of Pixel-Face, we re-parameterize the 3D Morphable Model (3DMM) into Pixel-3DM using the collected data. We show that the obtained Pixel-3DM is better in modeling a wide range of face shapes and expressions. We also carefully benchmark existing 3D face reconstruction methods on our dataset. Moreover, Pixel-Face serves as an effective training source. We observe that the performance of current face reconstruction models significantly improves both on existing benchmarks and Pixel-Face after being fine-tuned using our newly collected data. Extensive experiments demonstrate the effectiveness of Pixel-3DM and the usefulness of Pixel-Face.
AllenAct: A Framework for Embodied AI Research

The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities. This growth has been facilitated by the creation of a large number of simulated environments (such as AI2-THOR, Habitat and CARLA), tasks (like point navigation, instruction following, and embodied question answering), and associated leaderboards. While this diversity has been beneficial and organic, it has also fragmented the community: a huge amount of effort is required to do something as simple as taking a model trained in one environment and testing it in another. This discourages good science. We introduce AllenAct, a modular and flexible learning framework designed with a focus on the unique requirements of Embodied AI research. AllenAct provides first-class support for a growing collection of embodied environments, tasks and algorithms, provides reproductions of state-of-the-art models and includes extensive documentation, tutorials, start-up code, and pre-trained models. We hope that our framework makes Embodied AI more accessible and encourages new researchers to join this exciting area. The framework can be accessed at: this https URL
Impulsive wave excitation by rapidly changing granules

It is not yet fully understood how magnetohydrodynamic waves in the interior and atmosphere of the Sun are excited. Traditionally, turbulent convection in the interior is considered to be the source of wave excitation in the quiet Sun. Over the last few decades, acoustic events observed in the intergranular lanes in the photosphere have emerged as a strong candidate for a wave excitation source. Here we report our observations of wave excitation by a new type of event: rapidly changing granules. Our observations were carried out with the Fast Imaging Solar Spectrograph in the Hα and Ca II 8542 Å lines and the TiO 7057 Å broadband filter imager of the 1.6 m Goode Solar Telescope at the Big Bear Solar Observatory. We identify granules in the internetwork region that undergo rapid dynamic changes such as collapse (event 1), fragmentation (event 2), or submergence (event 3). In the photospheric images, these granules become significantly darker than neighboring granules. Following the granules’ rapid changes, transient oscillations are detected in the photospheric and chromospheric layers. In the case of event 1, the dominant period of the oscillations is close to 4.2 min in the photosphere and 3.8 min in the chromosphere. Moreover, in the Ca II–0.5 Å raster image, we observe repetitive brightenings in the location of the rapidly changing granules that are considered the manifestation of shock waves. Based on our results, we suggest that dynamic changes of granules can generate upward-propagating acoustic waves in the quiet Sun that ultimately develop into shocks.
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection

While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can defend WTNs against discharging contaminated water, denying access, destroying equipment, and causing public fear. While there are extensive studies in WTNs attack detection, they only exploit the data characteristics partially to detect cyber attacks. After preliminary exploring the sensing data of WTNs, we find that integrating spatio-temporal knowledge, representation learning, and detection algorithms can improve attack detection accuracy. To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatiotemporal characteristics of cyber attacks in WTNs. In particular, we propose a spatio-temporal representation framework specially tailored to cyber attacks after separating the sensing data of WTNs into a sequence of time segments. This framework has two key components. The first component is a temporal embedding module to preserve temporal patterns within a time segment by projecting the time segment of a sensor into a temporal embedding vector. We then construct Spatio-Temporal Graphs (STGs), where a node is a sensor and an attribute is the temporal embedding vector of the sensor, to describe the state of the WTNs. The second component is a spatial embedding module, which learns the final fused embedding of the WTNs from STGs. In addition, we devise an improved one class-SVM model that utilizes a new designed pairwise kernel to detect cyber attacks. The devised pairwise kernel augments the distance between normal and attack patterns in the fused embedding space. Finally, we conducted extensive experimental evaluations with real-world data to demonstrate the effectiveness of our framework: it achieves an accuracy of 91.65%, with average improvement ratios of 82.78% and 22.96% with respect to F1 and AUC, compared with baseline methods.