Miracle-dz/UniFAR-FTUs huggingface dataset:
THERAPEUTIC POTENTIAL OF SEAWEED BIOACTIVE COMPOUNDS AGAINST Helicobacter pylori

The prevalence of H. pylori is closely tied to socioeconomic conditions and accordingly, this infection is more common in developing countries than in developed countries such as the United States. Regardless, it has been estimated that 30–40% of the U.S. population is infected with H. pylori. Antimicrobial resistance is largely responsible for treatment failures. Resistance to metronidazole can frequently be overcome by increasing the dose and duration of treatment with acid suppression. Seaweeds or marine macroalgae are the renewable living resources which are also used as food, feed and fertilizer in many parts of the world. Seaweeds are of nutritional interest as they contain low calorie food, but rich in vitamins, minerals and dietary fibres. In these present investigation metabolites from seaweeds was evaluated for their antihelicobacterial activity. Two Helicobacter isolates were found in the samples and have been characterized based on selective biochemical and morphological characteristics. The isolates were screened for their antibiotics resistance / sensitivity pattern against twelve commercially availed antibiotics. Among the antibiotics ten of them exhibit sensitivity to the Helicobacter pylori isolates. But the isolates showed resistance to Nystatin, omeprazole, metronidazole, ranitidine, trimethoprim and methicillin. There are eleven marine seaweeds were collected from INFOKARA RESEARCH Volume 8 Issue 12 2019 1297 ISSN NO: 1021-9056 http://infokara.com/ Thondy coastal region ant they were screened for antihelicobacterial activity with crude metabolites. Among the seaweeds screened against both Helicobacter pylori isolates the seaweed Padina tetrastomatica (13, 16mm), Halmiola sp (9, 12mm) and Gracilaria edulis (6, 20mm) exhibit better antihelicobacterial activity in in vitro assay. The mass spectral data of LCMS report revealed that the peaks indicated, may be the presence of phenolic compounds and sulphated polysaccharides which were compared based on earlier reports on seaweeds.
Supplementary Material of “Controlling the Rain: from Removal to Rendering”

For better understanding our multi-level interpolation, we visualize the interpolated features in PGM, as marked with the green boxes in Fig. 1. They are the interpolation effects of features in two branches of PGM (corresponding to rain intensities of 100mm/hr and 200mm/hr). Then the interpolated features of PGM in different levels are compared. The low-level features are shown in the Fig. 1(a). It can be seen that, the interpolation in low level tends to generate detailed information of intermediate intensity, such as the rain streaks in small scales. While in high-level interpolated features, structure characteristics of intermediate intensity are rendered, such as big rain streaks and the overall background brightness, as shown in Fig. 1(b). Therefore, the interpolation on multiple levels can better extract different features ranging from low level to high level, which promotes the generation of different scales of rain streaks.
Infrared image clarifying and FPGA implementation based on dark channel prior

: In order to solve the problem of low contrast between the target and the background and blurred details in infrared images, an improved infrared image clearing algorithm based on dark channel prior theory was proposed and FPGA was used to design the hardware system of the proposed algorithm. The dark channel image was obtained based on nonlinear filtering of the current pixel and the neighborhood data of the input image. Moreover, the correction function was used to optimize the transmission to generate a look_up table. Then the transmission was looked up in the look_up table and the proposed algorithm enhanced the image with the atmospheric scattering model, thereby reducing or eliminating the block effects and the color distortion of the sky or other bright areas generated by the traditional dark channel algorithm. The design of FPGA hardware could work with an estimated frequency of 188 MHz by occupying only 4% of LUT and 8% of I/O resources, which was much higher than the operating frequency of 27 MHz of the camera used. Therefore, the design was realized to meet the requirements of real-time application of video images.
Antimicrobial Peptides in the Battle Against Orthopedic Implant-Related Infections – A Review

Prevention of orthopedic implant-related infections is a major medical challenge, particularly due to the involvement of biofilm-encased and multidrug-resistant bacteria. Current therapies, based on antibiotic administration, have proven to be insufficient, and infection prevalence may rise due to the dissemination of antibiotic resistance.Antimicrobial peptides (AMPs) have attracted attention as promising substitutes of conventional antibiotics, owing to their broad-spectrum of activity, high efficacy at very low concentrations, and importantly low propensity for inducing resistance. The current review focuses on the potential of using AMPs as a preventive strategy for orthopedic implant-related infections. This review addresses current progress in AMP-based approaches designed to both benefit from the key role envisioned for AMPs in the post-antibiotic era and overcome the limitations of peptides as therapeutics. Particular attention is given to application of AMPs onto titanium orthopedic and dental implants, and on encouraging achievements that pave the way for future research in this field.
Temptation and Stochastic Preferences

This paper is concerned with the nature of temptation in stochastic choice models. In particular, we study the distinction between the two leading approaches to modeling temptation in the literature (dynamic inconsistency and costly self-control) when preferences are stochastic. We first design an experimental methodology to distinguish random Strotz preferences from random Gul-Pesendorfer (GP) preferences. We then implement this method in a field experiment that uses data on choices of menus combined with data on choices from menus of food items in a large pool of subjects during a three month period. The results show the presence of large proportions of both random Strotz types and random GP types.
On Sticky Bookmaking as a Learning Device in Horse-Racing Betting Markets

We present a stochastic dynamic model of the adjustment of betting odds by bookmakers in a horse-racing betting market. We use optimal stopping theory in a two-horse benchmark model with both informed and noise punters. A costly learning process discloses what information the informed traders possess and a risk-neutral bookmaker selects a stopping time at which the betting odds for each horse in a race are adjusted. Our main finding shows that an increased fraction of informed punters has a non-monotonic effect on the loss per trade to the bookmaker. We also find that as the fraction of noise traders goes up, the learning process is less informative, so that the decision to change the prices for each horse is taken sooner.
Efficacy of Ricinus communis L., Cassia occidentalis L. and Bacillus thuringiensis against Helicoverpa armigera Hübner (Lepidoptera: Noctuidae)

Helicoverpaarmigera is one of the most destructive pests of field crops worldwide. The Study was designed to investigate through laboratory screening the insecticidal activity of Ricinus communis, Cassia occidentalis and Bacillus thuringiensis var. Kurstaki (Btk) and their combinations against 2nd larval instar of H. armigera. Five concentrations (4%, 6%, 8%, 10% and 12%) of each plant extract and (0.62, 1.25, 2.5, 5, and 10 mg/ml) of Bt were used in this experiment. Mortality (%) was recorded after 24,48,72 and 96 hrs post treatment. The results showed that R. communis, C. occidentalis and Btkhave insecticidal activity against 2nd instar larvae of H. armigera. The highest concentration (12%) caused 80% and 70% larval mortality after 96 hrs for R. communis and C. occidental respectively. The statistical analysis revealed that there is a significant differences between all treatments and control. Additionally, all combinations (plant/plant or plant/Bt) increased the mortality %. The results also clearly demonstrate that the R. communis are significantly more toxic than C. occidental where the LC50 values were 6.4 % for R. communis and 8.1% for C. occidental. On the other hand LC50 value for Bt was 0.41 mg/ml. The binary mixture of R. communis and C. occidental have a potentiation effect after 48 hrs. Regarding the mixture of Bt and plant extracts the results revealed that R. communis and Bt mixture induced a potentiation effect whereas C. occidental and Bt mixture induced a an additive effects.
Extensive Analysis on Images Encryption using Hybrid Elliptic Curve Cryptosystem and Hill Cipher

Corresponding Author: Zurina Mohd Hanapi Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti of Putra Malaysia, Serdang, Selangor, Malaysia Email: zurinamh@upm.edu. my Abstract: The advancement of communication technology helps individual to share images through an Internet. However, the sharing through insecure channels may expose the images to certain attacks that will compromise their confidentiality. Image encryption is one of the methods used to protect against the confidentiality threat. A Hill Cipher has been applied in image encryption because of its simple operation and fast computation, but it also possesses a weak security level which requires the sender and receiver to use and share the same private key within an unsecure channel. Thus, there are many solutions been proposed in utilizing hybrid approach of Hill Cipher where one of them is Elliptic Curve Cryptosystem together with Hill Cipher (ECCHC) to utilize the beauty of Hill Cipher while managing its weaknesses. However, the ECCHC only been experimented over four images which leads to inaccuracy of the results. Thus, this study extended the experiments on 209 images from USC-SIPI database in order to investigate the efficiency of ECCHC. The result shows the ECCHC produces poor performances on security analysis on grayscale and RGB images, which then concludes it is not suitable to encrypt grayscale and RGB images.
Distribution of Mercury in the Sediments and Fishes of the Lagoon of Aveiro, Portugal

Dry and wet sieving were compared on their suitability for the size fractionation of bottom sediment samples, collected from a brackish water, coastal lagoon, prior to flameless atomic absorption analysis of the sediment concentration of total mercury. The work aimed at identifying the most important point sources of mercury to the lagoon waters and at assessing the distribution, throughout the ecosystem, of the mercury discharged. The results reported in this paper refer to the lagoon sediments and fishes. Ongoing work is extending the survey to the other inland ecological niches and to the coastal waters and sediments. A chlor-alkali plant proved to be the most important source of mercury but its contribution has not yet been disentangled from those originating at a pyrite roasting facility and a PVC factory operating in the same industrial park. Sewage discharges from the bankside communities are the other significant point sources of Hg. Concentrations of Hg in the superficial sediments ranged from 850 mgkg−1, near the chlor-alkali sewer outlet, to 0.05 mgkg−1 in the intertidal sands of the coastal beaches. Sewage-affected sediments have concentrations of Hg in the range of 1 to 2 mgkg−1. The concentrations observed in the water samples and superficial sediments suggest that some of the Hg historically discharged into the lagoon may have been exported to the Atlantic Ocean coastal waters. Bioaccumulation of Hg in fish tissues shows the usual dependence on species, territoriality, feeding habits and size. Concentrations as high as 25 mgkg−1 fresh weight have been observed but the average values do not exceed the EEC quality objective of 0.3 mgkg−1 (EEC, 1982).
Mozzarella Whey Indigenous Yeasts and their Potential in Amino acid and Peptide Production through Fermentation

One of the methodologies that can be applied in whey utilization is fermentation. The products or metabolites that can be produced from whey fermentation include amino acids and peptides. Amino acids and peptides are metabolites that have broad benefits both for the food industry and for human health. Amino acids and peptides can be produced through the breakdown of proteins by protease and peptidase enzymes secreted by microorganisms. The use of microorganism’s types in fermentation will greatly affect the profile of amino acids and peptides produced, because each microorganism has a different proteolytic system. Indigenous yeast is one of the microorganisms that naturally live on whey and has the potential to be reapplied in whey fermentation to produce amino acids and peptides. Indigenous yeast has several advantages over other microorganisms, such as better adaptability to fermentation conditions and the ability to grow and dominate the substrate more easily and quickly. Some of the yeasts with proteolytic activity that have been isolated from whey are Candida lambica, Candida parapsilosis, Candida rugosa, Debaromyces hansenii, Kluyveromyces lactis, Kodamaea ohmeri, Torulaspora delbrueckii, Zygosaccharomyces rouxii, Candida ethanolica, Candidium pseudolasci, Pichia farinosa, Candida mogii, Candida intermedia, Saccharomyces cerevisiae, Kluyveromyces marxianus, Clavispora lusitaniae, and Galactomyces geotrichum. Candida tropicalis, Trichosporon beigelii, and Blastoshizomyces capitatus were reported as three indigenous yeasts isoalted from mozzarella whey. The yeasts are able to release the aspartate protease enzyme and produce peptides and amino acids that show various benefits including health benefits.
TWAG: A Topic-Guided Wikipedia Abstract Generator

Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts.
undeSErVed trust: Exploiting Permutation-Agnostic Remote Attestation

The ongoing trend of moving data and computation to the cloud is met with concerns regarding privacy and protection of intellectual property. Cloud Service Providers (CSP) must be fully trusted to not tamper with or disclose processed data, hampering adoption of cloud services for many sensitive or critical applications. As a result, CSPs and CPU manufacturers are rushing to find solutions for secure and trustworthy outsourced computation in the Cloud. While enclaves, like Intel SGX, are strongly limited in terms of throughput and size, AMD’s Secure Encrypted Virtualization (SEV) offers hardware support for transparently protecting code and data of entire VMs, thus removing the performance, memory and software adaption barriers of enclaves. Through attestation of boot code integrity and means for securely transferring secrets into an encrypted VM, CSPs are effectively removed from the list of trusted entities. There have been several attacks on the security of SEV, by abusing I/O channels to encrypt and decrypt data, or by moving encrypted code blocks at runtime. Yet, none of these attacks have targeted the attestation protocol, the core of the secure computing environment created by SEV. We show that the current attestation mechanism of Zen 1 and Zen 2 architectures has a significant flaw, allowing us to manipulate the loaded code without affecting the attestation outcome. An attacker may abuse this weakness to inject arbitrary code at startup–and thus take control over the entire VM execution, without any indication to the VM’s owner. Our attack primitives allow the attacker to do extensive modifications to the bootloader and the operating system, like injecting spy code or extracting secret data. We present a full end-to-end attack, from the initial exploit to leaking the key of the encrypted disk image during boot, giving the attacker unthrottled access to all of the VM’s persistent data.
Adaptive Sample Selection for Robust Learning under Label Noise

Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A prominent class of algorithms rely on sample selection strategies wherein, essentially, a fraction of samples with loss values below a certain threshold are selected for training. These algorithms are sensitive to such thresholds, and it is difficult to fix or learn these thresholds. Often, these algorithms also require information such as label noise rates which are typically unavailable in practice. In this paper, we propose an adaptive sample selection strategy that relies only on batch statistics of a given mini-batch to provide robustness against label noise. The algorithm does not have any additional hyperparameters for sample selection, does not need any information on noise rates and does not need access to separate data with clean labels. We empirically demonstrate the effectiveness of our algorithm on benchmark datasets.1
Boggart: Accelerating Retrospective Video Analytics via Model-Agnostic Ingest Processing

Delivering fast responses to retrospective queries on video datasets is difﬁcult due to the large number of frames to consider and the high costs of running convolutional neural networks (CNNs) on each one. A natural solution is to perform a subset of the necessary computations ahead of time, as video is ingested . However, existing ingest-time systems require knowledge of the speciﬁc CNN that will be used in future queries – a challenging requisite given the evergrowing space of CNN architectures and training datasets/methodologies. This paper presents Boggart, a retrospective video analytics system that delivers ingest-time speedups in a model-agnostic manner. Our underlying insight is that traditional computer vision (CV) algorithms are capable of performing computations that can be used to accelerate diverse queries with wide-ranging CNNs. Building on this, at ingest-time, Boggart carefully employs a variety of motion tracking algorithms to identify potential objects and their trajectories across frames. Then, at query-time, Boggart uses several novel techniques to collect the smallest sample of CNN re-sults required to meet the target accuracy: (1) a clustering strategy to efﬁciently unearth the inevitable discrepancies between CV-and CNN-generated outputs, and (2) a set of accuracy-preserving propagation techniques to safely extend sampled results along each trajectory. Across many videos, CNNs, and queries Boggart consistently meets accuracy targets while using CNNs sparingly (on 3-54% of frames).
Online Interaction Detection for Click-Through Rate Prediction

Click-Through Rate prediction aims to predict the ratio of clicks to impressions of a specific link. This is a challenging task since (1) there are usually categorical features, and the inputs will be extremely high-dimensional if one-hot encoding is applied, (2) not only the original features but also their interactions are important, (3) an effective prediction may rely on different features and interactions in different time periods. To overcome these difficulties, we propose a new interaction detection method, named Online Random Intersection Chains. The method, which is based on the idea of frequent itemset mining, detects informative interactions by observing the intersections of randomly chosen samples. The discovered interactions enjoy high interpretability as they can be comprehended as logical expressions. ORIC can be updated every time new data is collected, without being retrained on historical data. What's more, the importance of the historical and latest data can be controlled by a tuning parameter. A framework is designed to deal with the streaming interactions, so almost all existing models for CTR prediction can be applied after interaction detection. Empirical results demonstrate the efficiency and effectiveness of ORIC on three benchmark datasets.
Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered to be a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large transmission delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.
Multitask Learning for Scalable and Dense Multilayer Bayesian Map Inference

In this article, we present a novel and flexible multitask multilayer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for robots in a single mapping formalism while exploiting intralayer and interlayer correlations. It removes the need for a robot to access and process information from many separate maps when performing a complex task, advancing the way robots interact with their environments. To this end, we design a multitask deep neural network with attention mechanisms as our front-end to provide heterogeneous observations for multiple map layers simultaneously. Our back-end runs a scalable closed-form Bayesian inference with only logarithmic time complexity. We apply the framework to build a dense robotic map, including metric-semantic occupancy and traversability layers. Traversability ground truth labels are automatically generated from exteroceptive sensory data in a self-supervised manner. We present extensive experimental results on publicly available datasets and data collected by a three-dimensional bipedal robot platform and show reliable mapping performance in different environments. Finally, we also discuss how the current framework can be extended to incorporate more information, such as friction, signal strength, temperature, and physical quantity concentration using Gaussian map layers. The software for reproducing the presented results or running on customized data is made publicly available.