Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Source Code Summarization via Reinforcement Learning

Due to the fast development of computer and software, the volume of today’s code has reached an unprecedented level. For large-scale systems (e.g., cloud computing systems) with billions lines of codes, the majority of its maintenance effort is code management. And much of this effort is spent on understanding related source codes. With high-quality code summaries, one can quickly understand what a function does (even without reading the code). However, it’s nontrivial for a programmer to write good comments for source codes. If the code summary can be automatically generated, then we can greatly accelerate the whole pipeline of software development. To this end, in this project we develop a reinforcement learning framework to enhance the automatic generation of code summarization. By properly deﬁning the key modules, we employ an actor-critic network to solve this problem. Experimental results demonstrate that our model outperforms vallina sequence to sequence model by a noticeable margin. Introduction video of this project is available here 1 .
Experiment Study on Denitrification and Dephosphorization by Combination Process of MAP and Zeolite Desorption

By taking nitrogen and phosphorus with high concentration to simulate waste water as treatment object and by using static state experiment,the effect of denitrification and dephosphorization caused by MAP process and combination process of zeolite desorption is studied.By using outlet water of denitrification and dephosphorization by MAP process as inlet water of zeolite desorption course,the removal rate for nitrogen and phosphorus meets 86.69% and 99.9% respectively.Higher pH and Mg2+ concentration are in favor of desorption and removal of nitrogen and phosphorus at the late stage of zeolite in the reaction course of MAP.
A systematic p53 mutation library links functional impact to evolutionary conservation and cancer mutation pattern

The TP53 tumor suppressor gene is very frequently mutated in human cancer. Research on p53 mutations has focused predominantly on six major “hotspot” codons which comprise only ~30% of cancer-associated p53 mutations. To systematically study the impact of a wide diversity of p53 mutations, we created a synthetically designed mutation library and measured the functional impact of ~10,000 DNA-binding domain (DBD) p53 variants, including nearly all patient-reported mutations, in human cells. Our results elucidate the selective pressure to maintain specific residues at particular positions in p53 throughout evolution. Furthermore, they highlight the differential outcome of distinct classes of p53 mutations in human patients, and suggest that loss of anti-proliferative functionality is a key selective force shaping the landscape of cancer-associated p53 mutations. Finally, we show that when combined with additional acquired p53 mutations, seemingly neutral SNPs within the DBD may modulate phenotypic outcome and presumably tumor progression.
BUNGEE: An Adaptive Pushback Mechanism for DDoS Detection and Mitigation in P4 Data Planes

A DDoS attack aims for resource exhaustion and directly impacts the availability of servers in a network infrastructure. Although significant efforts have been made to detect and mitigate DDoS attacks in viable time, this type of attack remains one of the leading security concerns in networking. By leveraging data plane programmability, it becomes possible to implement novel security solutions that do not rely on coordination with external servers, keeping the detection and mitigation local to the data plane, potentially reducing delays and not being subject to usual communication bottlenecks. In this paper we present BUNGEE1, an in-network, collaborative pushback mechanism for DDoS attack mitigation that runs entirely in the data plane. This mechanism is able to, locally at a given switch, identify suspect IP addresses (through the use of continuous IP entropy analysis) and propagate them to other switches. The different switches that are made aware of the suspects enforce a pushback strategy for repelling potential attacks. We implemented our solution using the P4 language. The results reveal that the identification process has high accuracy and that the pushback strategy is effective in minimizing strain to network resources.
Secure-In-Between-Accesses Database Security

We introduce a deﬁnition of security that applies to databases that maintain dynamic information on users such as ﬁnancial account information, medical records, etc. Such a database system is secure in between accesses provided 1) users can eﬃciently access their data, and 2) while a user is not accessing their data, the user’s information is information theoretically secure to malicious agents. We propose a realization of such a database system and prove that a user’s stored information, in between times when it is being legitimately accessed, is information theoretically secure both to adversaries who interact with the database in the prescribed manner, as well as to adversaries who have installed a virus that controls the entire internet-facing server that stores the database. We stress that the security guarantee is information theoretic and everlasting: it relies neither on unproved hardness assumptions, nor on the assumption that the adversary is computationally or storage bounded. The central idea behind our design of an information theoretically secure database system is the construction of a “re-randomizing database” that periodically changes the internal representation of the information that is being stored. To ensure security, these remappings of the representation of the data must be made suﬃciently often in comparison to the amount of information that is being communicated from the database between remappings and the amount of local memory on the database server that a virus may preserve during the remappings. While this changing representation provably foils the ability of an adversary to glean information, it can be accomplished in a manner transparent to the legitimate users, preserving how database users access their data. The
Optimization of Parameters for Fluoride Removal by Electrocoagulation using Aluminum Electrodes in Monopolar Parallel Combination

One of the major challenges faced by mankind today is to provide clean drinking water to a vast majority of population. Fluoride in drinking water above permissible limit is responsible for causing fluorosis, a disease affecting the multiple tissues, organs and systems in the body. The present study was conducted to assess the ability of electrocoagulation process for removal of fluoride from synthetic fluoride sample in a batch reactor using four aluminum electrodes in monopolar parallel connection. Operating parameters such as initial fluoride concentration (4-10 mg/L), pH (5-9), voltage (5-25V), and electrolysis time (up to 60 minutes) was studied to evaluate their effect on fluoride removal efficiency. Results obtained revealed that effective fluoride removal within permissible limit (1.2 mg/L, WHO) was obtained at pH 6,15V, within electrolysis time of 60 minutes. Keywords—fluorosis, electrocoagulation, fluoride removal, aluminum electrode, eelectrolysis time
BIROn - Birkbeck Institutional Research Online

Saliency propagation has been widely adopted for identifying the most attractive object in an image. The propagation sequence generated by existing saliency detection methods is governed by the spatial relationships of image regions, i.e. , the saliency value is transmitted between two adjacent regions. However, for the inhomogeneous difﬁcult adjacent regions, such a sequence may incur wrong propagations. In this paper, we attempt to manipulate the propagation sequence for optimizing the propagation quality. Intuitively, we postpone the propagations to difﬁcult regions and meanwhile advance the propagations to less ambiguous simple regions. Inspired by the theoretical results in educational psychology, a novel propagation algorithm employing the teaching-to-learn and learning-to-teach s-trategies is proposed to explicitly improve the propagation quality. In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difﬁcult and then assign the simplest regions to the learner. In the learning-to-teach step, the learner delivers its learning conﬁdence to the teacher to assist the teacher to choose the subsequent simple regions. Due to the interactions between the teacher and learner, the uncertainty of original difﬁcult regions is gradually reduced, yielding manifest salient objects with optimized background suppression. Extensive experimental results on benchmark saliency datasets demonstrate the superiority of the proposed algorithm over twelve representative saliency detectors.
Risk Perception in the Internationalization of Brazilian Companies: An Analysis in Different Entry Modes

The existence of risks is a premise in business-related matters, mainly in the exploration of international opportunities. Organizations seeking to operate abroad are potentially more susceptible to the risks that exist internationally. This study follows the behavioral approaches to internationalization and conceptual ideas about risk and risk perception in international business. The main goal is to understand how the risk perception of managers in Brazilian companies with different entry modes is configured. In this qualitative research, a multiple case study was carried out. The data collection techniques established were the analysis of documents and the performance of semi-structured interviews with managers responsible for the internationalization processes of six Brazilian companies. Regarding the risks inherent to these processes, there was a greater emphasis on country/political risk factors, followed by monetary risk. The risks perceived with greater evidence, considering the entry modes addressed, are also presented for a better understanding of the results.
Monocular Pose Estimation for VR Motion Capture Applications

Virtual reality is a rapidly advancing field. Making virtual reality technology accessible to everyone would be beneficial, as there are many applications. In some sectors, for example, VR can be used to train employees who work in dangerous environments. VR is also useful for entertainment and educational purposes. To create a realistic experience, virtual reality technology requires accurate human pose estimation, of which a main component is localizing the body parts, or ”keypoints”, of the individual user. However, varying angles, inconsistent illumination, occlusion, ambiguity of human poses, and other factors make pose estimation a difficult task. In this paper, we focus on monocular pose estimation for virtual reality motion capture. Based off of a singular image of a person, we aim to accurately estimate their 2D skeletal pose. With this, we can advance to monocular 3D pose estimation. This would allow 3D pose detection using a single webcam, which can be used for more accessible, commercially-viable virtual reality motion capture. Currently, full-body presence in VR is achieved via expensive trackers which must be worn on the body during usage; while accurate, these trackers are more expensive and complicated to use compared to a webcam. Many researches have worked on solving this problem. For single-person pose estimation, Su et al. proposed a novel Cascade Feature Aggregation (CFA) method, which cascades hourglass networks for more robust pose estimation. Through their testing on MPII and LIP datasets, this CFA achieves the best performance on state-of-the-art benchmark MPII [1]. The next step after individual pose estimation would be multiple-person pose estimation, which we do not cover. This was considered by Cao et al., who presented an effective method to detect the 2D pose of multiple people in an image using a nonparametric representation [2]. Their work marks the release of OpenPose, which is the first real-time system for multiple-person 2D pose detection [2]. Their paper details a multi-stage CNN, in which they use convolution blocks and concatenate the predictions of each stage. Our method builds on these. We use convolutional networks to predict the human keypoints for 2D pose estimaFigure 1. Cascading Convolutional Structure. Upsampled and Downsampled data is added together to preserve features across resolutions.
CIRCULARLY POLARIZED MULTIPLE LAYER DIELECTRIC RESONATOR ANTENNAS

A sequence of anisotropic and isotropic materials of dielectric constant 12 and 10 respectively have been stacked alternatively to form a four-layer stack structure with aperture coupled feed mechanism for excitation. Applying this excitation, orthogonal mode pair TEδ21 and TE y 2δ1 has been excited at frequencies 7.54 GHz and 7.8 GHz, respectively in Y Z and ZX planes to generate circular polarization. A circularly polarized bandwidth in the region (7.54 GHz–7.92 GHz) in conjunction with impedance bandwidth in the region (5.23 GHz–5.52 GHz) with a gain of 5.2 dBi has been accomplished. The designed antenna is appropriate for C-band and weather radar applications. The design assessment has been done using Ansys HFSS. The three stages of antenna design are examined. Further, the design is investigated with a 6-layer structure and an 8-layer structure.
“You’re the One That Was on Uncle’s Wall!”: Identity, Whanaungatanga and Connection for Takatāpui (LGBTQ+ Māori)

Takatāpui (Māori LGBTIQ+) challenge static notions of relationality and belonging or whanaungatanga for Māori. Explorations of Māori and LGBTIQ+ identity can often polarise experiences of family as either nurturing spaces or sites comprised of actors of spiritual and physical violence. However, such framing ignores the ways in which cultural practices for establishing relationality for takatāpui extend beyond dichotomies of disconnection or connection within families and into spaces of new potential. In this paper we outline a bricoleur research praxis rooted in Māori ways of being which underpins the research. We engage in photo-poetry as an analytic tool, constructing poetry from our interviews with Waimirirangi, a twenty-year-old whakawahine (Māori term for trans woman or trans femme) and bring them into conversation with the images she provided as part of the broader research project. As the interface between her ancestors and future generations, Waimirirangi demonstrates the potentiality of whanaungatanga as a restorative practice for enhancing takatāpui wellbeing.
Shrimps of the genus Thor Kingsley, 1878 (Caridea, Thoridae): description of a new species using integrative data, remarks on Thor manningi Chace, 1972, and a world identification key

( versus absent in the new species), and the pterygostomial margin rounded and unarmed in T. manningi ( versus angular, with tooth in the new species). Thor manningi is morphologically closer to the western Atlantic Thor dobkini Chace, 1972 and Thor floridanus Kingsley, 1878
WIDE-BAND LOG-PERIODIC MICROSTRIP ANTENNA WITH DEFECTED GROUND STRUCTURE FOR C-BAND APPLICATIONS

The manuscript presents a log-periodic microstrip antenna with a defective ground structure (LPMADGS). The antenna is simulated, designed, and validated for C-band applications. The design of the antenna consists of three layers with upper most layer consisting of log-periodic, copper patches with a thickness of 0.035 mm; the middle layer is a 2 mm thick dielectric layer of FR-4 substrate; and the bottom layer is a defected ground structure (concentric ring resonators of 0.035 mm thickness). The suggested antenna design is simulated with a complete ground plane, without ground plane, and with a defective ground plane. The proposed antenna with optimized design is fabricated by wet etched method. The simulated results are approximately similar to the experimentally measured results. The experimentally measured results show transmission peaks at 7.65 GHz and 7.90 GHz. The resonating effect of log-periodic patches with a defected ground structure results in wide-band of 0.91 GHz (−10 dB bandwidth). The proposed antenna structure exhibits a wide bandwidth transmission which mostly resonates in frequency range that lies in C-band. It has future applications for mobile as well as wireless communication.
ARTIFICIAL INTELLIGENCE – KEY FOR RECENT BUSINESS PERFORMANCE

: Development of recent technologies tremendously identified a tool name artificial intelligence (AI) which arises a boom across all sectors and also create widespread fear especially of job losses for all people and also further rises inequality. This research paper discusses how AI will be an effective business development tool and also highlighted driving forces, challenges in deploying, platform readiness which improvise the productivity related / unrelated to the sectors both for skilled and unskilled professionals. This paper also highlighted the above factors through by developing a research model which helps the readers to better understand the behavioural characteristics / perception towards AI. The paper focused more for technological practitioners who have blind idea / image about AI which helps them to better understand the right path to deploy a platform like this to attain maximum benefit and allow business a tremendous opportunity for longer survival especially in this technological world .
Towards Ontology-based Cyber Threat Response

Response to the threats of information security in conditions of modern organization with а large infrastructure is an area with emer­ gency loaded intensity of the data usage. For а successful exposure and the prevention of computer attacks the construction of complex models of the events is required. ln this work, the question of the applicabll­ ity of ontological models is examined for the description of threats. On the basis of worked out applied ontologies, the model architecture of the knowledge base is being offered, the possiЫe practical scenarios of its use are being examined. The peculiarities of this work are the usage of rea­ soning on the different stages of event handling and design of knowledge, not only about events but also about an information infrastructure and its safety. Thus, the examined semantic technologies can Ье а base for the complete system of response to the threats of information security.
Delineation from Remotely-sensed Data

10 To assess the accuracy of individual tree crown (ITC) delineation techniques the same tree 11 needs to be identified in two different datasets, for example, ground reference (GR) data and 12 crowns delineated from LiDAR. Many studies use arbitrary metrics or simple linear-distance 13 thresholds to match trees in different datasets without quantifying the level of agreement. For 14 example, successful match-pairing is often claimed where two data points, representing the 15 same tree in different datasets, are located within 5m of one another. Such simple measures 16 are inadequate for representing the multi-variate nature of ITC delineations and generate 17 misleading measures of delineation accuracy. In this study, we develop a new framework for 18 objectively quantifying the agreement between GR and remotely-sensed tree datasets: the 19 Accuracy of Remotely-sensed Biophysical Observation and Retrieval (ARBOR) framework. 20 Using common biophysical properties of ITC delineated trees (location, height and crown 21 area), trees represented in different data sets were modelled as overlapping Gaussian curves 22 to facilitate a more comprehensive assessment of the level of agreement. Extensive testing 23 quantified the limitations of some frequently used match-pairing methods, in particular, the 24 Hausdorff distance algorithm. We demonstrate that within the ARBOR framework, the 25 Hungarian combinatorial optimisation algorithm improves the match between datasets, while 26 the Jaccard similarity coefficient is effective for measuring the correspondence between the 27 matched data populations. The ARBOR framework was applied to GR and remotely-sensed 28 tree data from a woodland study site to demonstrate how ARBOR can identify the optimum 29 ITC delineation technique, out of four different methods tested, based on two measures of 30 statistical accuracy. Using ARBOR will limit further reliance on arbitrary thresholds as it 31 provides an objective approach for quantifying accuracy in the development and application 32 of ITC delineation algorithms. 33
Strong Precipitation and Human Activity Spur Rapid Nitrate Deposition in Estuarine Delta: Multi-Isotope and Auxiliary Data Evidence

The intensive development of the Yellow River Delta has caused huge transportation of non-point pollutants into the Bohai Sea through source river estuaries and thus poses a considerable threat to eco-environmental security in the region. Long-term irrigation in the Yellow River basin, with occasional heavy rainfall and the related effects of ensuring hydrological processes and human activities in terms of nitrate N transport via surface water systems, is unclear. Using stable isotope (δ2H-H2O and δ18O-H2O, δ15N-NO3− and δ18O-NO3−) and auxiliary geographic data, the ISO source model was run to quantitatively analyze the supply relationship of river systems and the rapid change in the spatial pattern of nitrate N due to heavy rainfall in the estuarine delta. This analysis made clear the dominant contribution of agricultural activities and urbanization to NO3−-N emission, on which basis refined management measures were proposed to deal with NO3− in surface water from the “source-process”. The results of the study show that: (1) The relationship of surface water replenishment in the Yellow River Delta was affected not only by rainfall, irrigation, and other water conservancy measures but also the proportion of water from Yellow River flow declined from the source to estuary; (2) To a certain extent, rainfall diluted the concentration of nitrate N in the river and increased instantaneous flux of nitrate N into the sea, where nitrate N flux continuously increased from upstream to downstream; (3) The rapid deposition of nitrate in the estuary delta was driven by heavy rainfall and human activities such as excessive use of nitrogen fertilizers, rapid urbanization, and livestock waste discharge, and; (4) Scientific measures were needed to realize the interactive effect of the output of non-point source pollutants and the carrying and absorption capacity of coastal fragile ecosystems of the exogenous inputs.
Sonochemically produced hemoglobin microbubbles

Using high-intensity ultrasound, we have developed a method for the synthesis of airfilled hemoglobin (Hb) microbubbles (≈2.5 μm in diameter). Transmission electron, scanning electron, and optical microscopy show spherical particles with a shell thickness of approximately 35 nm, or roughly six protein molecules thick. The mechanism of microbubbles formation has been determined to involve both the dispersion of gas into micron-sized bubbles and the chemical cross-linking of cysteine residues between protein molecules. The primary oxidizing agent is superoxide (HO 2 ), which is sonochemically produced from oxygen and water during acoustic cavitation. The Hb microbubbles possess many of the desired characteristics of a blood substitute. The microbubbles are smaller than red blood cells and will not block capillaries. The microbubbles are air-filled and provide a large O 2 carrying capacity. The hemoglobins of the microbubbles retain their ability to bind oxygen reversibly. In addition, the oxygen affinities are similar to those of native Hb. Even more surprisingly, microbubbles show extensive cooperativity, as indicated by Hill coefficients as high as 18, which means that in the microbubble shell, there is communication between several of the crosslinked Hb tetramers upon binding oxygen. The Hb microbubbles show minimal degradation (>25%) after storage for six months at 4 °C.