Miracle-dz/UniFAR-FTUs huggingface dataset:
Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task

Rapid growth of the biomedical literature has led to many advances in the biomedical text mining field. Among the vast amount of information, biomedical article abstracts are the easily accessible sources. However, the number of the structured abstracts, describing the rhetorical sections with one of Background, Objective, Method, Result and Conclusion categories is still not considerable. Exploration of valuable information in the biomedical abstracts can be expedited with the improvements in the sequential sentence classification task. Deep learning based models has great performance/potential in achieving significant results in this task. However, they can often be overly complex and overfit to specific data. In this project, we study a state-of-the-art deep learning model, which we called SSN-4 model here. We investigate different components of the SSN-4 model to study the trade-off between the performance and complexity. We explore how well this model generalizes to a new data set beyond Randomized Controlled Trials (RCT) dataset. We address the question that whether word embeddings can be adjusted to the task to improve the performance. Furthermore, we develop a second model that addresses the confusion pairs in the first model. Results show that SSN-4 model does not appear to generalize well beyond RCT dataset.
Neuron-Specific Dropout: A Deterministic Regularization Technique to Prevent Neural Networks from Overfitting & Reduce Dependence on Large Training Samples

In order to develop complex relationships between their inputs and outputs, deep neural networks train and adjust large number of parameters. To make these networks work at high accuracy, vast amounts of data are needed. Sometimes, however, the quantity of data needed is not present or obtainable for training. Neuron-specific dropout (NSDropout) is a tool to address this problem. NSDropout looks at both the training pass, and validation pass, of a layer in a model. By comparing the average values produced by each neuron for each class in a data set, the network is able to drop targeted units. The layer is able to predict what features, or noise, the model is looking at during testing that isn't present when looking at samples from validation. Unlike dropout, the"thinned"networks cannot be"unthinned"for testing. Neuron-specific dropout has proved to achieve similar, if not better, testing accuracy with far less data than traditional methods including dropout and other regularization methods. Experimentation has shown that neuron-specific dropout reduces the chance of a network overfitting and reduces the need for large training samples on supervised learning tasks in image recognition, all while producing best-in-class results.
Using Pre-Trained Models to Boost Code Review Automation

Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep Learning (DL) models targeting the automation of two tasks: the first model takes as input a code submitted for review and implements in it changes likely to be recommended by a reviewer; the second takes as input the submitted code and a reviewer comment posted in natural language and automatically implements the change required by the reviewer. While the preliminary results we achieved are encouraging, both models had been tested in rather simple code review scenarios, substantially simplifying the targeted problem. This was also due to the choices we made when designing both the technique and the experiments. In this paper, we build on top of that work by demonstrating that a pre-trained Text-To-Text Transfer Transformer (T5) model can outperform previous DL models for automating code review tasks. Also, we conducted our experiments on a larger and more realistic (and challenging) dataset of code review activities.
When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data

One of the most remarkable things about the human moral mind is its flexibility. We can make moral judgments about cases we have never seen before. We can decide that pre-established rules should be broken. We can invent novel rules on the fly. Capturing this flexibility is one of the central challenges in developing AI systems that can interpret and produce human-like moral judgment. This paper details the results of a study of real-world decision makers who judge whether it is acceptable to break a well-established norm: ``no cutting in line. '' We gather data on how human participants judge the acceptability of line-cutting in a range of scenarios. Then, in order to effectively embed these reasoning capabilities into a machine, we propose a method for modeling them using a preference-based structure, which captures a novel modification to standard ``dual process'' theories of moral judgment.
Safe Online Bid Optimization with Return-On-Investment and Budget Constraints subject to Uncertainty

In online marketing, the advertisers’ goal is usually a tradeoff between achieving high volumes and high profitability. The companies’ business units customarily address this tradeoff by maximizing the volumes while guaranteeing a lower bound to the Return On Investment (ROI). Technically speaking, such a task can be naturally modeled as a combinatorial optimization problem subject to ROI and budget constraints to be solved online since the parameter values are uncertain and need to be estimated during the sequential arrival of data. In this picture, the uncertainty over the constraints’ parameters plays a crucial role. Indeed, these constraints can be arbitrarily violated during the learning process due to an uncontrolled algorithms’ exploration, and such violations represent one of the major obstacles to the adoption of automatic techniques in real-world applications as often considered unacceptable risks by the advertisers. Thus, to make humans trust online learning tools, controlling the algorithms’ exploration so as to mitigate the risk and provide safety guarantees during the entire learning process is of paramount importance. In this paper, we study the nature of both the optimization and learning problems. In particular, when focusing on the optimization problem without uncertainty, we show that it is inapproximable within any factor unless P = NP, and we provide a pseudo-polynomial-time Corresponding author Email address: nicola.gatti@polimi.it (Nicola Gatti) Preprint submitted to Journal of LTEX Templates January 19, 2022 algorithm that achieves an optimal solution. When considering uncertainty, we prove that no online learning algorithm can violate the (ROI or budget) constraints during the learning process a sublinear number of times while guaranteeing a sublinear pseudo-regret. Thus, we provide an algorithm, namely GCB, guaranteeing sublinear regret at the cost of a potentially linear number of constraints violations. We also design its safe version, namely GCBsafe, guaranteeing w.h. p. a constant upper bound on the number of constraints violations at the cost of a linear pseudo-regret. More interestingly, inspired by the previous two algorithms, we provide an algorithm, namely GCBsafe(ψ, φ), guaranteeing both sublinear pseudo-regret and safety w.h. p. at the cost of accepting tolerances ψ and φ in the satisfaction of the ROI and budget constraints, respectively. This algorithm actually mitigates the risks due to the constraints violations without precluding the convergence to the optimal solution. Finally, we experimentally compare our algorithms in terms of pseudo-regret/constraint-violation tradeoff in settings generated from real-world data, showing the importance of adopting safety constraints in practice and the effectiveness of our algorithms.
Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces

Brain computing interfaces (BCI) are used in a plethora of safety/privacy-critical applications, ranging from healthcare to smart communication and control. Wearable BCI setups typically involve a head-mounted sensor connected to a mobile device, combined with ML-based data processing. Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers. In this paper, we: (i) analyse the whole-system security and privacy threats to existing wearable BCI products from an operating system and adversarial machine learning perspective; and (ii) introduce Argus, the first information flow control system for wearable BCI applications that mitigates these attacks. Argus' domain-specific design leads to a lightweight implementation on Linux ARM platforms suitable for existing BCI use-cases. Our proof of concept attacks on real-world BCI devices (Muse, NeuroSky, and OpenBCI) led us to discover more than 300 vulnerabilities across the stacks of six major attack vectors. Our evaluation shows Argus is highly effective in tracking sensitive dataflows and restricting these attacks with an acceptable memory and performance overhead (<15%).
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K. M. Pierce Taken from a larger study about life in high school classrooms from students’ perspectives, this paper discusses how study participants pinpointed individual classrooms as the nerve centers in students’ high school experiences. Punctuating the swirls of movement within school days, individual class periods contain clues about how students construct knowledge and meaning in school. Nested within classrooms period to period, participants reported being tangled in webs of peer influence that variously encourage, constrict, poison, and otherwise determine students’ classroom interaction. Each 42-minute class period or classroom episode necessitates that students perform a kind of double-duty as they strike appropriate academic and social poses not only for their teacher but—much more importantly—for their peers, who create classrooms that can be comfortable, indifferent, or perilous to students. Once inside individual classrooms, students work hard to follow tacit codes for appropriate behavior among assembled peers. Although this unspoken but de facto student culture can differ period to period, it nevertheless dominates students’ experience of school. Because this finding reveals so much about students’ social compromise and so little about their engaged learning, it seems that classroom teachers hold the surest, most immediate power to reclaim and reform classrooms from sites of student accommodation to sites of active and even enjoyable accomplishment.
05 Resosudarmo

Illegal logging is one of the major causes of deforestation in many parts of the world. Frequently, this illegal activity goes hand in hand with bribery and corruption. This paper examines wood-based firms’ characteristics to determine the likelihood of their involvement in bribery and illegal logging in Indonesia. It adopts the Gary Becker’s model of crime to develop a firm demand function of illegally harvested logs. The firm-level data set available from the Indonesian Central Statistics Agency is used to estimate this demand function.
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© 2007 The University of North Carolina Press The research examining the correlates of academic achievement is immense. In particular, scores of studies have examined individualand family-level variables that influence student achievement. Based upon Bronfenbrenner’s (1979) ecological theory of human development, this study extends one step beyond previous studies and incorporates school-level characteristics into an investigation of the factors that influence adolescents’ academic achievement. Using regressionbased techniques that account for withinschool clustering of students, this research examined the extent to which individual-level and school structural variables predict academic achievement among a sample of 10th grade African American students abstracted from the National Educational Longitudinal Study (NELS) database. The results suggest that individual-level predictors, such as student effort, parent-child discussion, and associations with positive peers, play a substantial role in increasing students’ achievement. Further, the results also suggest that school climate, in particular the sense of school cohesion felt by students, teachers, and administrators, is important to successful student outcomes. Given these findings, the author suggests that an ecological approach which encompasses individual-, family-, and schoollevel variables be considered when examining predictors of academic achievement. Also, policy and interventions aimed at improving academic achievement need to take into consideration the impact of individual-level and school structural factors on students and their ability to succeed.
Homology of homologous knotted proteins

Quantification and classification of protein structures, such as knotted proteins, often requires noise-free and complete data. Here, we develop a mathematical pipeline that systematically analyses protein structures. We showcase this geometric framework on proteins forming open-ended trefoil knots, and we demonstrate that the mathematical tool, persistent homology, faithfully represents their structural homology. This topological pipeline identifies important geometric features of protein entanglement and clusters the space of trefoil proteins according to their depth. Persistence landscapes quantify the topological difference between a family of knotted and unknotted proteins in the same structural homology class. This difference is localized and interpreted geometrically with recent advancements in systematic computation of homology generators. The topological and geometric quantification we find is robust to noisy input data, which demonstrates the potential of this approach in contexts where standard knot theoretic tools fail.
LANGUAGE LEARNING AMONG DEAF CHILDREN

The lexical quality hypothesis asserts that wordspecific memories of orthographic, phonological, and semantic constituents for each word in a reader’s vocabulary are a prerequisite for a high level of proficiency in reading and writing (Perfetti, 1992). Within this framework, a highquality lexical representation is complete and accurate in all three constituents, with strong links in between (Perfetti, 2007). Whether this soundformmeaning triangle can be solidified in beginning readers who are deaf or hard of hearing has not been examined empirically, however. Furthermore, the lexical constituency model (Perfetti, Liu, & Tan, 2005) also suggests that phonological awareness might be less important than semanticsrelated factors in reading logographic languages such as Chinese, which conveys more meaning than sound. The phonological structure and other characteristics of writing systems decide the nature of phonological awareness and its relationship with reading (Ching & Nunes, 2015). This, in turn, might suggest a different phonologicalorthographicsemantic relationship for nonalphabetic writing systems such as Chinese, compared with alphabetic writing systems such as English. Chinese children who are deaf or hard OMPARING DEAF and hard of hearing (DHH) children with their hearing peers in learning Chinese, the study tested the lexical quality hypothesis (Perfetti, 1992, 2007), which asserts the importance of building orthographic, phonological, and semantic connections in highquality lexical representations. DHH children and hearing peers matched on reading age were randomly assigned to one of two groups: One received writing training on both Day 1 and Day 2; the other received pinyin typing training on Day 1 and writing training on Day 2. Compared with younger hearing controls, DHH children showed equivalent vocabulary acquisition and appeared to benefit from orthography training. The study demonstrated, for the first time, the writing effect in DHH children’s acquisition of Chinese. It suggests that a combination of character handwriting and phonology training can promote the ability of children to read Chinese, including children who are DHH. CONNIE QUN GUAN AND YE WANG
01 Peridy

This paper is aimed at providing a better understanding of FDI determinants in the main ASEAN countries through new developments in FDI theories. This paper simultaneously explores bilateral, third-country as well as regional integration as determinants for FDI. As an extension to the exisiting literature, it first proposes a theoretical framework which builds on and extends the work of Baltagi, Egger, and Pfaffermayr (2007) by including the impact of regional integration. The theoretical model is then tested empirically for FDI flows from the United States to five ASEAN countries, i.e., Singapore, Malaysia, Thailand, Indonesia and Philippines, over the period 1995–2007 for fifteen industries. Alternative panel data models are considered, especially models with spatially correlated residuals. The results show that third-country effects and regional integration are significant determinants of FDI in ASEAN countries, especially for vertical and complex vertical FDI.
03 Khuong

This paper analyses the sources of Singapore’s GDP and labour productivity growth in 1965– 2008 and reveals insights into related trends and patterns. The study documents four main findings. Firstly, both GDP and average labour productivity (ALP) growth in Singapore tended to decline during this period, and the contribution of ALP growth to GDP growth tended to decrease over time. Secondly, the contribution of labour to GDP growth was rather stable at approximately 1.5 to 2 percentage points per year, of which an increasing share came from foreign labour. Thirdly, total factor productivity growth improved substantially after the mid-1980s and has become a healthy source of GDP and ALP growth since that time. Finally, Singapore’s ALP low growth and its declining trends has become notable recently, of which the main reason was a sharp decrease in the contribution of capital deepening. The study discusses the challenges facing Singapore’s sharply declining ALP growth rate and proposes policy approaches that might help boost the country’s ALP growth in the future.
Antimicrobial Efficacy of Un-Ionized Ammonia (NH3) against Salmonella Typhimurium in Buffered Solutions with Variable pH, NH3 Concentrations, and Urease-Producing Bacteria

Poultry litter is a widely used biological soil amendment in the production of fresh produce. However, poultry litter may contain human pathogens, such as Salmonella, which introduces the risk of preharvest produce contamination in agricultural fields. ABSTRACT The presence of Salmonella in poultry litter, when used as a biological soil amendment, presents a risk for the preharvest contamination of fresh produce. Poultry litter is rich in organic nitrogen, and previous studies have suggested that ammonia (NH3) in poultry litter may affect the survival of Salmonella. Salmonella enterica serovar Typhimurium was inoculated into buffer solutions to characterize the pH dependency, minimum antimicrobial concentration, and efficacy of NH3 production. In solutions with 0.4 M total ammonia nitrogen (TAN) at various pH levels (5, 7, 8, and 9), significant inactivation of Salmonella only occurred at pH 9. Salmonella was reduced by ∼8 log CFU/mL within 12 to 18 h at 0.09, 0.18, 0.26, and 0.35 M NH3. The minimum antimicrobial concentration tested was 0.04 M NH3, resulting in an ∼7 log CFU/mL reduction after 24 h. Solutions with urea (1% and 2%) and urease enzymes rapidly produced NH3, which significantly reduced Salmonella within 12 h. The urease-producing bacterium Corynebacterium urealyticum showed no antagonistic effects against Salmonella in solution. Conversely, with 1% urea added, C. urealyticum rapidly produced NH3 in solution and significantly reduced Salmonella within 12 h. Salmonella inactivation data were nonlinear and fitted to Weibull models (Weibull, Weibull with tailing effects, and double Weibull) to describe their inactivation kinetics. These results suggest that high NH3 levels in poultry litter may reduce the risk of contamination in this biological soil amendment. This study will guide future research on the influence of ammonia on the survival and persistence of Salmonella in poultry litter. IMPORTANCE Poultry litter is a widely used biological soil amendment in the production of fresh produce. However, poultry litter may contain human pathogens, such as Salmonella, which introduces the risk of preharvest produce contamination in agricultural fields. Ammonia in poultry litter, produced through bacterial degradation of urea, may be detrimental to the survival of Salmonella; however, these effects are not fully understood. This study utilized aqueous buffer solutions to demonstrate that the antimicrobial efficacy of ammonia against Salmonella is dependent on alkaline pH levels, where increasing concentrations of ammonia led to more rapid inactivation. Inactivation was also demonstrated in the presence of urea and urease or urease-producing Corynebacterium urealyticum. These findings suggest that high levels of ammonia in poultry litter may reduce the risk of contamination in biological soil amendments and will guide further studies on the survival and persistence of Salmonella in poultry litter.
Plasma Jet Prepared Gold and Silver Nanoparticles to Induce Caspase-Independent Apoptosis in Digestive System Cancers

Alot of medical and industrial applications used the metal nanoparticles (NPs) with increase interest to be used as cancer therapy. The current work aimed to prepare AuNPs and AgNPs through the use of plasma jet and test their antitumor mechanism of apoptosis induction. The results indicating the face-centered cubic structures and crystalline nature of AuNPs and AgNPs. Also, the image of FESEM showed that the well dispersions regarding AuNPs and AgNPs, while the NP’s spherical shape with the particle size distributions which are considered to be close that estimated from the XRD. cytotoxicity have been assessed against the Normal embryonic cell line REF and the digestive system (HC , SK-GT-4) cell lines under a variety of the series dilute of the Ag and Au NPs (6.25, 12.5, 25, 50 and 100%), have been determined through a microtetrazolium (MTT) assay. The capacity of Ag and Au NPs to induce apoptosis to an infected cell has been studied by crystal violet stain to measure the percentage of induction of apoptosis. In cases where 100 μg\ml Au NP concentrations are 69.60 percent, the maximum cytotoxicity of the HC cell line was reported, while 100 μg\ml Au NP was 69.20% for the SKg cell line exposure. qRT-PCR in AuNPs and AgNPs treated of (HC and SKG) cell lines revealed a remarkable in the expression of BAX, BCL2 and AIF, Endo G (independent pathway).
Approximating 4-cliques in streaming graphs: the power of dual sampling

Clique counting is considered to be a challenging problem in graph mining. The reason is combinatorial explosion; even moderate graphs with a few million edges could have clique counts in the order of many billions. In this paper, we propose a fast and scalable algorithm for approximating 4-clique counts in a single-pass streaming model. By leveraging a combination of sampling approaches, we estimate the 4-clique count with high accuracy. Our algorithm performs well on massive graphs containing several billions of 4-cliques, and terminates within a reasonable amount of time.