Hard Task Research Articles

Book review: Kevin Lane Keller and Vanitha Swaminathan (2020). Strategic Brand Management: Building, Measuring, and Managing Brand Equity, 5th edition, Pearson Education Limited, ISBN 978-0-13-489249-8

What are brands? Brands are intangible assets that are invaluable for an organization. Though, creating and bringing up strong brands over a while against immense competition is a hard task and a challenge for managers. Creating and nurturing a huge brand doesn’t happen overnight. Sound plans for the brand are essential in nurturing a strong brand and it consumes a considerable period. Just planning to build strong brands is not sufficient to generate yield on the investment. But powerful strategies are a must to endure strong brands over the huge competition in the digital era. Thus, all-powerful marketing thinking should be directed at strengthening the brand to generate yield on investment. It has been implied that a comprehensive and cohesive system is fundamental to fostering an outstanding brand.

Emergency shelter materials under a complex non-linear diophantine fuzzy decision support system

AbstractThe distribution of emergency shelter materials in emergency cases around the world is a hard task, the goal of this research is to offer a Complex Non-linear Diophantine Fuzzy (C-NLDF) decision-making model for earthquake shelter construction. Essentially, the article is divided into three sections to acquire acceptable and precise measures in emergency decision-making situations. First, we present the Complex Non-Linear Diophantine Fuzzy Set (CN-LDFS), a new generalization of the complex linear Diophantine fuzzy set (CLDFS) and q-linear Diophantine fuzzy set (q-LDFS), as well as explore its key aspects. Furthermore, aggregation operators are useful for aggregating uncertainty in decision-making issues. As a result, algebraic norms for CN-LDFSs are produced based on certain operational laws. In the second section of the work, we offer a series of averaging and geometric aggregation operators under CN-LDFS that are based on defined operating laws. In the final section of the work, under complex Non-linear Diophantine fuzzy information, the ranking algorithms based on suggested aggregation operators are present to address the case study regarding emergency situation of earthquakes. In comparison section, results of existing and proposed operators explore the effectiveness of proposed methodologies and provide accurate emergency measures to address the global uncertainty about the construction of emergency shelters in earthquakes.

A Hybrid Scheduling Framework for Mixed Real-Time Tasks in an Automotive System With Vehicular Network

As vehicles integrate more and more autonomous driving functionalities, it becomes more and more important to use vehicular networks to fully guarantee the safety and real-time performance of on-board computing tasks. Current studies on vehicular networks pay much attention to the performance improvement of network communication and resource allocation, while ignoring the fact that automotive on-board computing tasks play a significant role in vehicle safety and need to be elegantly handled in vehicular networks. In this paper, we propose a hybrid scheduling framework for meeting all hard real-time task deadlines while minimizing soft real-time task deadline misses. In particular, the proposed scheduler is composed of some local schedulers and a global scheduler, where the former guarantees the schedulability of all hard real-time tasks, and the latter decides the assignment of soft real-time jobs dynamically online. Depending on the remaining processing capability of a vehicular network, arrival jobs are either assigned for further processing or discarded. An approach combining the utilization-based schedulability test and demand-supply analysis is proposed to effectively assign tasks to processors offline. To meet as many soft real-time task deadlines as possible, tasks' demand and supply bounds are computed at runtime, such that online execution information can be used to compensate for the scheduling loss with the worst-case assumption made by the offline test. Experimental results demonstrate that, compared to a genetic algorithm, our proposed approach needs far less computation to assign more tasks offline. The online scheduling also saves many soft real-time jobs that were to be dropped by the offline algorithm.

Using Keyform Maps to Measure Confidence & Competency Ratios to Promote OT Student Success

Abstract Date Presented 04/22/2023 A confidence-to-competence ratio is helpful to identify potential overly aggressive or overly conservative treatments (Gottlieb et al., 2021). Craig Velozo’s 2021 Eleanor Clark Slagle lecture defined keyform maps as an evidence-based tool for identifying easy and hard task items and identifying a client’s just-right challenge. This research studied keyform maps as a tool for assessing confidence-to-competence ratios among OT students to promote student success and client safety. Primary Author and Speaker: Cynthia Sears Additional Authors and Speakers: Christa Lawler, Rachel Bernhardt, Morgan Foster

Detecting man-in-the-middle attacks via hybrid quantumclassical protocol in software-defined network

Man-in-the-middle (MitM) attacks became one of the most risk attacks on OpenFlow communication channel in software-defined networking, its detection is a very hard task due there is no authentication in OpenFlow protocol. This channel is the most important in the network and is responsible for sending the control commands from the controller to the switches, so once the OpenFlow channel is hacked, the entire network is controlled by the attacker. Therefore, we propose a complementary solution to transport layer security protocol to detect man-in-the-middle attacks based on hybrid quantum-classical protocol. Based on the hybrid protocol, an easy-toimplement authentication between controller and switches depends on quantum and classical security layers. Also, detect eavesdropping on channel depending on quantum parameters. In this paper, we implement a simulation of hybrid protocol using a software-defined networking emulator for monitoring the OpenFlow channel to detect attacks, and the results showed the ease of detecting the eavesdrop and verifying the authentication of the other party with a hybrid method to get a high level of authentication.

A Study of Agriculture Students’ Attitudes towards English Language: A Case Study

Background: Like other professional institutions i.e., medical, law and engineering, at the University of Agriculture, Peshawar, all the core subjects are taught through English and all the exams (both oral and written) are conducted in English; hence, proficiency in English is an essential need of every student. As a majority of the agriculture students perform poorly in the core subjects due to their weakness in the English language, therefore, this study was undertaken to explore the attitudes of agriculture students towards the English language and its four skills i.e., listening, speaking, reading and writing.
 Purpose: The main goal of the study was to investigate the Agriculture students’ attitude towards the English language and its basic skills. Moreover, the present study is also an attempt to unearth the differences, if any, in the attitudes of the male and female students towards English.
 Method: This study employed a mixed-methods approach to address the research questions. To obtain valid and reliable results, the researcher considered application of both the quantitative and qualitative approaches to be very appropriate for the collection and analysis of data for the present study. The quantitative data were collected by means of a five-point Likert scale questionnaire with 30 closed items and was analyzed using SPSS version 20.0. The qualitative data were collected through semi-structured interviews from 30 purposively selected students and were analyzed using thematic analysis.
 Conclusion: The findings revealed that the agriculture students had positive attitudes towards the English language and its four skills. The study also highlighted that there was no statistically significant difference in the attitudes of students by their gender; however, the female students displayed slightly more positive attitudes towards English as compared to their male counterparts. A majority of the students regarded writing in English as a hard task. However, most students desired achieving high proficiency in all the four skills of English language.

Gender of the Apologiser as a Social Factor in the Selection of Apology Strategies

"Politeness" is a culturally defined pattern of language use that enables the speaker to formulate appropriate speech acts like an apology. The speech act of apology is a part of pragmatics study and belongs to expressive speech acts. However, it cannot be separated from sociolinguistics because social factors such as social context, gender, and the participants' relationship can all have an impact on how apologizers make their apologies. The selection of the appropriate formulas of apology strategies to pay off violations of social norms is considered a hard task since it is based on some cultural and social norms. Thus, the present study aims to study the apology strategies used by Iraqi male/female postgraduate students at the University of Anbar, in addition to investigating the effect of gender on the use of polite apology strategies. An oral Discourse Completion Task was used as a data collection instrument, supported by a semi-structured interview. The data has been analysed using the descriptive analysis method, based on Leech’s (2014) classification of apology. The findings revealed that "expressing regret" was the most frequently used apology strategy. Furthermore, both genders used different indirect strategies to support their apologies. Both genders used a new supporting move, which is "admitting responsibility for in-group member’s fault." In conclusion, the gender of the participants has a slight effect on the choice of polite apology strategies. Moreover, social status and age were the most effective social factors as compared to the others.

An Underground Mine Ore Pass System Optimization via Fuzzy 0–1 Linear Programming with Novel Torricelli–Simpson Ranking Function

In this work, we propose a 3D dynamic optimization model that enables the design of an underground mine ore pass system with uncertainties. Ore transportation costs and ore pass development costs are quantified by triangular fuzzy numbers. Transportation costs are treated as production costs, and they vary over the duration of mining operation, while development costs of ore passes are treated as an investment, and they are treated as constant. The developed model belongs to the class of fuzzy 0–1 linear programming models, where the fuzzy objective cost function achieves a minimum value, with respect to given set of techno-dynamic constraints. Searching for optimal value in the fuzzy environment is a hard task, and because of that, we developed a new ranking function which transforms the fuzzy optimization model into a crisp one. A triangular fuzzy number can be presented as a triangular graph G(V,E) composed of vertices and edges. The x-coordinate of the Torricelli point of a triangular graph presents the crisp value of a triangular fuzzy number. The use of this model lets us know the optimal number of ore passes, optimal location of ore passes, and optimal dynamic ore transportation plan.

Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks

Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning problems. One possible solution to this issue is to exploit inherent task structures for an acceleration of exploration. In this paper, we present a novel exploration approach, which encodes a special structural prior on the reward function into exploration, for sparse-reward multi-agent tasks. Specifically, a novel entropic exploration objective which encodes the structural prior is proposed to accelerate the discovery of rewards. By maximizing the lower bound of this objective, we then propose an algorithm with moderate computational cost, which can be applied to practical tasks. Under the sparse-reward setting, we show that the proposed algorithm significantly outperforms the state-of-the-art algorithms in the multiple-particle environment, the Google Research Football and StarCraft II micromanagement tasks. To the best of our knowledge, on some hard tasks (such as 27m_vs_30m}) which have relatively larger number of agents and need non-trivial strategies to defeat enemies, our method is the first to learn winning strategies under the sparse-reward setting.

Context-Aware Transformer for 3D Point Cloud Automatic Annotation

3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined training for 3D foreground/background segmentation, cylindrical object proposals, and point completion. Furthermore, they often overlook the inter-object feature correlation that is particularly informative to hard samples for 3D annotation. To this end, we propose a simple yet effective end-to-end Context-Aware Transformer (CAT) as an automated 3D-box labeler to generate precise 3D box annotations from 2D boxes, trained with a small number of human annotations. We adopt the general encoder-decoder architecture, where the CAT encoder consists of an intra-object encoder (local) and an inter-object encoder (global), performing self-attention along the sequence and batch dimensions, respectively. The former models intra-object interactions among points and the latter extracts feature relations among different objects, thus boosting scene-level understanding. Via local and global encoders, CAT can generate high-quality 3D box annotations with a streamlined workflow, allowing it to outperform existing state-of-the-arts by up to 1.79% 3D AP on the hard task of the KITTI test set.

Learning Safe Numeric Action Models

Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Yet obtaining such an action model is a notoriously hard task. This task is even more challenging in mission-critical domains, where a trial-and-error approach to learning how to act is not an option. In such domains, the action model used to generate plans must be safe, in the sense that plans generated with it must be applicable and achieve their goals. Learning safe action models for planning has been recently explored for domains in which states are sufficiently described with Boolean variables. In this work, we go beyond this limitation and propose the NSAM algorithm. NSAM runs in time that is polynomial in the number of observations and, under certain conditions, is guaranteed to return safe action models. We analyze its worst-case sample complexity, which may be intractable for some domains. Empirically, however, NSAM can quickly learn a safe action model that can solve most problems in the domain.

Molecular Assays Simulator to Unravel Predictors Hacking in Goal-Directed Molecular Generations.

Generative models are being increasingly used in drug discovery, very often coupled with absorption, distribution, metabolism, and excretion (ADME) bioassays or quantitative structure-activity relationship (QSAR) models to optimize a given set of properties. The molecules proposed by these algorithms are often revealed to be false positives; that is, they are predicted to be active and turn out to be inactive after synthesis and testing, mostly due to overoptimization of the predicted scores, which leads to an actual decrease or stagnation of the real scores. This behavior is also known as the "hacking" of the predictive models by the generative model during the optimization step. This issue is reminiscent of adversarial examples in machine learning and it can be seen as enunciated by Goodhart's law: "when a measure becomes a target, it ceases to be a good measure." This issue is even more apparent in a multiparameter optimization (MPO) case, where the models need to extrapolate outside the training set distribution because there are no known molecules satisfying all the objectives simultaneously in the initial training set. Experimental evaluation of this problem is a hard and expensive task since it requires synthesis and testing of the generated molecules. Thus, efforts have been made to develop in silico "oracles"─real-valued functions used as proxies for molecular properties─to help with the evaluation of these generative-model-based pipelines. However, these oracles have had a limited value so far because they are often too easy to model in comparison with biological assays and are usually limited to mono-objective cases. In this work, we introduce a simulator of multitarget assays using a smartly initialized neural network (NN) that returns continuous values for any input molecule. We use this oracle to replicate a real-world prospective lead optimization (LO) scenario. First, we trained predictive models on an initial small sample of molecules aimed at predicting their oracle values. Afterward, we generated new optimized molecules using the open-source GuacaMol package coupled with the previously built predictive models. Finally, we selected compounds matching the candidate drug target profile (CDTP) according to the predicted values and evaluated them by computing the true oracle values. We observed that even when the predictive models had excellent estimated performance metrics, the final selection still contained multiple false positives according to the NN-based oracle. Then, we evaluated the optimization behavior in mono- and bi-objective scenarios using either a logistic regression or a random forest predictive model. We also propose and evaluate several methods to help mitigate the hacking issue.

Parental views of Disney Princesses in the context of value-based expectations for their pre-school children

Content analyses of the female image of Disney Princess (DP) tales increasingly report a change in the representation of women. While classic DPs are found to be passive in terms of the plot, they are not “saviour” and their romantic options are limited, newer princesses are more androgynous, more active, more “saviour”-like and freer in their decisions and choices than traditional ones. Media can influence gender stereotypes and gender-related behaviour in childhood, and women who identify themselves as “princesses” in adulthood typically think in gender stereotype patterns, are less motivated in self-actualisation, and are more likely to run away from hard tasks. All this draws attention to the possibility of the early socialisation effect of princess tales and raises the importance of examining individual characters. Quantitative study was conducted to examine how parents see their children’s favorite DPs, whether there is a correlation between parents’ value preferences for their kids and the character of favorite DPs perceived by the parents. Our self-developed questionnaire contained items from Schwartz Value Profile and previous studies about DPs. Participant (N=1310) were Hungarian parents of pre-school girls and boys, from various social background and residence. Our study found that parents’ own character preferences, their characterisation of their child and the value-based expectations placed on children are related to which Disney character they consider as their children’s favorite. Results can be discussed from different approaches: media effect, gender stereotypes, national characteristics of parenting and values.

CARTIS: a method for the typological-structural characterization of Italian ordinary buildings in urban areas

Large-scale risk assessments relevant to natural hazards are commonly based on very poor exposure and vulnerability data, often drawn from census data. In fact, obtaining a detailed knowledge of the built heritage is a very hard task especially for those countries, like Italy, characterized by very high urban density and large variety of building typologies, where a building-by-building knowledge can sound as a utopian ambition. Nevertheless, exposure and vulnerability are two of the four factors governing, along with hazard and capacity, risk convolution, and hence their uncertainties yield to corresponding uncertainties in the resulting expected losses. The lack of suitable information on building typologies is responsible of very strong simplifications in risk analyses, like the assumption of the same building typologies, indistinctly scattered all over the Country territory, without distinctions at a local or at a regional level. With the goal of improving exposure description and reducing such uncertainties, since 2014 the Italian Civil Protection Department (ICPD) has undertaken a new research branch in the framework of ReLUIS (Network of University Laboratories in Earthquake Engineering) projects, dedicated to territorial analyses, by funding also the CARTIS project. The project has the goal to characterize the building structural typologies trough a data collection at a local and an extensive scale in Italy, with the final aim to improve the reliability of seismic risk analyses. The paper describes the method and some first statistics so far elaborated.

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR.

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

Performal: Formal Verification of Latency Properties for Distributed Systems

Understanding and debugging the performance of distributed systems is a notoriously hard task, but a critical one. Traditional techniques like logging, tracing, and benchmarking represent a best-effort way to find performance bugs, but they either require a full deployment to be effective or can only find bugs after they manifest. Even with such techniques in place, real deployments often exhibit performance bugs that cause unwanted behavior. In this paper, we present Performal, a novel methodology that leverages the recent advances in formal verification to provide rigorous latency guarantees for real, complex distributed systems. The task is not an easy one: it requires carefully decoupling the formal proofs from the execution environment, formally defining latency properties, and proving them on real, distributed implementations. We used Performal to prove rigorous upper bounds for the latency of three applications: a distributed lock, ZooKeeper and a MultiPaxos-based State Machine Replication system. Our experimental evaluation shows that these bounds are a good proxy for the behavior of the deployed system and can be used to identify performance bugs in real-world systems.

Abstract 80: The Impact of Covid-19 Pandemic in Colonoscopies and Colorectal, Rectosigmoid and Anal Cancer Screening

Abstract Purpose: Colorectal, rectosigmoid and anal cancer are responsible for the death of around 20,000 people in Brazil every year. Those types of cancer typically have a slow evolution, around 10 years. Thus, colonoscopies performed periodically are an important tool in the early detection of early neoplasms, increasing the survival rate. With the advent of the Covid-19 pandemic, many procedures considered non-urgent were canceled, such as colonoscopies for cancer screening, which left thousands of patients unattended. Methods: This is a descriptive analysis and data was collected from the DATASUS database, relating the number of colorectal, rectosigmoid and anus cancer diagnoses and performed colonoscopies among Brazilian regions in the years of 2019 and 2020. Results: In 2019, 347,098 colonoscopies were performed in Brazil and 36,326 people were diagnosed with colorectal, rectosigmoid or anus cancer. On the other hand, in 2020, only 241,329 colonoscopies were performed, meaning a 30,4% reduction in the number of procedures. However, 36.274 people were diagnosed with colorectal, rectosigmoid or anus cancer. Conclusion: With the advent of Covid-19 pandemic in 2020, many patients lost their connection with their hospitals and the constancy of carrying out tests, making screening and early detection of possible cancer a hard task. Although the number of diagnoses in 2020 did not have a significant drop, the decrease of more than 30% in the number of colonoscopies in that same year will have impacts not yet accounted for on the diagnoses of colorectal, rectosigmoid and anal cancers, since these cancers have a slow development. In view of this situation, solutions that recover the damned exams patients of the pandemic must be implemented so that cancer patients have a chance of treatment and cure. Citation Format: Rafaela de Andrade, Gabriele Eckerdt Lech, Pedro Antonio Paludo Menna Barreto, Arthur Minas Alberti, Luiza Seixas de Sá Beltramo, Fernando Azambuja, Laura Tibola Marques da Silva, Lucas Kieling, Pedro Arthur Zimmer Silveira. The Impact of Covid-19 Pandemic in Colonoscopies and Colorectal, Rectosigmoid and Anal Cancer Screening [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 80.

The effects of self-selected background music and task difficulty on task engagement and performance in a visual vigilance task.

Listening to self-selected background music has been shown to be associated with increased task focus and decreased mind wandering during a sustained attention task (Kiss and Linnell, Psychological Research Psychologische Forschung 85:2313-2325, 2021). It is unclear, however, how this relation may depend on the potentially critical factor of task difficulty. To address this knowledge gap, we explored how listening to self-selected music, compared to silence, affects subjectively experienced task engagement (i.e., task focus, mind wandering, and external distraction/bodily sensation states) and task performance during either an easy or a hard vigilance task. We also examined how these effects vary with time-on-task. Our results replicated prior work demonstrating that background music enhanced task focus and decreased mind wandering, compared to silence. There was also lower reaction time variability in the background music condition relative to the silence condition. Notably, these findings held regardless of task difficulty. Interestingly, when examined over time-on-task, the presence of music led to smaller task focus declines and mind wandering increases, compared to silence. Thus, listening to self-selected music appears to confer a protective effect on task engagement, especially over time-on-task.

Design of amphiphilic-function-aiding biopolymer adhesives for strong and durable underwater adhesion via a simple solvent-exchange approach

Achieving strong and durable underwater adhesion in an easy way is highly demanded for practical applications. Despite extensive progresses, developing high-performance underwater adhesives by leveraging low-cost and sustainable biopolymer materials remains a hard task. Here, we report a simple one-pot strategy to construct a superior underwater adhesive through designing an amphiphilic function (established by thioctic acid and epoxy connector) in commercial biopolymer system. This amphiphilic-function-aiding biopolymer adhesive (AFBA), prepared in ethanol solution, can harness solvent exchange to trigger hydrophobic aggregation and phase transition in water, thereby yielding hydrophobic repulsion for effective dehydration, high structural stability for bulk cohesion strengthening, and versatile molecular interactions for interfacial adhesion enhancement. As such, AFBA exhibits a strong yet durable underwater adhesion (constantly increasing from 560 to 729 KPa to wood substrate after storing 30 days) and remarkable tolerance to various harsh environments (seawater, acidic, and alkaline solutions), allowing for underwater applying as a sealant to repair damage. Moreover, this simple strategy is generic that can derive a family of AFBAs with excellent underwater adhesion. This work may provide a promising pathway for engineering high-performance adhesives, and also open up a new direction to overcome the limitations of hydrophilic biopolymers in underwater application.

Grain segmentation in sandstone thin-section based on computer analysis of microscopic images

The segmentation of minerals visible in sandstone thin-sections is a necessary part of microscopic estimation of mineral composition, the most important part of petrographic and sedimentological studies. This process is often a hard task due to the difficulty in determining the exact boundaries between grains, mainly caused by secondary alteration in minerals, etching of grains and pore-space filling by cements. Structural features play a very important role in mineral identification and undoubtedly without their use mineral recognition in thin sections gives many misclassification results. Calculation of each grain area is an important part of the process that is very time-consuming if done by-hand. Presented method provides a precise solution while mixing automated and non-automated approach. Photos of a sandstone thin section were taken using a Nikon Eclipse LV100N POL polarizing microscope, at 200x magnification, in transmitted light, with crossed polarizers. Then to determine the borders and therefore area of distinct grains, a sample image has been chosen. Initial contours have been created by-hand inside graphic application on a tablet device, as the non-automated part of the presented method. Afterward, the layer with marked boundaries was analyzed by computer software. In the automated part of the method, marked contours of grains were detected inside the algorithm with areas calculated afterward.