Reviewerly/exHarmony huggingface dataset:
Cost effective hybrid genetic algorithm for scheduling scientific workflows in cloud under deadline constraint

Cloud has emerged as a convenient platform for executing complicated scientific applications from multiple disciplines by providing on-demand and scalable infrastructure on rental basis. Research and scientific community often opt for workflows to model these scientific applications Workflow scheduling has been extensively studied for decades with regard to grid and cluster computing, but few initiatives have been tailored for cloud. What's more, the previous work fails to incorporate the basic principles of IaaS clouds like pay-as-you-go model, elasticity, heterogeneity, dynamic provisioning and issues of VM's performance variation and acquisition delay besides other QoS requirements. This paper proposes a resource provisioning and scheduling strategy using genetic algorithm with the aim to optimise the overall execution cost while staying below the given deadline. The performance is further enhanced by using a high quality seed generated by predict earliest finish time (PEFT) algorithm which acts as a catalyst and helps the algorithm to converge faster. The proposed approach is simulated in WorkflowSim and evaluated using various well-known different sized realistic scientific workflows. The results validate the better performance of our approach over numerous state-of-art-algorithms.
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System

In this short paper, we present our ongoing work on the veriFIRE project—a collaboration between industry and academia, aimed at using verification for increasing the reliability of a real-world, safety-critical system. The system we target is an airborne platform for wildfire detection, which incorporates two deep neural networks. We describe the system and its properties of interest, and discuss our attempts to verify the system’s consistency, i.e., its ability to continue and correctly classify a given input, even if the wildfire it describes increases in intensity. We regard this work as a step towards the incorporation of academic-oriented verification tools into real-world systems of interest.
Automating Ground Truth Annotations For Gland Segmentation Through Immunohistochemistry

Abstract The microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of inflammatory bowel disease(IBD) and cancer. When properly trained, deep learning pipelines can provide a systematic, reproducible, and quantitativeassessment ofdisease-related changes in glandular tissue architecture. The training and testing of deep learning modelsrequire large amounts of manual annotations, which are difficult, time-consuming, and expensive to obtain. Here, we propose amethod for the automated generation of ground truth in digital H&amp;E slides using immunohistochemistry (IHC) labels. The imageprocessing pipeline generates annotations of glands in H&amp;E histopathology images from colon biopsies by transfer of gland masks from CK8/18, CDX2, or EpCAM IHC. The IHC gland outlines are transferred to co-registered H&amp;E images for the trainingof deep learning models. We compare the performance of the deep learning models to manual annotations using an internalheld-out set of biopsies as well as two public data sets. Our results show that EpCAM IHC provides gland outlines that closelymatch manual gland annotations (DICE = 0.89) and are robust to damage by inflammation. In addition, we propose a simpledata sampling technique that allows models trained on data from several sources to be adapted to a new data source using justa few newly annotated samples. The best-performing models achieved average DICE scores of 0.902 and 0.89, respectively,on GLAS and CRAG colon cancer public datasets when trained with only 10% of annotated cases from either public cohort.Altogether, the performances of our models indicate that automated annotations using cell type-specific IHC markers can safelyreplace manual annotations. The automated IHC labels from single institution cohorts can be combined with small numbers ofhand-annotated cases from multi-institutional cohorts to train models that generalize well to diverse data sources
Determinants of Employee Retention in Pharmaceutical Companies: Case of Saudi Arabia

Saudi Arabia's fast-growing pharmaceutical market aims to retain employees to increase organizational effectiveness and reduce attrition costs. This study aimed to investigate employee retention determinants in Saudi pharmaceutical companies. In total, 325 responses were collected and analysed. Quantitative research was conducted using an electronic survey sent to Saudi pharmaceutical employees through different social media platforms. Different variables were collected from the survey tool using several question formats. Descriptive and inferential statistics were used for data analysis. Six determinants were identified to have a positive correlation with employee retention: salary/benefits, job/work conditions, work relationships, company culture, motivation, and leadership within the company. The motivation was a key factor influencing employee retention. Nearly 52.1% to 91.1% of employee retention was explained by the aforementioned determinants. Job/work conditions and motivation in the company were significant positive predictors of employee retention. Our study showed a significant relationship between salary/employee benefits, motivation, work conditions, organizational culture, leadership support, and employee retention in pharmaceutical companies in Saudi Arabia. Leaders in Saudi pharmaceutical companies should maintain long-term staff retention to boost organizational performance and minimise the cost of employee attrition.
Consistency Models

Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64x64 and LSUN 256x256.
PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages

Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score.
Hybrid algorithm proposal for optimizing benchmarking problems: Salp swarm algorithm enhanced by arithmetic optimization algorithm

Metaheuristic algorithms are easy, flexible and nature-inspired algorithms used to optimize functions. To make metaheuristic algorithms better, multiple algorithms are combined and hybridized. In this context, a hybrid algorithm (HSSAOA) was developed by adapting the exploration phase of the arithmetic optimization algorithm (AOA) to the position update part of the salp swarm algorithm (SSA) of the leader salps/salps. And also, there have also been a few new additions to the SSA. The proposed HSSAOA was tested in three different groups using 22 benchmark functions and compared with 7 well-known algorithms. HSSAOA optimized the best results in a total of 16 benchmark functions in each group. In addition, a statistically significant difference was obtained compared to other algorithms.
User Satisfaction Analysis f Service Quality Of Dapodik Applications (Educational Data) Using Servqual Method

Dapodik is an education data system with a national scale in which there are various kinds of information regarding educational data units. The problem that occurs is that the operator has difficulty if the input data is invalid, so to correct the invalid input data, it is done in the operator's account, while to synchronize the data the operator must first login to the principal's account. In addition, for filling the infrastructure (facilities and infrastructure) operators have difficulty measuring the percentage of damage that occurs to school facilities because they are not in their expertise or field. Therefore, this dapodik application needs to be measured user satisfaction with the quality of its services using the servqual method. This study aims to measure user satisfaction in using the dapodik application on the quality of its services using the servqual method. The results of the measurement can be concluded that the empathy dimension, H5 (-1.03) should be further improved because the value of user dissatisfaction is higher. In addition, based on tangible values, reliability, responsiveness and assurance are still not good, so it is necessary to improve the quality of dapodik application services.
Towards adversarial realism and robust learning for IoT intrusion detection and classification

The Internet of Things (IoT) faces tremendous security challenges. Machine learning models can be used to tackle the growing number of cyber-attack variations targeting IoT systems, but the increasing threat posed by adversarial attacks restates the need for reliable defense strategies. This work describes the types of constraints required for a realistic adversarial cyber-attack example and proposes a methodology for a trustworthy adversarial robustness analysis with a realistic adversarial evasion attack vector. The proposed methodology was used to evaluate three supervised algorithms, Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), and one unsupervised algorithm, Isolation Forest (IFOR). Constrained adversarial examples were generated with the Adaptative Perturbation Pattern Method (A2PM), and evasion attacks were performed against models created with regular and adversarial training. Even though RF was the least affected in binary classification, XGB consistently achieved the highest accuracy in multi-class classification. The obtained results evidence the inherent susceptibility of tree-based algorithms and ensembles to adversarial evasion attacks and demonstrates the benefits of adversarial training and a security by design approach for a more robust IoT network intrusion detection and cyber-attack classification.
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses

Website fingerprinting (WF) attacks, usually conducted with the help of a machine learning-based classifier, enable a network eavesdropper to pinpoint which website a user is accessing through the inspection of traffic patterns. These attacks have been shown to succeed even when users browse the Internet through encrypted tunnels, e.g., through Tor or VPNs. To assess the security of new defenses against WF attacks, recent works have proposed feature-dependent theoretical frameworks that estimate the Bayes error of an adversary's features set or the mutual information leaked by manually-crafted features. Unfortunately, as WF attacks increasingly rely on deep learning and latent feature spaces, our experiments show that security estimations based on simpler (and less informative) manually-crafted features can no longer be trusted to assess the potential success of a WF adversary in defeating such defenses. In this work, we propose DeepSE-WF, a novel WF security estimation framework that leverages specialized kNN-based estimators to produce Bayes error and mutual information estimates from learned latent feature spaces, thus bridging the gap between current WF attacks and security estimation methods. Our evaluation reveals that DeepSE-WF produces tighter security estimates than previous frameworks, reducing the required computational resources to output security estimations by one order of magnitude.
Generating Trees for 0021-Avoiding Inversion Sequences and a Conjecture of Hong and Li

An inversion sequence of length n is a word e = e0 • • • en which satisfies, for each i ∈ [n] = {0, 1, . . ., n}, the inequality 0 ≤ ei ≤ i.In this paper, by generating tree tools, an explicit formula is found for the generating function for the number of inversion sequences of length n that avoid 0021, which resolves the conjecture of Hong and Li posed in the recent paper [Electron.
UAV Route Planning to Anticipate the COVID-19 Crowd Clusters with Dynamic Programming

&lt;p&gt;Crowds are considered trivial by the community because they feel they have implemented health protocols by wearing masks. Crowds must be minimized so that the spread of the Covid-19 virus does not get higher. This paper aims to plan a UAV shuttle route so that it can approach as many locations as possible with potential crowding while simultaneously leading to the destination of the flight route without having to go around first. The method used is a comparison of Greedy algorithms and Dynamic Programs in determining the most effective route. The flight simulation was carried out using Software in The Loop (SITL) and ArduPilot Mission Planner. The results obtained are that the Dynamic Program can visit 14 locations out of 18 existing location choices, whereas with the Greedy algorithm approach, UAV can only visit 8 locations out of 18 existing location choices. The conclusion is that the Dynamic Program is able to maximize routes so that more locations are visited by UAVs and certainly better than the Greedy Algorithm.&lt;/p&gt;
Rate-Distortion Optimization-Based Learned Fractional Interpolation Filter Design for HEVC

Fractional motion compensation (FMC) has been widely adopted in modern video coding standards such as high-efficiency video coding (HEVC). The fractional pixels in FMC are generated by the interpolation filters, and the coefficients of the filters are designed using a Fourier decomposition of the discrete cosine transform (DCT) in HEVC. To overcome the compression deficiency caused by these handcrafted filters, deep learning-based interpolation filter approaches have been introduced recently. In this paper, we analyze the drawbacks of the mean square error (MSE) only loss function which is widely adopted in existing learned-based approaches, and introduce the rate-distortion optimization (RDO) theory into network training to formulate a joint loss function to overcome this issue. Then, a new training scheme is proposed for the detailed implementation of the joint loss function. Besides the calculated MSE loss, the residual will be sent into a rate estimation model which includes three stages: transform/quantization/rate modeling, and the output rate cost will be used as the second part of the loss function. Finally, we propose a novel network structure that utilizes the deformable convolution layer and the residual dense block to enhance the feature extraction and transmission ability to adapt to complex video contents. The proposed RDO-based interpolation filter (RDOIF) has been integrated into HEVC reference software HM-16.7, and extensive experiments are conducted to verify the efficiency of the proposed method. Experimental results show that the proposed RDOIF method achieves 3.1%, 2.4%, and 1.4% BD-rate reduction on average under the low-delay-P, low-delay-B, and random access configurations, respectively. When implemented in the versatile video coding (VVC) test model VTM-10.0, the proposed method can achieve 2.3%, 1.2%, and 0.5% BD-rate savings on average under the low-delay-P, low-delay-B, and random access configurations, respectively.
CyberAlumni a Cybersecurity Collaboratory

CyberAlumni is a case study of a new model for using peer to peer digital networks to make students breach ready. The CyberAlumni organization was founded in 2021 with the goals of pursuing continuing education and collaborations with academia, industry, and government to bridge the gap between curriculum and job placement. This model serves to accelerate the professional development and acquisition of top-level cybersecurity talent while recursively bolstering cybersecurity curriculum in the process. All goals were achieved within one year, leading to further investigation of applying this model at scale in conjunction with courses offered through NSA Centers of Academic Excellence.
Machine Learning Based Phishing Attacks Detection Using Multiple Datasets

Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset; the performance of the classifiers has been affected.
BALABOLKA Software to Improve the Ability to Access Electronic Learning Resources for Visual Impairment Students

This research was aimed to discover the use of Balabolka software in improving the ability to access electronic learning resources for visual impairment students who face the difficulties in accessing electronic learningresources. Assistive technology is needed to access these electronic learning resources. The quantitative approach to the Single Subject Research (SSR) method of A-B-A design was conveyed in order to collect the data. Based on the results, the baseline phase level 1 (A1) was obtained in RF subject of 54.60 percent. Mean intervention phase level (B) in RF subject was 75.64 percent. Mean baseline phase level 2 (A2) in RF subject by 81.9 percent. The findings showed that the use of Balabolka software to be helpful in supporting the ability to access electronic learning resources because Balabolka software offered audio functions to get information from computer devices.
Entangling Quantum Memories via Heralded Photonic Bell Measurement

A common way to entangle quantum memories is via photonic entanglement swaps. Each of two memories, connected by an optical channel, emits a photonic qubit entangled with itself, and the photonic qubits undergo an entanglement swap on a beamsplitter in the middle of the channel. We compare two choices of encoding of the photonic qubit: single rail and dual rail. At low channel loss the dual-rail scheme outperforms the single rail scheme. However, as expected, the high-loss rate asymptote for the dual rail scheme scales quadratically worse with loss compared with single rail. Considering the following non-idealities: imperfect mode matching at the swap, carrier-phase mismatch across the interfered photonic qubits, and detector excess noise, we evaluate the density operator of the heralded two-qubit entangled state. We calculate a lower bound on its distillable entanglement per copy, and its Fidelity (with the ideal Bell state). For both schemes, imperfect swap-visibility results in a constant-factor decrease in the rate, while excess noise results in a dropoff of distillable entanglement beyond a certain total channel loss threshold, to zero. Despite the single-rail scheme's better rate-loss scaling, it is more severely affected by excess noise. The single-rail scheme is adversely affected by stochastic carrier-phase mismatch, which does not affect the dual-rail scheme. We study entanglement distillation on the heralded noisy entangled states for both methods, and outline a suite of quantum networking studies that our work could incite.
A Rule Based Theorem Prover: an Introduction to Proofs in Secondary Schools

The introduction of automated deduction systems in secondary schools faces several bottlenecks.Beyond the problems related with the curricula and the teachers, the dissonance between the outcomes of the geometry automated theorem provers and the normal practice of conjecturing and proving in schools is a major barrier to a wider use of such tools in an educational environment.Since the early implementations of geometry automated theorem provers, applications of artificial intelligence methods, synthetic provers based on inference rules and using forward chaining reasoning are considered to be best suited for education proposes.Choosing an appropriate set of rules and an automated method that can use those rules is a major challenge.We discuss one such rule set and its implementation using the geometry deductive databases method (GDDM).The approach is tested using some chosen geometric conjectures that could be the goal of a 7th year class (≈12-year-old students).A lesson plan is presented, its goal is the introduction of formal demonstration of proving geometric theorems, trying to motivate students to that goal.