Miracle-dz/UniFAR-FTUs huggingface dataset:
TyphonML: a modeling environment to develop hybrid polystores

Designing and deploying a hybrid data persistence architecture that involves a combination of relational and NoSQL databases is a complex, technically challenging, and error-prone task. In this tool paper, we propose TyphonML, a modeling language and supporting environment, which permits modelers to specify data that need to be persisted in hybrid architectures, by abstracting over the specificities of the underlying technologies. The language enables the specification of both conceptual entities and available data layer technologies, and then how the modeled entities have to be mapped to the available database systems. TyphonML models are used to generate microservice-based infrastructures, which permit users to interact with the designed hybrid polystores at the conceptual level. In this tool paper, we show the different components of the TyphonML environment at work through a demonstration scenario.
Shell-crossing in a ΛCDM Universe

Perturbation theory is an indispensable tool for studying the cosmic large-scale structure, and establishing its limits is therefore of utmost importance. One crucial limitation of perturbation theory is shell-crossing, which is the instance when cold-dark-matter trajectories intersect for the first time. We investigate Lagrangian perturbation theory (LPT) at very high orders in the vicinity of the first shell-crossing for random initial data in a realistic three-dimensional Universe. For this, we have numerically implemented the all-order recursion relations for the matter trajectories, from which the convergence of the LPT series at shell-crossing is established. Convergence studies performed at large orders reveal the nature of the convergence-limiting singularities. These singularities are not the well-known density singularities at shell-crossing but occur at later times when LPT already ceased to provide physically meaningful results.
Game elicitation: exploring assistance in delayed-effect supply chain decision making

Socio-technical systems are creating work environments that are data-driven and real-time oriented. While algorithms can assist in this complex environment, decision making still predominantly relies on humans. An inadequate presentation of information and limited conception of decision outcomes are thereby potential sources of human error. Misperceptions of feedback and time-delayed effects, for example, contribute to the bullwhip effect observed in supply chains. In this case study, we applied the novel approach of game elicitation (GE) to explore human-centred assistance strategies for delayed-effect decision making. We designed a gaming simulation of a supply chain shortage incident to observe four logistics experts and four non-experts trying to balance the distribution system. Qualitative content analysis of thinking aloud protocols and reflective interviews yielded design suggestions for data presentation, monitoring, and learning regarding delayed-effect decisions. Findings suggest applicability in further domains of digital society, such as privacy decision making.
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards

In this paper, we consider stochastic multi-armed bandits (MABs) with heavy-tailed rewards, whose $p$-th moment is bounded by a constant $\nu_{p}$ for $1<p\leq2$. First, we propose a novel robust estimator which does not require $\nu_{p}$ as prior information, while other existing robust estimators demand prior knowledge about $\nu_{p}$. We show that an error probability of the proposed estimator decays exponentially fast. Using this estimator, we propose a perturbation-based exploration strategy and develop a generalized regret analysis scheme that provides upper and lower regret bounds by revealing the relationship between the regret and the cumulative density function of the perturbation. From the proposed analysis scheme, we obtain gap-dependent and gap-independent upper and lower regret bounds of various perturbations. We also find the optimal hyperparameters for each perturbation, which can achieve the minimax optimal regret bound with respect to total rounds. In simulation, the proposed estimator shows favorable performance compared to existing robust estimators for various $p$ values and, for MAB problems, the proposed perturbation strategy outperforms existing exploration methods.
A Novel Classification Approach for Credit Scoring based on Gaussian Mixture Models

Credit scoring is a rapidly expanding analytical technique used by banks and other financial institutions. Academic studies on credit scoring provide a range of classification techniques used to differentiate between good and bad borrowers. The main contribution of this paper is to introduce a new method for credit scoring based on Gaussian Mixture Models. Our algorithm classifies consumers into groups which are labeled as positive or negative. Labels are estimated according to the probability associated with each class. We apply our model with real world databases from Australia, Japan, and Germany. Numerical results show that not only our model's performance is comparable to others, but also its flexibility avoids over-fitting even in the absence of standard cross validation techniques. The framework developed by this paper can provide a computationally efficient and powerful tool for assessment of consumer default risk in related financial institutions.
What It Would Take to Use Mutation Testing in Industry—A Study at Facebook

Traditionally, mutation testing generates an abundance of small deviations of a program, called mutants. At industrial systems the scale and size of Facebook's, doing this is infeasible. We should not create mutants that the test suite would likely fail on or that give no actionable signal to developers. To tackle this problem, in this paper, we semi-automatically learn error-inducing patterns from a corpus of common Java coding errors and from changes that caused operational anomalies at Facebook specifically. We combine the mutations with instrumentation that measures which tests exactly visited the mutated piece of code. Results on more than 15,000 generated mutants show that more than half of the generated mutants survive Facebook's rigorous test suite of unit, integration, and system tests. Moreover, in a case study with 26 developers, all but two expressed that the mutation exposed a lack of testing in principle. As such, almost half of the 26 would actually act on the mutant presented to them by adapting an existing or creating a new test. The others did not for a variety of reasons often outside the scope of mutation testing. It remains a practical challenge how we can include such external information to increase the true actionability rate on mutants.
REMIX: Efficient Range Query for LSM-trees

LSM-tree based key-value (KV) stores organize data in a multi-level structure for high-speed writes. A range query on this structure has to seek and sort-merge data from multiple table files on the fly, which is expensive and often leads to mediocre read performance. To improve range query efficiency on LSM-trees, we introduce a space-efficient KV index data structure, named REMIX, that records a global sorted view of KV data spanning multiple table files. A range query with REMIX on multiple data files can quickly locate the target key using a binary search, and retrieve the subsequent keys in the sorted order without key comparisons. We build RemixDB, an LSM-tree based KV-store that adopts a write-efficient compaction strategy and employs REMIX for fast point and range queries. Experimental results show that REMIX can substantially improve range query performance in a write-optimized LSM-tree based KV-store.
A Model of the Deviation between the Intended and the Actual Experiences with Interactive Installations

Interactive installations engage people in different ways, many of which are different than originally intended by the designer. We present a model that illustrates the differences between the intended experience and the actual experience of people. The model is demonstrated through the analysis of one interactive installation at the Science Museum (London) and the various parameters are mapped and visualized. We suggest that the participants of interactive installations engage differently than originally intended by the designer, which can be defined here as “deviation”. There are several levels of deviation, and the proposed model will illustrate the critical interaction stages and visualize the deviations. This model offers new tools for designers and curators alike.
Mobile web browsing under memory pressure

Mobile devices have become the primary mode of Internet access. Yet, differences in mobile hardware resources, such as device memory, coupled with the rising complexity of Web pages can lead to widely different quality of experience for users. In this work, we analyze how device memory usage affects Web browsing performance. We quantify the memory footprint of popular Web pages over different mobile devices, mobile browsers, and Android versions, analyze the induced memory distribution across different browser components (e.g., JavaScript engine and compositor), investigate how performance gets impacted under memory pressure and propose optimizations to reduce the memory footprint of Web browsing. We show that these optimizations can improve performance and reduce chances of browser crashes in low memory scenarios.
Brain at Work and in Everyday Life as the Next Frontier: Grand Field Challenges for Neuroergonomics

1 ISAE-SUPAERO, Université de Toulouse, Toulouse, France, 2 School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States, Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States, Drexel Solutions Institute, Drexel University, Philadelphia, PA, United States, Department of Psychology, College of Arts and Sciences, Drexel University, Philadelphia, PA, United States, Department of Family and Community Health, University of Pennsylvania, Philadelphia, PA, United States, Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia,
LB Scalability: Achieving the Right Balance Between Being Stateful and Stateless

A high performance Layer-4 load balancer (LB) is one of the most important components of a cloud service infrastructure. Such an LB uses network and transport layer information for deciding how to distribute client requests across a group of servers. A crucial requirement for a stateful LB is per connection consistency (PCC); namely, that all the packets of the same connection will be forwarded to the same server, as long as the server is alive, even if the pool of servers or the assignment function changes. The challenge is in designing a high throughput, low latency solution that is also scalable. This paper proposes a highly scalable LB, called Prism, implemented using a programmable switch ASIC. As far as we know, Prism is the first reported stateful LB that can process millions of connections per second and hundreds of millions connections in total, while ensuring PCC. This is due to the fact that Prism forwards all the packets in hardware, even during server pool changes, while avoiding the need to maintain a hardware state per every active connection. We implemented a prototype of the proposed architecture and showed that Prism can scale to 100 million simultaneous connections, and can accommodate more than one pool update per second.
An ensemble hyper-tuned model for IoT sensors attacks and anomaly detection

Abstract The extensive promotion of Internet of Thing (IoT), provides assorted opportunities and benefits in wide aspect of our life but unfortunately, the IoT is associated with various kinds of vulnerability attacks and anomaly exploits. Security experts indicate voluminous risks enforced by the IoT devices in different aspects. So, the attacks and anomaly detection become a growing concern in this sector. Disparate attacks allied Malicious Control, Denial of Service, Malicious Operations, Scan, Data Type Probing, Wrong Setup and spying becomes the severe cause of IoT system failure. The main objective of these attacks has to steal the confidential information from the system and generates unavailability of the system for authorized users. As compulsion of IoT security, we proposed a novel ensemble hyper-tuned model that automatically and effectively detects IoT sensors attacks and anomalies. This robust model is built on the basis of feature selection and ensemble technique of Machine Learning. The virtual IoT sensor’s environment generates a dataset by Distributed Smart Space Orchestration System (DS2OS), that is used for performing the experiments of attack detection by hyper-tuned Gradient Boosting ensemble algorithm. First, the feature selection process is applied for reducing the dimensions of the dataset, which enrich the environment for attacks and anomaly detection. After that, Gradient Boosting ensemble algorithm is applied with little hyperparameter tuning for getting the best results. Our model is outperformed for detecting the attacks on IoT sensor’s environment and effectiveness of this model is measured in terms of Accuracy = 99.40%, Precision = 99%, Recall = 99%, F1-Score = 99%. whereas, the ROC-AUC curve and confusion matrix is also generated for predicting the efficiency of our ensemble model.
Concrete syntax-based find for graphical DSLs

There are services available in the most software tools we have got used to like, copy, paste, cut, find, and replace. However, the state of the art is not so good with tools of graphical languages. Even many commercial modelling tools have limited support of the find feature. We propose to add find as a service of graphical DSL tool development frameworks. This way find is available in any DSL built using the DSL tool development framework. The concrete syntax-based find has been implemented as a service of the DSL tool development framework ajoo. Two graph-based languages: UML Activity diagrams and Deterministic Finite Automata (DFA) transition diagrams are used to demonstrate usage of the concrete syntax-based find.
Phylogenetic community structure of southern African termites (Isoptera).

The processes that structure communities are still largely unknown. Therefore, we tested whether southern African termite communities show signs of environmental filtering and/or competition along a rainfall gradient in Namibia using phylogenetic information. Our results revealed a regional species pool of 11 species and we found no evidence for phylogenetic overdispersion or clustering at the local scale. Rather, our results suggest that the assembly of the studied termite communities has as strong random component on the local scale, but that species composition changes along the climatic gradient.
Four Types of Toxic People: Characterizing Online Users’ Toxicity over Time

Identifying types of online users’ toxic behavior reveals important insights from social media interactions, including whether a user becomes “radicalized” (more toxic) or “pacified” (less toxic) over time. In this research, we design two metrics to identify toxic user types: F score that captures the changes in a user’s toxicity, and G score that captures the direction of the shift taking place in the user’s toxicity pattern. We apply these metrics to a dataset of 4M user comments from Reddit by defining four toxic user types based on the toxicity scores of a user’s comments: (a) Steady Users whose toxicity scores are steady over time, (b) Fickle-Minded Users that switch between toxic and non-toxic commenting, (c) Pacified Users whose commenting becomes less toxic in time, and (d) Radicalized Users that become gradually toxic. Findings from the Reddit dataset indicate that fickle-minded users form the largest group (31.2%), followed by pacified (25.8%), radicalized (25.4%), and steadily toxic users (17.6%). The results suggest that the most typical behavior type of toxicity is switching between toxic and non-toxic commenting. This research has implications for preserving the user-friendliness of online communities by identifying continuously toxic users and users in danger of becoming radicalized (in terms of their toxic behavior), and designing interventions to mitigate these behavior types. Using the metrics we have defined, identifying these user types becomes possible. More research is needed to understand why these patterns take place and how they could be mitigated.
Spoligotype Diversity of Mycobacterium tuberculosis over Two Decades from Tiruvallur, South India

Geographically, most tuberculosis (TB) cases in 2018 were reported from India. This TB burden is compounded by MDR-TB and XDR-TB. The strategies for the management and control of TB in the community depend on an understanding of the mode of spread of the different strains of TB isolates in the community. To determine the distribution and trends of M. tb strains over the time period in the community due to treatment, we carried out the present study on changes over two decades. Design/Methods. A total of 1218 M. tb isolates (year: 2001–2018) from Tiruvallur, India, were genotyped by spoligotyping after DNA extraction and subjected to anti-TB drug susceptibility testing for the first-line anti-TB drugs. Results. On analysis with the SpolDB4 database, majority (2001–2003: 53.32% and 2015–2018: 46.3%) of the isolates belonged to East African Indian (EAI) lineage, and the orphans designated in comparison to SpolDB4 stood 33% among 2001–2003 strain collection and 46.3% among 2015–2018 strain collection. 10.2% (2001–2003) and 9.26% (2015 to 2018) of isolates were monoresistant to isoniazid (H). MDR strains were less common among EAI strains (3.2%) compared to non-EAI strains (10.32%). Conclusions. EAI is the most predominant lineage in Tiruvallur, despite the presence of highly transmissible lineages like Beijing for the last two decades. The prevalence of MDR-TB is below the national average of 2-3% among the new TB cases in the last two decades. The reason can be attributed to the well-established nature of the locally circulating strains in this region which are not associated with drug resistance.
DoMoBOT: a bot for automated and interactive domain modelling

Domain modelling transforms domain problem descriptions written in natural language (NL) into analyzable and concise domain models (class diagrams) during requirements analysis or the early stages of design in software development. Since the practice of domain modelling requires time in addition to modelling skills and experience, several approaches have been proposed to automate or semi-automate the construction of domain models from problem descriptions expressed in NL. Despite the existing work on domain model extraction, some significant challenges remain unaddressed: (i) the extracted domain models are not accurate enough to be used directly or with minor modifications in software development, (ii) existing approaches do not facilitate the tracing of the rationale behind the modelling decisions taken by the model extractor, and (iii) existing approaches do not provide interactive interfaces to update the extracted domain models. Therefore, in this paper, we introduce a domain modelling bot called DoMoBOT, explain its architecture, and implement it in the form of a web-based prototype tool. The bot automatically extracts a domain model from a problem description written in NL with an accuracy higher than existing approaches. Furthermore, the bot enables modellers to update a part of the extracted domain model and in response the bot re-configures the other parts of the domain model pro-actively. To improve the accuracy of extracted domain models, we combine the techniques of Natural Language Processing and Machine Learning. Finally, we evaluate the accuracy of the extracted domain models.
PSF-LO: Parameterized Semantic Features Based Lidar Odometry

Lidar odometry (LO) is a key technology in numerous reliable and accurate localization and mapping systems of autonomous driving. The state-of-the-art LO methods generally leverage geometric information to perform point cloud registration. Furthermore, obtaining the point cloud semantic information describing the environment more abundantly will facilitate the registration. We present a novel semantic lidar odometry method based on self-designed parameterized seman-tic features (PSFs) to achieve low-drift ego-motion estimation for autonomous vehicle in real time. We first use a convolutional neural network-based algorithm to obtain point-wise semantics from the input laser point cloud, and then use semantic labels to separate road, building, traffic sign and pole-like point cloud and fit them separately to obtain corresponding PSFs. A fast PSF-based matching enables us to refine geometric features (GeFs) registration, thereby reducing the impact of blurred submap surface on the accuracy of GeFs matching. Besides, we design an efficient instance-level method to accurately recognize and remove the dynamic objects while retaining static ones in the semantic point cloud, which are beneficial to further improve the accuracy of LO. We evaluate our method, namely PSF-LO, on the public dataset KITTI Odometry Benchmark and rank #1 among semantic lidar methods with an average translational error of 0.82% in the test dataset.
FitBirds: Designing Heart Rate Feedback for Playful and Social Physical Education

Participating in physical activity at a moderate-and-vigorous-intensity-level is recommended for teenagers to advance in health benefits. However, due to the fitness differences, the amount of physical activity to reach the recommended intensity varies among teenagers. Therefore, tailoring the physical intensity of each teenager's fitness level is meaningful in physical education. In this paper, we present FitBirds, a multiplayer fitness game to encourage teenagers in physical activity at a recommended intensity level, which is calibrated by their real-time heart rate. The FitBirds game leverages both competition and cooperation game mechanics to enhance teenagers’ playful experiences and social engagement, which could further contribute to their physically active participation in the physical education context.
Challenges & opportunities in low-code testing

Low-code is a growing development approach supported by many platforms. It fills the gap between business and IT by supporting the active involvement of non-technical domain experts, named Citizen Developer, in the application development lifecycle. Low-code introduces new concepts and characteristics. However, it is not investigated yet in academic research to point out the existing challenges and opportunities when testing low-code software. This shortage of resources motivates this research to provide an explicit definition to this area that we call it Low-Code Testing. In this paper, we initially conduct an analysis of the testing components of five commercial Low-Code Development Platforms (LCDP) to present low-code testing advancements from a business point of view. Based on the low-code principles as well as the result of our analysis, we propose a feature list for low-code testing along with possible values for them. This feature list can be used as a baseline for comparing low-code testing components and as a guideline for building new ones. Accordingly, we specify the status of the testing components of investigated LCDPs based on the proposed features. Finally, the challenges of low-code testing are introduced considering three concerns: the role of citizen developer in testing, the need for high-level test automation, and cloud testing. We provide references to the state-of-the-art to specify the difficulties and opportunities from an academic perspective. The results of this research can be used as a starting point for future research in low-code testing area.