Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing cold starts during elastic scaling of containers in kubernetes

Automatic scaling of containers is an important feature to handle fluctuating workloads. However, the delay caused by the time to bootstrap a container has an impact on applications with deadline-based Service-Level Objectives (SLOs). This delay is called the cold start problem. Many techniques have already been proposed to tackle this problem but not all techniques have been integrated and evaluated in Kubernetes, the de-facto standard in container orchestration. This paper combines and evaluates three techniques in the context of Kubernetes: (i) pre-creation of network containers, (ii) using container images that enable sharing of linked libraries in memory and (iii) extending the declarative configuration management approach of Kubernetes with imperative configuration for creating multiple application containers in parallel. A prototype of the approach is implemented and tested on a Java-based Spring Boot application where the cold start problem occurs due to various library dependencies. Our findings illustrate that the use of containers that allow for library sharing already has a large, positive impact when starting up a single container. The pre-creation of network containers in combination with imperative configuration enables the application to meet deadline-driven SLOs without a non-deterministic delay that appears in Kubernetes when multiple containers are created in parallel. We conclude that the use of container images that allow for library sharing is a must for all applications that require fast container start-ups in Kubernetes. Pre-creation of network containers when combined with imperative configuration also has a positive impact on SLO compliance during elastic scaling of containers.
Partial order based non-preemptive communication scheduling towards real-time networks-on-chip

Due to the increasing performance requirement of cyber-physical systems, many-core processors with high parallelism are gaining wide utilization, where network-on-chip (NoC) is a prevalent choice for inter-core communication. Unfortunately, the contention on NoCs introduces large timing uncertainties, which complicates the response time estimation. To address this problem, for real-time applications modeled as a directed acyclic graph (DAG), we introduce DAG-Order, a partial order based time-predictable scheduling paradigm, resulting in real-time NoCs. Specifically, DAG-Order is built upon an existing single-cycle long-range traversal (SLT) NoC that is to simplify the process of validation and verification. Then, DAG-Order is proposed based on a dynamic scheduling approach, which jointly considers communication as well as computation workloads, and matches SLT NoC. DAG-Order achieves provably bound safety by enforcing certain partial order constraints among edges/vertices that eliminate the execution-timing anomaly during the runtime phase. Finally, an effective algorithm exploring for a proper schedule order is deployed to tighten the upper bound. Experimental results demonstrate that DAG-Order performs better than state-of-the-art scheduling approaches.
A method for the automated generating of the code-coverage ensuring input test data based on the control flow analysis

This paper describes the design and implementation of a method aimed at the automation of the input test data. The method is based on the analysis of the control flow graph of the tested application and should provide an automated way for obtaining input test data that will allow us to systematically cover the program with unit tests. The analysis starts with the tested method parameters and searches the method body to find the relation between the inputs and the conditions that affect the control flow. The control flow graphs are obtained from the Java bytecode, however, the rest of the analysis is performed only on the control flow graph and thus should be platform-independent. The functionality of the method is verified on the set of Java applications.
Open Collaborative Writing

Work of all kinds increasingly takes place via networked digital environments that support diverse modes of collaboration. Previous work has investigated how collaborative writing takes place in shared and open workspaces. This study investigates how the "fork-and-pull'' model of distributed version management, now widely utilized in software development, supports coordination and public contribution in collaborative writing. We employ a case study approach to understand coordination around written artifacts in an open pull-based environment. Through interviews and archival analysis of two open text projects: a mathematics textbook on homotopy type theory (HoTT) and open source policies by 18F, a branch of federal agency, we reconstruct how text artifacts were created. We find that in both cases an intensive multi-channel communication among a small core preceded an explicit release action in the form of public communication to solicit broader contribution. Our analysis reveals a dichotomy between two modes of collaboration: a perfecting mode of crafting a public written document versus an evolution mode. In perfecting, authors used the fork-and-pull model to manage contributions refining the text to a more correct version. In evolution, authors used the fork-and-pull model to use a written artifact as a template and starting point, customizing it to their relevant scope. Based on our results, we consider how we might design systems and policies to support pull-based coordination around written artifacts and the forms it takes.
Automated vs. Human Health Coaching

Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.
Trust assessment in 32 KiB of RAM: multi-application trust-based task offloading for resource-constrained IoT nodes

There is an increasing demand for Internet of Things (IoT) systems comprised of resource-constrained sensor and actuator nodes executing increasingly complex applications, possibly simultaneously. IoT devices will not be able to execute computationally expensive tasks and will require more powerful computing nodes, called edge nodes, for such execution, in a process called computation offloading. When multiple powerful nodes are available, a selection problem arises: which edge node should a task be submitted to? This problem is even more acute when the system is subjected to attacks, such as DoS, or network perturbations such as system overload. In this paper, we present a trust model-based system architecture for computation offloading, based on behavioural evidence. The system architecture provides confidentiality, authentication and non-repudiation of messages in required scenarios and will operate within the resource constraints of embedded IoT nodes. We demonstrate the viability of the architecture with an example deployment of Beta Reputation System trust model on real hardware.
Automated Design of Deep Neural Networks

In recent years, research in applying optimization approaches in the automatic design of deep neural networks has become increasingly popular. Although various approaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this hot research topic. In this article, we propose a unified way to describe the various optimization algorithms that focus on common and important search components of optimization algorithms: representation, objective function, constraints, initial solution(s), and variation operators. In addition to large-scale search space, the problem is characterized by its variable mixed design space, it is very expensive, and it has multiple blackbox objective functions. Hence, this unified methodology has been extended to advanced optimization approaches, such as surrogate-based, multi-objective, and parallel optimization.
Utilizing textual reviews in latent factor models for recommender systems

Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modelling technique (i.e., Latent Factor Model) with a topic modelling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.
OntoConnect: unsupervised ontology alignment with recursive neural network

Ontology alignment is performed to combine or integrate multiple knowledge bases at the elemental and structural levels. The current state-of-the-art systems use many different approaches to match semantics, syntax, and terminologies of different ontological entities. However, most of the ontology alignment systems depend on domain knowledge, which makes the alignment process domain-specific. To address this challenge, we aim at developing an ontology alignment approach that is independent of domain knowledge. To achieve this goal, an ontology alignment approach is proposed which exploits an unsupervised learning method using a recursive neural network to align classes between different ontologies. In particular, the proposed approach extracts structural information of the classes in ontology to train the unsupervised model. The proposed approach is tested against a reference gold copy of the Anatomy data set in the Ontology Alignment Evaluation Initiative. Our evaluation results show that the proposed unsupervised neural network approach using the meta information of ontological classes yields satisfactory results with a precision of 95.66% and F-measure of 80.26% for a similarity threshold of 0.96 with the 100-dimension input vector. Increasing the input vector dimension to 300 results in improved precision of 97.71% and F-measure of 80.38% with a 0.96 threshold. The significance of the proposed approach is that it can be used for ontology alignment independent of domain expertise and without the need for human intervention.
Cyber-aggression, Cyberbullying, and Cyber-grooming

Cyber-aggression, cyberbullying, and cyber-grooming are distinctive and similar phenomena that represent the objectionable content appearing on online social media. Timely detection of the objectionable content is very important for its prevention and reduction. This article explores and spotlights diversity of definitions of cyber-aggression, cyberbulling, and cyber-grooming; analyzes current categorization systems and taxonomies; identifies the targets, target categories, and subcategories of the subjects of the objectionable content research; analyzes the ambiguity of the linguistic terms in the domain; reviews present databases gathered for researching the field; explores types of features used for modeling systems for automatic detection; and examines methods for automatic detection and/or prediction of the objectionable content. The results point to directions of system development for tracing transformations of objectionable content over time on different online social platforms.
Cost-effective identification of on-topic search queries using multi-armed bandits

Identifying the topic of a search query is a challenging problem, for which a solution would be valuable in diverse situations. In this work, we formulate the problem as a ranking task where various rankers order queries in terms of likelihood of being related to a specific topic of interest. In doing so, an explore-exploit trade-off is established whereby exploiting effective rankers may result in more on-topic queries being discovered, but exploring weaker rankers might also offer value for the overall judgement process. We show empirically that multi-armed bandit algorithms can utilise signals from divergent query rankers, resulting in improved performance in extracting on-topic queries. In particular we find Bayesian non-stationary approaches to offer high utility. We explain why the results offer promise for several use-cases both within the field of information retrieval and for data-driven science, generally.
Supervised learning over test executions as a test oracle

The challenge of automatically determining the correctness of test executions is referred to as the test oracle problem and is a key remaining issue for automated testing. The paper aims at solving the test oracle problem in a scalable and accurate way. To achieve this, we use supervised learning over test execution traces. We label a small fraction of the execution traces with their verdict of pass or fail. We use the labelled traces to train a neural network (NN) model to learn to distinguish runtime patterns for passing versus failing executions for a given program. We evaluate our approach using case studies from different application domains - 1. Module from Ethereum Blockchain, 2. Module from PyTorch deep learning framework, 3. Microsoft SEAL encryption library components and 4. Sed stream editor. We found the classification models for all subject programs resulted in high precision, recall and specificity, averaging to 89%, 88% and 92% respectively, while only training with an average 15% of the total traces. Our experiments show that the proposed NN model is promising as a test oracle and is able to learn runtime patterns to distinguish test executions for systems and tests from different application domains.
Reducing tail latency of LSM-tree based key-value store via limited compaction

Key-value stores based on log-structured merge-tree (LSM-tree), e.g. LevelDB by Google and RocksDB by Facebook, have been widely used in a lot of cyber-physical systems scenarios to provide flexible data access and high performance. Compaction is a necessary operation to delete some invalid data, reduce the data size and improve the efficiency of reading and writing. However, compaction would stall write requests, which significantly degrades system performance. This paper studies the effect of compaction on tail latency that shows frequent latency spikes. These spikes would cause poor user experience and have already concerned by industrial researchers. Furthermore, the number of involved SSTables proportionally decides the height of latency spikes. We propose the limited compaction to only allow a part of SSTables involved in the compaction. We implement the limited compaction with a basic method to choose the SSTables randomly and a selective method to choose the SSTables according to their overlapped ranges with the next level. Experimental results on LevelDB with comprehensive benchmarks have shown that the proposed methods can effectively reduce the tail latency, while inducing an acceptable write amplification.
A Survey on Distributed Graph Pattern Matching in Massive Graphs

Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.
Dignity, Autonomy, and Style of Company

Research into companion robots for older adults, including those who are socially isolated and lonely, continues to grow. Although some insight into older adults' preferences for various robotic types and functionality is emerging, we lack research examining how these robots fulfil or challenge a range of values and aspirations individuals have in later life. This study examines the attitudes and perspectives of 16 older adults (aged 65+) living independently but alone in their own homes, who were interviewed and shown videos depicting three distinctive companion robots: a talking assistant; a roving toylike vehicle; and a robotic dog. This approach illuminated values, preferences, and needs amongst older people that are vital for understanding the potential of companion robots. In comparing the robots, participants expressed concerns about the impact of different companion robots on their abilities and skills, their sense of autonomy and control over their lives, and the maintenance of several kinds of dignity. These results inform user-centered design and use of companion robots for older people living alone and independently.
Unsupervised behavioural mining and clustering for malware family identification

More accurate and advanced detection and classification of malware requires exploiting program behaviour and not merely syntactic or static features. One approach is to use system call dependency graphs (SCDGs) that represent the program behaviour by interactions with the system, and the relations between these interactions. These SCDGs have been used with supervised learning techniques to very accurately detect and classify malware. This works considers the unsupervised learning challenge of mining for common clusters of behaviour without a priori knowledge. This allows for clustering of similar programs by behaviour, that can then be used for either classification or further analysis.
Formation of Social Ties Influences Food Choice

Nutrition is a key determinant of long-term health, and social influence has long been theorized to be a key determinant of nutrition. It has been difficult to quantify the postulated role of social influence on nutrition using traditional methods such as surveys, due to the typically small scale and short duration of studies. To overcome these limitations, we leverage a novel source of data: logs of 38 million food purchases made over an 8-year period on the Ecole Polytechnique Federale de Lausanne (EPFL) university campus, linked to anonymized individuals via the smartcards used to make on-campus purchases. In a longitudinal observational study, we ask: How is a person's food choice affected by eating with someone else whose own food choice is healthy vs. unhealthy? To estimate causal effects from the passively observed log data, we control confounds in a matched quasi-experimental design: we identify focal users who at first do not have any regular eating partners but then start eating with a fixed partner regularly, and we match focal users into comparison pairs such that paired users are nearly identical with respect to covariates measured before acquiring the partner, where the two focal users' new eating partners diverge in the healthiness of their respective food choice. A difference-in-differences analysis of the paired data yields clear evidence of social influence: focal users acquiring a healthy-eating partner change their habits significantly more toward healthy foods than focal users acquiring an unhealthy-eating partner. We further identify foods whose purchase frequency is impacted significantly by the eating partner's healthiness of food choice. Beyond the main results, the work demonstrates the utility of passively sensed food purchase logs for deriving insights, with the potential of informing the design of public health interventions and food offerings, especially on university campuses.
Automated Text Simplification

Text simplification (TS) reduces the complexity of the text to improve its readability and understandability, while possibly retaining its original information content. Over time, TS has become an essential tool in helping those with low literacy levels, non-native learners, and those struggling with various types of reading comprehension problems. In addition, it is used in a preprocessing stage to enhance other NLP tasks. This survey presents an extensive study of current research studies in the field of TS, as well as covering resources, corpora, and evaluation methods that have been used in those studies.
A Practical Tutorial for Decision Tree Induction

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.
Data Protection in AI Services

Advances in artificial intelligence (AI) have shaped today’s user services, enabling enhanced personalization and better support. As such AI-based services inevitably require user data, the resulting privacy implications are de facto the unacceptable face of this technology. In this article, we categorize and survey the cutting-edge research on privacy and data protection in the context of personalized AI services. We further review the different protection approaches at three different levels, namely, the management, system, and AI levels—showing that (i) not all of them meet our identified requirements of evolving AI services and that (ii) many challenges are addressed separately or fragmentarily by different research communities. Finally, we highlight open research challenges and future directions in data protection research, especially that comprehensive protection requires more interdisciplinary research and a combination of approaches at different levels.