Miracle-dz/UniFAR-FTUs huggingface dataset:
Neighborhood-based Strategies for Widening of the Greedy Algorithm of the Set Cover Problem

We live in the age of ever-increasing parallel computing resources. For a decade there has been intense research into parallel data mining algorithms. Most of this research is focused on improving the running time of existing algorithms. In contrast our focus is the improvement of the solution quality, or model accuracy. We are looking for "smart" strategies to invest parallel compute resources in order to achieve a better exploration of the search space by exploring several solutions in parallel, referred to as Widening. In this paper, we demonstrate the effect of neighborhood-based Widening with different types of neighborhoods for the greedy algorithm of the set cover problem.
Scalable Analytics on Fast Data

Today’s streaming applications demand increasingly high event throughput rates and are often subject to strict latency constraints. To allow for more complex workloads, such as window-based aggregations, streaming systems need to support stateful event processing. This introduces new challenges for streaming engines as the state needs to be maintained in a consistent and durable manner and simultaneously accessed by complex queries for real-time analytics. Modern streaming systems, such as Apache Flink, do not allow for efficiently exposing the state to analytical queries. Thus, data engineers are forced to keep the state in external data stores, which significantly increases the latencies until events become visible to analytical queries. Proprietary solutions have been created to meet data freshness constraints. These solutions are expensive, error-prone, and difficult to maintain. Main-memory database systems, such as HyPer, achieve extremely low query response times while maintaining high update rates, which makes them well-suited for analytical streaming workloads. In this article, we explore extensions to database systems to match the performance and usability of streaming systems.
CLONE: a Promising System for the Remote Monitoring of Alzheimer's Patients: An Experimentation with a Wearable Device in a Village for Alzheimer's Care

Alzheimer's disease is a form of dementia that affects a large segment of the older population and, due to aging, its prevalence will substantially increase in the next decades. This paper describes an experiment of application of remote monitoring technologies for the collection of data to detect the onset of crises in patients affected by Alzheimer's in an Italian village dedicated to the treatment of the Alzheimer's disease. Furthermore, the paper details the design concept of Eclipse, a smart wearable that compensates for the current lack of Alzheimer's specific wearables on the market.
The Neural Hype and Comparisons Against Weak Baselines

Recently, the machine learning community paused in a moment of self-reflection. In a widelydiscussed paper at ICLR 2018, Sculley et al. [13] wrote: "We observe that the rate of empirical advancement may not have been matched by consistent increase in the level of empirical rigor across the field as a whole. " Their primary complaint is the development of a "research and publication culture that emphasizes wins" (emphasis in original), which typically means "demonstrating that a new method beats previous methods on a given task or benchmark". An apt description might be "leaderboard chasing"-and for many vision and NLP tasks, this isn't a metaphor. There are literally centralized leaderboards1 that track incremental progress, down to the fifth decimal point, some persisting over years, accumulating dozens of entries. Sculley et al. remind us that "the goal of science is not wins, but knowledge". The structure of the scientific enterprise today (pressure to publish, pace of progress, etc.) means that "winning" and "doing good science" are often not fully aligned. To wit, they cite a number of papers showing that recent advances in neural networks could very well be attributed to mundane issues like better hyperparameter optimization. Many results can't be reproduced, and some observed improvements might just be noise.
A Refined Mean Field Approximation for Synchronous Population Processes

Mean field approximation is a popular method to study the behaviour of stochastic models composed of a large number of interacting objects. When the objects are asynchronous, the mean field approximation of a population model can be expressed as an ordinary differential equation. When the objects are synchronous the mean field approximation is a discrete time dynamical system. In this paper, we focus on the latter. We show that, similarly to the asynchronous case, the mean field approximation of a synchronous population can be refined by a term in 1/N. Our result holds for finite time-horizon and steady-state. We provide two examples that illustrate the approach and its limit.
The Effects of Adding Non-Compulsory Exercises to an Online Learning Tool on Student Performance and Code Copying

This study analyzes the impact of adding a review exercises module to an online tool used in a software engineering degree program. The objective of the module is to promote students’ self-learning effort to improve their performance. We also intend to determine if this new feature has any effect on the amount of code copies detected in lab sessions when using the same online tool. Two groups of students were compared quantitatively: the first group used the tool exclusively during lab sessions, whereas the second group had the option of employing the tool's new module to enhance their study. The tool allows us to collect interesting data related to the focus of this research: supplementary work completed voluntarily by students and the percentage of students copying others’ code during compulsory lab sessions. The results show that the students in the second group achieved better academic results and copied less in lab sessions. In the second group, the students who invested more effort in doing revision exercises and copied less in lab sessions obtained better results; and, interestingly, the effort invested in completing review exercises did not seem to compensate for the learning effort avoided by copying others’ exercises during lab sessions. The results show the advantages of a tool used with a dual orientation: compulsory and voluntary. Mandatory usage in lab sessions establishes some milestones that, eventually, act as an incentive fostering learning, while voluntary use reinforces students’ perception of the tool's usefulness in terms of learning.
Multi-armed Bandit with Additional Observations

We study multi-armed bandit (MAB) problems with additional observations, where in each round, the decision maker selects an arm to play and can also observe rewards of additional arms (within a given budget) by paying certain costs. We propose algorithms that are asymptotic-optimal and order-optimal in their regrets under the settings of stochastic and adversarial rewards, respectively.
Convex Prophet Inequalities

We introduce a new class of prophet inequalities-convex prophet inequalities-where a gambler observes a sequence of convex cost functions ci(xi) and is required to assign some fraction 0 ≤ xi ≤ 1 to each, such that the sum of assigned values is exactly 1. The goal of the gambler is to minimize the sum of the costs. We provide an optimal algorithm for this problem, a dynamic program, and show that it can be implemented in polynomial time when the cost functions are polynomial. We also precisely characterize the competitive ratio of the optimal algorithm in the case where the gambler has an outside option and there are polynomial costs, showing that it grows as Θ(np-1/l), where n is the number of stages, p is the degree of the polynomial costs and the coefficients of the cost functions are bounded by [l, u].
Securing Objects With K-wheel Locks

Locks are effective control against intruders who may want to gain access to protected objects such as houses, cars, boxes, briefcases, and the likes. Control can be physical or procedural. The physical control still involves the use of locks, some of which have wheels that are calibrated from 0 to (n-1), where n is the number of digits on the kth wheel with k as the number of wheels. A k-wheel briefcase, with each wheel having n integer number of digits for example, will lead to nk keyspace, in which one of the key combination is the right one for opening the object. This paper identified the flaws associated with the design of locks and then propose a method based on the cryptanalysis of the keyspace to generate the key combination for opening an object with k-wheel locks and at the same time recommended further actions that can forestalled masquerades from opening objects having k-wheel locks by increasing the keyspace. In addition, the use of irregular wheels with varied calibrated digits and letters was recommended to further secure locks with k-wheel dials.
Machine Learning Approach for Pre-Eclampsia Risk Factors Association

The preeclampsia/eclampsia syndrome is a multisystem disorder that usually includes cardiovascular changes, hematologic abnormalities, hepatic and renal impairment, and neurologic or cerebral manifestations. Preeclampsia (PE) is a clinical syndrome that afflicts 3--5% of pregnancies and it is a leading cause of maternal mortality, especially in developing countries. To understand in greater depth the preeclampsia/eclampsia syndrome, we applied some well-known Machine Learning (ML) techniques. ML has been successfully applied to medical research to improve the diagnosis and the prevention of complex diseases and syndromes. In our contribution, we have created a supervised model to predict if a patient suffers the disease. This model has been optimized by selecting the best features and by optimizing the threshold when predicting a class. We used these techniques to point out the most related features of the patients to the disease. Finally, we used interpretability techniques to extract and visualize through a decision tree the most relevant associations of the disease with the patients' features.
Cloud Computing Classroom Acceptance Model in Thailand Higher Education's Institutes: A Conceptual Framework

In the era of digitization, higher education institutes and universities are strengthening to provide a new way of virtual technologies and electronics for learning and teaching. Cloud computing technology is currently considered to become an integral part of the educational experience and will have a significant impact on the learning environment. This innovative technology has been applied to cloud-computing learning system and has facilitated the development of cloud computing for classrooms. With the development of the cloud computing classrooms, improving students' attitudes is quite significant to adopt and use cloud computing classroom, however, student behavioral intention using cloud computing remains unclear among higher education institutions in Thailand. Limited studies integrating and analyzing factors influencing user acceptance of cloud computing classroom in higher education are investigated for guiding in students' adoption decision. Thus, this study review significant factors that could affect user acceptance of cloud computing classroom and develop a conceptual framework of cloud computing classroom acceptance model in higher education's institutions in Thailand. A conceptual framework developed is theoretically based on Technology Acceptance Model (TAM) and Service and System Quality (SSQ). This framework provides a comprehensive understanding of the potential factors influencing the students' behavioral intention and the use of cloud computing classrooms.
Source code authorship approaches natural language processing

This paper proposed method for source code authorship attribution using modern natural language processing methods. Our method based on text embedding with convolutional recurrent neural network reaches 94.5% accuracy within 500 authors in one dataset, which outperformed many state of the art models for authorship attribution. Our approach is dealing with source code as with natural language texts, so it is potentially programming language independent with more potential of future improving.
ECI-Cache: A High-Endurance and Cost-Efficient I/O Caching Scheme for Virtualized Platforms

In recent years, high interest in using Virtual Machines (VMs) in data centers and cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to providing a high-performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualized platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache size to provide the required performance level for each VM, while neglecting other important parameters such as cache write policy and request type, which can adversely affect both performance-per-cost and endurance.
Using Machine Learning Classifiers to Identify the Critical Proteins in Down Syndrome

Pharmacotherapies of intellectual disability (ID) are largely unknown as the abnormalities at the complex molecular level which causes ID are difficult to understand. Down syndrome (DS) which is the prevalent cause of ID and caused by an extra copy of the human chromosome21 (Hsa21) has been investigated on protein levels by using the Ts65Dn mouse model of DS which are orthologs of %50 of Hsa21 classical protein coding genes. Recent works have applied the classification methods to understand critical factors in DS as it is believed that the problem was naturally related to classification problem since the determination of proteins discriminatory between classes of mice was required. In this study, we apply forward feature selection method to identify correlated proteins and their interactions in DS. After identification, we report supervised learning model of expression levels of selected proteins in order to understand the critical proteins for diagnosing and explaining DS. The proposed technique depicts optimum classification results achieved by optimizing parameters with grid search. When compared with the former work, our classification results give higher accuracy.
Looking at Alzheimer's Disease Using Enhanced Algorithm for Feature Collection

Machine learning technology has taken substantial leaps in the past few years. From the rise of voice recognition as an interface to interact with our computers to self-organizing photo albums and self-driving cars. Neural networks and deep learning contributed significantly to drive this revolution. Biomedicine is one of the research areas that have yet to fully embrace the possibilities of deep learning. The ability to learn hierarchical features makes deep learning models highly applicable to biomedicine and researchers have started to notice this. Deeper understanding can be enabled by the hierarchical features that marked in deep models. This paper explores the use of neural networks and deep learning models for the qualitative assessment of biomedical datasets using a non-iterative, data feature collection algorithm to preserve original features and provide qualitative analysis on their importance. This algorithm is employed in numerous areas including Pima Indian diabetes and children tumor detection. The learning coefficients found to contain clinically significant features. When combined, in a hierarchical way, these features reveal useful insights for the evaluation of treatment effectivity.
Speeding up Iterative Polyhedral Schedule Optimization with Surrogate Performance Models

Iterative program optimization is known to be able to adapt more easily to particular programs and target hardware than model-based approaches. An approach is to generate random program transformations and evaluate their profitability by applying them and benchmarking the transformed program on the target hardware. This procedure’s large computational effort impairs its practicality tremendously, though. To address this limitation, we pursue the guidance of a genetic algorithm for program optimization via feedback from surrogate performance models. We train the models on program transformations that were evaluated during previous iterative optimizations. Our representation of programs and program transformations refers to the polyhedron model. The representation is particularly meaningful for an optimization of loop programs that profit a from coarse-grained parallelization for execution on modern multicore-CPUs. Our evaluation reveals that surrogate performance models can be used to speed up the optimization of loop programs. We demonstrate that we can reduce the benchmarking effort required for an iterative optimization and degrade the resulting speedups by an average of 15%.
Bandwidth and Locality Aware Task-stealing for Manycore Architectures with Bandwidth-Asymmetric Memory

Parallel computers now start to adopt Bandwidth-Asymmetric Memory architecture that consists of traditional DRAM memory and new High Bandwidth Memory (HBM) for high memory bandwidth. However, existing task schedulers suffer from low bandwidth usage and poor data locality problems in bandwidth-asymmetric memory architectures. To solve the two problems, we propose a Bandwidth and Locality Aware Task-stealing (BATS) system, which consists of an HBM-aware data allocator, a bandwidth-aware traffic balancer, and a hierarchical task-stealing scheduler. Leveraging compile-time code transformation and run-time data distribution, the data allocator enables HBM usage automatically without user interference. According to data access hotness, the traffic balancer migrates data to balance memory traffic across memory nodes proportional to their bandwidth. The hierarchical scheduler improves data locality at runtime without a priori program knowledge. Experiments on an Intel Knights Landing server that adopts bandwidth-asymmetric memory show that BATS reduces the execution time of memory-bound programs up to 83.5% compared with traditional task-stealing schedulers.
An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.
Tools for Advanced Time Series Analytics: Enabling the Future

It is widely anticipated that time series data will dominate our future. From autonomous driving to industrial automation, the inevitable rise of the Internet of Things (IoT) has already exposed us to billions of data sources generating time-varying data at unprecedented complexity and scale. The opportunity to utilize this data reliably and efficiently to the benefit of the society critically depends on equipping our computing systems and their users with the right tools and capabilities. A key capability that lies at the core of managing time series data is the identification of patterns that signify “interesting" phenomena, such as anomalies. Anomalies are patterns in data that do not conform to expected (or, normal) behavior [1]. Detecting anomalies before, after, or as they occur not only finds use in many mission-critical domains, but also empowers systems and users with the ability to cope with large data volumes by guiding attention and resources to information that matters the most. Although anomaly detection has been actively studied for many decades, there are several challenges to be overcome before it can become practical in real-world deployments of time series applications, including the following: Anomaly detection is a highly domain-specific problem. What constitutes an anomaly changes greatly from one application domain to another. This bears the need to develop, compare, and choose from a variety of models and algorithms. Furthermore, domains may have different preferences for accuracy or performance tradeoffs (e.g., tolerance to different types of classification errors). Time series anomalies can be complex. They typically fall under the category of collective and contextual anomalies (as opposed to simpler, point anomalies) [1]. These anomalies may expose themselves at different time granularities or aggregations. Furthermore, anomalous patterns may vary over time due to temporal and dynamic nature of the data. Some time series datasets may even consist of multiple, potentially correlated attributes, requiring analyzing anomalies in a multi-variate fashion. Training data can be unavailable or noisy. In most domains, anomalies are relatively rare or unique events, making it challenging to collect anomalous datasets. Often, training datasets capture a small number of anomalous patterns, which may or may not be properly labeled. In IoT, presence of noisy sensor data makes this