Miracle-dz/UniFAR-FTUs huggingface dataset:
Similarity Group-By operators for multi-dimensional relational data

The SQL group-by operator plays an important role in summarizing and aggregating large datasets in a data analytics stack. The Similarity SQL-based Group-By operator (SGB, for short) extends the semantics of the standard SQL Group-by by grouping data with similar but not necessarily equal values. While existing similarity-based grouping operators efficiently realize these approximate semantics, they primarily focus on one-dimensional attributes and treat multi-dimensional attributes independently. However, correlated attributes, such as in spatial data, are processed independently, and hence, groups in the multi-dimensional space are not detected properly. To address this problem, we introduce two new SGB operators for multi-dimensional data. The first operator is the clique (or distance-to-all) SGB, where all the tuples in a group are within some distance from each other. The second operator is the distance-to-any SGB, where a tuple belongs to a group if the tuple is within some distance from any other tuple in the group. Since a tuple may satisfy the membership criterion of multiple groups, we introduce three different semantics to deal with such a case: (i) eliminate the tuple, (ii) put the tuple in any one group, and (iii) create a new group for this tuple. We implement and test the new SGB operators and their algorithms inside PostgreSQL. The overhead introduced by these operators proves to be minimal and the execution times are comparable to those of the standard Group-by. The experimental study, based on TPC-H and a social check-in data, demonstrates that the proposed algorithms can achieve up to three orders of magnitude enhancement in performance over baseline methods developed to solve the same problem.
Efficient Sequence Embedding For SARS-CoV-2 Variants Classification

Kernel-based methods, such as Support Vector Machines (SVM), have demonstrated their utility in various machine learning (ML) tasks, including sequence classification. However, these methods face two primary challenges:(i) the computational complexity associated with kernel computation, which involves an exponential time requirement for dot product calculation, and (ii) the scalability issue of storing the large n × n matrix in memory when the number of data points(n) becomes too large. Although approximate methods can address the computational complexity problem, scalability remains a concern for conventional kernel methods. This paper presents a novel and efficient embedding method that overcomes both the computational and scalability challenges inherent in kernel methods. To address the computational challenge, our approach involves extracting the k-mers/nGrams (consecutive character substrings) from a given biological sequence, computing a sketch of the sequence, and performing dot product calculations using the sketch. By avoiding the need to compute the entire spectrum (frequency count) and operating with low-dimensional vectors (sketches) for sequences instead of the memory-intensive n × n matrix or full-length spectrum, our method can be readily scaled to handle a large number of sequences, effectively resolving the scalability problem. Furthermore, conventional kernel methods often rely on limited algorithms (e.g., kernel SVM) for underlying ML tasks. In contrast, our proposed fast and alignment-free spectrum method can serve as input for various distance-based (e.g., k-nearest neighbors) and non-distance-based (e.g., decision tree) ML methods used in classification and clustering tasks. We achieve superior prediction for coronavirus spike/Peplomer using our method on real biological sequences excluding full genomes. Moreover, our proposed method outperforms several state-of-the-art embedding and kernel methods in terms of both predictive performance and computational runtime.
AUTHENTICATED SUBGRAPH SIMILARITY SEARCH IN OUTSOURCED GRAPH DATABASES

Data mining is a computer process that combines techniques from artificial intelligence, machine learning, statistics, along with database systems to find patterns in big data sets. The process's objective is to take the information from the data collection and organise it so that it may be used in other ways. It includes features of database and data administration, pre-processing of the data, model and inference considerations, interestingness measures, complexity concerns, post-processing of the structures that are identified, visualisation
Trophic interactions and population growth rates: describing patterns and identifying mechanisms.

While the concept of population growth rate has been of central importance in the development of the theory of population dynamics, few empirical studies consider the intrinsic growth rate in detail, let alone how it may vary within and between populations of the same species. In an attempt to link theory with data we take two approaches. First, we address the question 'what growth rate patterns does theory predict we should see in time-series? ' The models make a number of predictions, which in general are supported by a comparative study between time-series of harvesting data from 352 red grouse populations. Variations in growth rate between grouse populations were associated with factors that reflected the quality and availability of the main food plant of the grouse. However, while these results support predictions from theory, they provide no clear insight into the mechanisms influencing reductions in population growth rate and regulation. In the second part of the paper, we consider the results of experiments, first at the individual level and then at the population level, to identify the important mechanisms influencing changes in individual productivity and population growth rate. The parasitic nematode Trichostrongylus tenuis is found to have an important influence on productivity, and when incorporated into models with their patterns of distribution between individuals has a destabilizing effect and generates negative growth rates. The hypothesis that negative growth rates at the population level were caused by parasites was demonstrated by a replicated population level experiment. With a sound and tested model framework we then explore the interaction with other natural enemies and show that in general they tend to stabilize variations in growth rate. Interestingly, the models show selective predators that remove heavily infected individuals can release the grouse from parasite-induced regulation and allow equilibrium populations to rise. By contrast, a tick-borne virus that killed chicks simply leads to a reduction in the equilibrium. When humans take grouse they do not appear to stabilize populations and this may be because many of the infective stages are available for infection before harvesting commences. In our opinion, an understanding of growth rates and population dynamics is best achieved through a mechanistic approach that includes a sound experimental approach with the development of models. Models can be tested further to explore how the community of predators and others interact with their prey.
A Blind Roll-off Factor Estimation Method for Wireless Signal Identification

In blind signal detection, exact roll-off factor estimation needs to be performed for matched filtering, but the performance of the present methods is far from satisfactory. Considering this, this paper proposes a new method for the roll-off factor estimation, and the root mean square error (RMSE)criterion is employed to measure its performance advantage compared with the previous estimation methods. To be more specific, the envelope of the impulse response of the raised cosine-roll-off filter is derived with the help of the inverse Fourier transforms, and the roll-off factor is estimated by the ratio of the minimum to the maximum of the envelope. Simulation results show that the RMSE performance of the proposed method can be improved by 78% compared with that of the existing method when the SNR is 15dB. Moreover, the RMSE performance can be found to decrease dramatically with the growth of the roll-off factor when the factor is larger than 0.6.
From compositional Petri Net modeling to macro and micro simulation by means of Stochastic Simulation and Agent-Based models

Computational modeling has become a widespread approach for studying real-world phenomena by using different modeling perspectives, in particular, the microscopic point of view concentrates on the behavior of the single components and their interactions from which the global system evolution emerges, while the macroscopic point of view represents the system’s overall behavior abstracting as much as possible from that of the single components. The preferred point of view depends on the effort required to develop the model, on the detail level of the available information about the system to be modeled, and on the type of measures that are of interest to the modeler; each point of view may lead to a different modeling language and simulation paradigm. An approach adequate for the microscopic point of view is Agent-Based Modeling and Simulation, which has gained popularity in the last few decades but lacks a formal definition common to the different tools supporting it. This may lead to modeling mistakes and wrong interpretation of the results, especially when comparing models of the same system developed according to different points of view. The aim of the work described in this paper is to provide a common compositional modeling language from which both a macro and a micro simulation model can be automatically derived: these models are coherent by construction and may be studied through different simulation approaches and tools. A framework is thus proposed in which a model can be composed using a Petri Net formalism and then studied through both an Agent-Based Simulation and a classical Stochastic Simulation Algorithm, depending on the study goal.
Ieee Transactions on Communications, Accepted for Publication on the Performance of Ofdm-based Amplify-and-forward Relay Networks in the Presence of Phase Noise

—We investigate the performance of orthogonal frequency division multiplexing (OFDM)-based dual-hop amplify-and-forward (AF) relay networks in the presence of phase noise (PHN). We show that the use of an AF relay may not be beneficial compared to a direct transmission in the presence of PHN. Using outage probability analysis, an upper bound on the allowable PHN level is derived which ensures that the dual-hop outperforms the direct transmission. To improve dual-hop transmission performance in the presence of PHN, we propose a reduced-complexity joint channel and PHN estimator using full-pilot OFDM symbols for AF relay transmission. The proposed approach achieves a lower mean squared error compared to the conventional channel estimator. In addition, we derive a joint data detection and PHN estimation scheme for comb-type data OFDM symbols by modifying the maximum ratio combining metric to account for the effect of PHN at the destination node.
Structural and Functional Changes in a Lake Bacterial Community Exposed to Multiple Stress Regimes

Bacterial community resilience and resistance may play an important role in a world affected by climate change and increasing environmental disturbances. In this study we examined the resilience of a bacterial lake community from Lake Ekoln, Sweden, under increased environmental stress. The community was exposed to various stress regimes of altered temperature and salinity to answer the questions if and how higher amounts of stress influence the bacterial community in terms of its community structure and functionality. We measured bacterial productivity, ecto-enzyme activities and changes of fluorescent dissolved organic matter to evaluate the community's functional responses under stress and used terminal-restriction length polymorphism (TRFLP) to assess the community's structural characteristics over time. Although bacterial community composition (BCC) changed constantly under changing environmental conditions, community functionality remained high and the community performed well under the various stress regimes. Our results suggest that the Lake Ekoln bacterial community is highly resilient considering the investigated stresses, and its adaptive capacity is high due to the community's inherent adaptability and redundancy.
A Performance Study of the 2D Ising Model on GPUs

The simulation of the two-dimensional Ising model is used as a benchmark to show the computational capabilities of Graphic Processing Units (GPUs). The rich programming environment now available on GPUs and flexible hardware capabilities allowed us to quickly experiment with several implementation ideas: a simple stencil-based algorithm, recasting the stencil operations into matrix multiplies to take advantage of Tensor Cores available on NVIDIA GPUs, and a highly optimized multi-spin coding approach. Using the managed memory API available in CUDA allows for simple and efficient distribution of these implementations across a multi-GPU NVIDIA DGX-2 server. We show that even a basic GPU implementation can outperform current results published on TPUs and that the optimized multi-GPU implementation can simulate very large lattices faster than custom FPGA solutions.
Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach

—The rise of the Internet of Medical Things introduces the healthcare ecosystem in a new digital era with multiple beneﬁts, such as remote medical assistance, real-time monitoring, and pervasive control. However, despite the valuable healthcare services, this progression raises signiﬁcant cybersecurity and privacy concerns. In this article, we focus our attention on the IEC 60 870-5-104 protocol, which is widely adopted in industrial healthcare systems. First, we investigate and assess the severity of the IEC 60 870-5-104 cyberattacks by providing a quantitative threat model, which relies on Attack Defence Trees and Common Vulnerability Scoring System v3.1. Next, we introduce an intrusion detection and prevention system (IDPS), which is capable of discriminating and mitigating automatically the IEC 60 870-5-104 cyberattacks. The proposed IDPS takes full advantage of the machine learning (ML) and software deﬁned networking (SDN) technologies. ML is used to detect the IEC 60 870-5-104 cyberattacks, utilizing 1) Transmission Control Protocol/Internet Protocol network ﬂow statistics and 2) IEC 60 870-5-104 payload ﬂow statistics.
A protocol of systematic review and meta-analysis of narrow band imaging endoscopy in detection of early gastric cancer

Abstract Background: Although previous studies have utilized narrow band imaging endoscopy diagnosis (NBIED) in detection of patients with early gastric cancer (EGC), there are still inconsistent results. Thus, this study will explore the accuracy of NBIED in detection of patients with EGC. Methods: We propose to perform literature search of potential studies investigating the accuracy of NBIED in detection of patients with EGC in MEDLINE, EMBASE, Cochrane Library, Web of Science, WANGFANG, VIP database and China National Knowledge Infrastructure from the beginning of each database to January 31, 2020 without restrictions to language and publication time. Two authors will independently scrutinize these databases to identify studies that satisfy all predefined eligibility criteria. We will check study quality and analyze outcome data using Quality Assessment of Diagnostic Accuracy Studies tool, and RevMan 5.3 software respectively. Results: We anticipate the results of this study will afford additional insight into the appraising of the accuracy of NBIED in patients with EGC. Conclusion: The findings of this study will be useful informing diagnostic decisions for the diagnosis of patients with EGC. PROSPERO registration number: PROSPERO CRD42020171053.
Software Metrics : A Rigorous and Practical Approach

From the Publisher: The Second Edition of Software Metrics provides an up-to-date, coherent, and rigorous framework for controlling, managing, and predicting software development processes. With an emphasis on real-world applications, Fenton and Pfleeger apply basic ideas in measurement theory to quantify software development resources, processes, and products. The book offers an accessible and comprehensive introduction to software metrics, now an essential component of software engineering for both classroom and industry. Software Metrics features extensive case studies from Hewlett Packard, IBM, the U.S. Department of Defense, Motorola, and others, in addition to worked examples and exercises. The Second Edition includes up-to-date material on process maturity and measurement, goal-question-metric, planning a metrics program, measurement in practice, experimentation, empirical studies, ISO9216, and metric tools.
A Temporal RFID Data Model for Querying Physical Objects

RFID holds the promise of real-time identifying, locating, tracking and monitoring physical objects without line of sight, and can be used for a wide range of pervasive computing applications. To achieve these goals, RFID data have to be collected, transformed and expressively modeled as their virtual counterparts in the virtual world. RFID data, however, have their own unique characteristics – including aggregation, location, temporal and history-oriented – which have to be fully considered and integrated into the data model. The diversity of RFID applications pose further challenges to a generalized framework for RFID data modeling. In this paper, we explore the fundamental characteristics of RFID applications, and classify applications into a set of basic scenarios based on these characteristics. We then develop constructs for modeling each scenario, which then can be integrated to model most complex RFID applications in real world. We further demonstrate that our model provides powerful support on querying physical objects in RFID-based pervasive computing environment.
Complete Dictionary Recovery Over the Sphere I: Overview and the Geometric Picture

We consider the problem of recovering a complete (i.e., square and invertible) matrix <inline-formula> <tex-math notation="LaTeX">$ A_{0}$ </tex-math></inline-formula>, from <inline-formula> <tex-math notation="LaTeX">$ Y \in \mathbb R ^{n \times p}$ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$ Y = A_{0} X_{0}$ </tex-math></inline-formula>, provided <inline-formula> <tex-math notation="LaTeX">$ X_{0}$ </tex-math></inline-formula> is sufficiently sparse. This recovery problem is central to theoretical understanding of dictionary learning, which seeks a sparse representation for a collection of input signals and finds numerous applications in modern signal processing and machine learning. We give the first efficient algorithm that provably recovers <inline-formula> <tex-math notation="LaTeX">$ A_{0}$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$ X_{0}$ </tex-math></inline-formula> has <inline-formula> <tex-math notation="LaTeX">$O \left ({ n }\right )$ </tex-math></inline-formula> nonzeros per column, under suitable probability model for <inline-formula> <tex-math notation="LaTeX">$ X_{0}$ </tex-math></inline-formula>. In contrast, prior results based on efficient algorithms either only guarantee recovery when <inline-formula> <tex-math notation="LaTeX">$ X_{0}$ </tex-math></inline-formula> has <inline-formula> <tex-math notation="LaTeX">$O(\sqrt {n})$ </tex-math></inline-formula> zeros per column, or require multiple rounds of semidefinite programming relaxation to work when <inline-formula> <tex-math notation="LaTeX">$ X_{0}$ </tex-math></inline-formula> has <inline-formula> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> nonzeros per column. Our algorithmic pipeline centers around solving a certain nonconvex optimization problem with a spherical constraint. In this paper, we provide a geometric characterization of the objective landscape. In particular, we show that the problem is highly structured with high probability: 1) there are no “spurious” local minimizers and 2) around all saddle points the objective has a negative directional curvature. This distinctive structure makes the problem amenable to efficient optimization algorithms. In a companion paper, we design a second-order trust-region algorithm over the sphere that provably converges to a local minimizer from arbitrary initializations, despite the presence of saddle points.
Moisture Buffering in the Indoor Environment

Moisture buffering in the indoor environment is the ability, through absorption and desorption, of surface materials to attenuate the moisture variations of the indoor air. Moisture buffering plays an important role in understanding the risks for biological growth in surface materials in the indoor environment, e.g., mold growth on walls and house dust mites in beds, and thereby also have an impact on the health of the inhabitants. Apart from the health aspects, moisture buffering is also important to take into account in hygrothermal calculations as well as for design of indoor climate systems. The interaction between surface materials and the humidity of the indoor air is the scope of this Ph.D.-project reported in this thesis. The main objective is to enhance the understanding of the moisture buffering of surface materials in the indoor environment. The focus has been on moisture buffering in textile materials and upholstered furniture, common to Scandinavia. The moisture buffering has been investigated through experimental work, field measurements and modeling. The experimental work covers development of laboratory methods and determination of basic material properties to full scale climatic chamber measurements. A definition scheme regarding the moisture buffering in the indoor environment, at three spatial levels ? material, component and room level, is proposed. Within the project, a method, to determine sorption isotherms has been developed. The moisture sorption properties for textile materials, common in the indoor environment, have been determined. The moisture conditions in a chair seat and in two bed systems have been investigated through climatic chamber measurements and field measurements, respectively. Both types of upholstery have also been studied through mathematical models. The impact of furniture and furnishing, on moisture buffering performance, was studied in a full scale climatic chamber study. The main conclusion from the project is that moisture buffering in furniture and furnishing will be important in the moderation of indoor humidity variations with short time scales (hours and days).
Comparing measures of sparsity

Sparsity is a recurrent theme in machine learning and is used to improve performance of algorithms such as non-negative matrix factorization and the LOST algorithm. Our aim in this paper is to compare several commonly-used sparsity measures according to intuitive attributes that a sparsity measure should have. Sparsity of representations of signals in fields such as blind source separation, compression, sampling and signal analysis has proved not just to be useful but a key factor in the success of algorithms in these areas. Intuitively, a sparse representation is one in which a small number of coefficients contain a large proportion of the energy. In this paper we discuss six properties (robin hood, scaling, rising tide, cloning, bill gates and babies) that we believe a sparsity measure should have. The main contribution of this paper is a table which classifies commonly-used sparsity measures based on whether or not they satisfy these six propositions. Only one of these measures satisfies all six: the Gini index.