Miracle-dz/UniFAR-FTUs huggingface dataset:
Workload Distribution and Division of Labor in Cooperative Societies

Cooperative groups are ubiquitous in animals, as are the challenges of allocating labor to accomplish cooperative tasks, including territory defense, hunting, and brood care. Individual contributions can differ in two ways, both of which can influence fitness: group members can vary in overall helpfulness (workload) or they can specialize on different tasks (division of labor). In this review, we encourage additional, simultaneous study of both workload distribution and division of labor. Historically, workload distribution has been studied in the contexts of collective action and cooperative offspring care. Both areas of research aim to understand how shared benefits can emerge from individual contributions, but they remain poorly integrated. We argue that each of these literatures has strengths that could benefit the other, and we highlight potential areas of crosstalk. Next, we review the literature on division of labor in taxa other than the eusocial insects, discuss parallels and differences in division of labor between eusocial insects and other animals, and encourage more work on cooperative, noninsect taxa. Rigorous work on individual contributions to cooperative tasks will expand our understanding of the causes and consequences of individual variation and the evolutionary stability of social living.
The Recent Star Formation History of GR 8 from Hubble Space Telescope Photometry of the Resolved Stars

We have used the Hubble Space Telescope to observe the resolved stars in the dwarf irregular galaxy GR 8 (DDO 155, UGC 8091). The data consisted of dithered Wide Field Planetary Camera 2 images in three bands: F439W (1 hr), F555W (30 minutes), and F814W (30 minutes). The stellar photometry was extracted with a modified version of DoPHOT. Artificial star tests showed the data to be 50% complete to V = 26.3, B = 25.4, and I = 25.2. The color-magnitude diagrams contain well-defined populations, including a very young main sequence (MS) (<10 Myr), and a red giant branch as old as several Gyr. These features align well with stellar evolution models of the appropriate metallicity. The distance based on the tip of the red giant branch is in excellent agreement with the Cepheid determination of μ = 26.75 ± 0.35 (2.2 Mpc), which we adopted. An extended stellar "halo" was discovered well beyond the H I. Based on the MS and blue HeB luminosity function, we calculated the star formation rate (SFR) over the past 500 Myr. The SFR has been fairly constant, at 400 M⊙ Myr-1 kpc-2 with up to 60% variations. The blue HeB stars were used as a tracer for the location of star formation over this time period. The star formation occurred in superassociation size regions (100–200 pc), which lasted ∼100 Myr. These regions come and go with no obvious pattern, except that they seem to concentrate in the current locations of H I clumps. This suggested that the H I clumps are long-lived features that support several star-forming events over time. The most likely explanation is that the star-forming regions are gravitationally bound. We estimated the gas-to-star conversion efficiency to be 6%. We compared our results with those of three other dI galaxies: Sextans A, Pegasus DIG, and Leo A. There is a trend of higher SFR per area with larger MH I/LB. Also, the star formation pattern is similar in all four galaxies. Finally, none of the four dI galaxies contained a large starburst, comparable to those in BCD galaxies. Combining the histories of all four galaxies, this implies that less than 5% of dI galaxies are hosting a strong burst of star formation at any given time. Observations of more galaxies are needed to improve this statistic.
A novel, refined dataset for real-time Network Intrusion Detection

In this day and age of widespread Internet access, more and more aspects of the economy are becoming dependent on various aspects of network technologies. Cybercrimes are on the rise and massive numbers of network security breaches occur every year. This paper presents network data collected in the Netflow format and its application to detect network attacks. The paper proposes a refined, real-world dataset collected from an academic network. The dataset is a direct result from the experience gained by working on and with the SIMARGL2021 dataset. The applicability of the new dataset is demonstrated on several machine learning algorithms. This novel dataset is open-sourced for researchers to download and use in scientific work.
A Near Real-Time Scheme for Collecting and Analyzing IoT Malware Artifacts at Scale

The chronic proliferation of Internet of Things (IoT) botnet malware activities coupled with an unprecedented rise in security vulnerabilities convene a new world of opportunities for perpetrators and unveil a new set of hurdles in deriving relevant IoT malware intelligence. Such shortfall within the IoT paradigm exacerbates the capabilities for largely identifying the prevailing IoT malware threats, the origin of the IoT attacks, as well as, the security deficit associated with the IoT paradigm. Previous work has vastly studied IoT malware activities in the wild but has not profiled at a large scale malicious activities to collect in near real-time central IoT artifacts much-needed to understand and eventually elevate the security posture of the IoT ecosystem. To this end, we propose in this work a near real-time collection scheme to collect and analyze at large IoT malware artifacts essential for understanding the prevalent cyber security risks. We leverage in this work a large network telescope comprising of 16.7 million IPs as one extensive honeypot to examine evidence of malicious IoT probes in the wild. Subsequently, we employ a deception technique to respond to these probes and eventually establish bogus connections to collect IoT malware artifacts. In only 120 hours of near real-time measurements, our proposed scheme collected 80,569,070 interactions originating from 30,190 malware-infected IoT devices. Accordingly, we derive pivotal IoT malware intelligence which includes system commands, file-less attacks evidence, payload URLs, Executable and Linkable Format (ELF) binaries, log-in credentials, malicious LDAP servers, and unique insights on the abuse of the recent Log4shell security vulnerability in distributing IoT malware binaries.
Reactive Jamming Detection for LoRaWAN Based on Meta-Data Differencing

Reactive jamming in LoRaWAN networks is a stealthy way to implement Denial-of-Service attacks against selected devices because the cause of the interference remains hidden from a network operator. In order to make such attacks more detectable this paper proposes a novel algorithm, which is able to expose a jamming attempt from a single LoRaWAN packet. By monitoring deviations in meta-data of LoRaWAN frames, our method can distinguish a jamming attempt from a normal packet collision with up to 99% accuracy. Furthermore, the presented algorithm is also suitable for light-weight implementations on LoRaWAN devices due to its low complexity.
On the feasibility of detecting injections in malicious npm packages

Open-source packages typically have their source code available on a source code repository (e.g., on GitHub), but developers prefer to use pre-built artifacts directly from the package repositories (such as npm for JavaScript). Between the source code and the distributed artifacts, there could be differences that pose security risks (e.g., attackers deploy malicious code during package installation) in the software supply chain. Existing package scanners focus on the entire artifact of a package to detect this kind of attacks. These procedures are not only time consuming, but also generate high irrelevant alerts (FPs). An approach called LastPyMile by Vu et al. (ESEC/FSE’21) has been shown to be effective in detecting discrepancies and reducing false alerts in vetting Python packages on PyPI by focusing only on the differences between the source and the package. In this work, we propose to port that approach to scan JavaScript packages in the npm ecosystem. We presented a preliminary evaluation of our implementation on a set of real malicious npm packages and the top popular packages. The results show that while being 20.7x faster than git-log approach, our approach managed to reduce the percentage of false alerts produced by package scanner by 69%.
Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking—a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading.
Comprehensive Electric load forecasting using ensemble machine learning methods

The accuracy of electric load forecasting is crucial when working on applications in power grid decision-making and operation. Due to the non-linear and stochastic behaviour of customers, the electric load profile is a complicated signal. In this paper, authors propose machine learning based automated system for electricity load forecasting, taking into consideration various variable factors that have an impact on the future electricity load demand. Three machine learning algorithms are used for evaluation of the proposed framework. The algorithms are evaluated on electricity load data collected from eastern region of Ontario, integrated with the weather and population data of the region. The Light GBM algorithm comparatively performs best with mean absolute error of 0.156. The developed system can be used for more accurate and efficient load forecasting applications.
Key Criticisms of Six Sigma: A Systematic Literature Review

Six Sigma has been one of the most popular initiatives to improve management processes over the past few decades. Amidst the success stories on the Six Sigma, there exist some literature studies on the criticisms of Six Sigma. The purpose of this paper is to analyze the existing literature on the criticisms of Six Sigma through a systematic literature review. The criticisms need to be analyzed so that there is growth in the knowledge and understanding of Six Sigma. This paper analyzes literature through various electronic databases such as Academic Source Premier (EBSCO), Google Scholar, Business Source Premier (EBSCO), Emerald Insight, IEEE Xplore Digital Library, JSTOR, ProQuest, Science Direct, Taylor & Francis, PubMed, Wiley, Inderscience, Scopus, and World Public Library. Sixty-one relevant articles are found and analyzed in depth. This paper finds 12 major themes of criticisms on Six Sigma. Based on the findings, 16 research directions are offered. Amidst the plethora of literature review on the success of Six Sigma, this is the first comprehensive systematic literature review on the criticisms of Six Sigma. The authors firmly believe that such criticisms will help the academicians as well as the practitioners to understand some of the rudimentary gaps in implementing Six Sigma as a business improvement strategy.
Deep Learning Image Classification for Pneumonia Detection

Classification of medical images has an important role in medical diagnosis support. Recently deep learning methods have achieved great success at classification of medical images. This paper shows the methodology and experimental results of applying deep learning methods for pneumonia detection from X-rays images. The available dataset was classified using a convolutional neural network. The encoder part of the network follows UNet architecture, which is followed by two fully connected layers. The proposed architecture achieved median F1-Score on the testing set of 0.938 and median accuracy of 0.965.
New examples of terminal and log canonical singularities

3 (Previous examples). One of the first results on the singularities of higher dimensional birational geometry, proved in [Rei80], says that a 3-fold terminal singularity whose canonical class is Cartier is a hypersurface double point. Thus every 3-fold terminal singularity is a quotient of a hypersurface double point by a cyclic group action. This description can be developed into a complete list; see [Rei87]. For some time it has been an open question if, in some sense, terminal singularities in higher dimensions also form an essentially bounded family. The first relevant examples are in an unpublished note [BR10].
Decentralized guaranteed cost control of a class of large-scale interconnected systems

This paper focuses on a class of large-scale systems with parameter uncertainties and nonlinear perturbations in each subsystem and interconnections. We deal with the design of decentralized state feedback and output feedback controllers such that for all admissible uncertainties and interconnections, the closed-loop large scale system is asymptotically stable and an upper bound of the given quadratic cost is minimized. We first derive a robust performance analysis result in terms of a set of linear matrix inequalities (LMIs). This analysis result is then applied to solve both the decentralized state feedback and dynamic output feedback guaranteed cost control problems. A procedure is given for the minimization of the quadratic cost in terms of LMIs.
Amatör Futbolcularin Seçilmiş Fiziksel, Fizyolojik ve Motorik Özelliklerinin Mevkilerine Göre Karşilaştirilmasi Comparing Selected Physical and Motoric Characteristics of Turkish Amateur Soccer Players According to Playing Positions

The aim of this study was determined differences in physical, physiology and motoric characteristics of a group of Turkish amateur soccer players according to their playing positions. 70 amateur male soccer players participated voluntarily to the study. Participants were divided into 6 groups according to the playing position as goalkeeper (n=4), fullback (n=10), stopper (n=13), midfielder (n=18), winger (n=15) and forward (n=10). Participant’s age, body weight and height, body mass index, body fat percentage, fat free mass, anaerobic power, speed, leg strength, vertical jump, aerobic capacity and flexibility characteristics were measured. Obtained values were compared statistically by using SPSS (ver. 14) with the help of ANOVA and TUKEY test was used to identify which groups had significant differences. Alpha level was set as 0,05 for statistical significance. ANOVA results showed that there were significant differences (p>0,05) among groups at the point of body weight, height, fat free mass, vertical jump and anaerobic power, but there were no differences in the rest of the features. Consequently, running distances of the players during matches became closer to each other because of today’s football game concept, it is thought to have reduction of the differences in physical and physiological characteristics of players in different positions.
Evaluation of European regional reanalyses and downscalings for precipitation in the Alpine region

Datasets of the observed weather and climate on a regular grid are an important requisite for environmental and climate change research. In this paper we evaluate several European-scale regional reanalyses, developed in the EU project EURO4M, and compare them to existing datasets popularly used in applications today. The evaluation focuses on precipitation in the region of the European Alps, which, due to the marked topographic imprints, poses a challenging test bed. Utilizing a set of statistical indicators, we examine how the datasets represent the spatial pattern, annual cycle, frequency distribution and interannual variations. The evaluated datasets encompass new model-based regional reanalyses (UKMO and HIRLAM) and downscaling datasets (MESAN and MESCAN), one global reanalysis (ERA-Interim), and three station-based interpolation datasets (E-Obs, CRU, GPCC). The reference (APGD) is a gridded interpolation derived from very dense rain-gauge observations. The evaluation provides insight into the relative strengths and limitations of the various datasets and construction concepts. The new model-based regional reanalyses show richer spatial variations in the climatology of daily precipitation compared to the driving global reanalysis (ERA-Interim), and they correct for several unrealistic spatial features seen in global and continental interpolation datasets. But they also show biases, shifts in regional anomalies and inaccuracies in mountain-valley contrasts. Common to both regional reanalyses are overestimates of mean precipitation and wet day frequency, and an underestimate of the frequency of heavy precipitation. The 4DVar UKMO reanalysis shows a better space-time coherency with APGD compared to the 3DVar HIRLAM reanalysis. The accuracy of datasets that explicitly use rain-gauge observations (interpolation and downscaling) is found to be very heterogeneous, depending on the density of available station time series. In regions of high station density, downscaling procedures effectively correct for biases in the underlying reanalysis and reduce RMSE and SEEPS errors by a factor of 2 to 5. In areas where E-Obs and the downscaling datasets have similar input of station data the skill of the two methodologies is comparable. Interannual variations of monthly mean precipitation are highly correlated (> 0.9) with those of the reference for all European-scale datasets and even for meso-beta scale subregions. Our evaluations illustrate that the new regional reanalyses provide a valuable data resource also in a region with complex topography, but many applications will have to involve bias correction and downscaling procedures based on direct observations, ideally at high spatial resolution.
Integration of a High Frequency Inductive Power Transfer System to Energize Agricultural Sensors Through Soil

This paper describes the design and integration of a 13.56 MHz inductive power transfer (IPT) system and an in-house underground soil sensor network. The transmitter of this wireless power system was retrofitted on a Matrice 100 drone for periodic fast-charging missions, and the receiver was designed to fit into a sealed underground enclosure. The experiments reported in this paper involve a 40-day agricultural field trial where power was fed wirelessly to the soil sensor network. During this trial, the receiver’s enclosure was buried at a depth of 200 mm in soil. These experiments feature an average charging efficiency (from the drone’s battery to the underground station) of 33.5% (5% peak) with a peak power transfer of 35 W. Compared to laboratory testing, a significant increase in the end-to-end losses (of around 30 W) was observed in the field tests due to the presence of soil.The work reported in this paper is part of a collaborative project which looks to enable state-of-the-art high-frequency wireless power technology in agricultural applications.
On permutations of lacunary series

It is a well known fact that for periodic measurable $f$ and rapidly increasing $(n_k)_{k \geq 1}$ the sequence $(f(n_kx))_{k\ge 1}$ behaves like a sequence of independent, identically distributed random variables. For example, if $f$ is a periodic Lipschitz function, then $(f(2^kx))_{k\ge 1}$ satisfies the central limit theorem, the law of the iterated logarithm and several further limit theorems for i.i. d. \ random variables. Since an i.i. d. \ sequence remains i.i. d. \ after any permutation of its terms, it is natural to expect that the asymptotic properties of lacunary series are also permutation-invariant. Recently, however, Fukuyama (2009) showed that a rearrangement of the sequence $(f(2^kx))_{k\ge 1}$ can change substantially its asymptotic behavior, a very surprising result. The purpose of the present paper is to investigate this interesting phenomenon in detail and to give necessary and sufficient criteria for the permutation-invariance of the CLT and LIL for $f(n_kx)$.
Stationary solutions in the Rayleigh-Taylor instability for spatially periodic flow

Stationary solutions in the Rayleigh-Taylor instability for spatially periodic flow with C4 symmetry (square lattice of bubbles and jets, lattice period=21r/k, k is the wave number) are investigated analytically. A one-parameter family of solutions S is found. The solutions are shown to converge on S as a function of the order of the approximation, and it is proved that the family S exists and is unique. We parametrize the solutions with the radius of curvature R at the stagnation point on a bubble. The ascent velocity v(R) of a bubble is found as a function of the parameter. Each point of S corresponds to an exact solution. The family has a termination point with kRc,-3.0 and v,,(Rc,) 0.99&3, where g is the acceleration of gravity. A solitary jet is another terminal point of S and is localized in the limit kR+m with v(R)--+4 &%JkR. A comparison is made to the case of a stationary two-dimensional flow. O 1995 American Institute of Physics.
Determinants of smartwatch adoption among IT professionals - an extended UTAUT2 model for smartwatch enterprise

Smartwatches these days have gained popularity under wearable device category. Consumers of smartwatches consider several factors while choosing smartwatches. On this premise, the current research is undertaken to identify the determinants of the smartwatch adoption. In order to arrive at this objective, the study has employed unified theory of acceptance and use of technology 2 (UTAUT2) model as a means and underpinning framework. To suit context, the present study has extended UTAUT2 model by incorporating self-efficacy (SEF), personal innovativeness (PINNO), social media influence (SMI), social image (SIMG), aesthetics (AES) and external social influence (ESI). To validate and check the explanatory power of the extended UTAUT2 model the study has considered Structural equation modelling using Smart PLS 2.0. The results arrived in this study has concluded that the extended UTAUT2 model has a better explanatory power on behavioural intention towards smartwatch adoption. Effect size (f2) and predictive relevance (Q2) for the extended UTAUT2 model are also discussed.