Miracle-dz/UniFAR-FTUs huggingface dataset:
“It Must Include Rules”

When middle school students encounter computer models of science phenomenon in science class, how do they think those computer models work? Computer models operationalize real-world behaviors of selected variables, and can simulate interactions between the modeled elements through programmed instructions. This study explores how middle school students think about the high-level semantic meaning of those instructions, which we term rules. To investigate this aspect of students’ computational thinking, we developed the Computational Modeling Inventory and administered it to 253 7th grade students. The Inventory included three computer models that students interacted with during the assessment. In our sample, 99% of students identified at least one key rule underlying a model, but only 14% identified all key rules; 65% believed that model rules can contradict; and 98% could not distinguish between emergent patterns and behaviors that directly resulted from model rules. Despite these misconceptions, compared to the “typical” questions about the science content alone, questions about model rules elicited deeper science thinking, with 2--10 times more responses including reasoning about scientific mechanisms. These results suggest that incorporating computational thinking instruction into middle school science courses might yield deeper learning and more precise assessments around scientific models.
A Multi-view Approach to Preserve Both Privacy and Utility in Network Trace Anonymization

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, which may results in a significant loss of data utility. In this paper, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces: those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy; whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The experimental results show that our approach can significantly reduce the level of information leakage (e.g., less than 1% of the information leaked by CryptoPAn) with comparable utility.
The Philosopher's Corner

In an effort to contribute to the recent debate around epistemological and methodological anarchism inspired by the thinking of Paul Feyerabend, we reflect on Habermas's pragmatist perspective of social science. We argue that the information systems field instantiates a sort of pluralism that goes beyond the relativistic conclusions of Feyerabend. This is evident through the different traditions of research into business processes and organizational routines. There is a healthy diversity of epistemological and methodological approaches in this research. Accompanying this diversity is an openness to novelty and change. Yet, at the same time, this does not necessitate the abandonment of rigor and a cumulative tradition implied by "anything goes. " Anything does not go, and that's a good thing. There is not a singular, hegemonic approach to what constitutes strong information systems research, but neither have we devolved into anarchy.
Autoantibodies against complement component C1q in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is the archetype of a systemic autoimmune disease, but the multifaceted pathogenic mechanisms leading to inflammation and organ damage are not fully understood. Homozygous deficiency of complement C1q, the first component of the classical pathway of complement, is strongly associated with the development of SLE, thus pointing at a primarily protective role of C1q. However, while most SLE patients do not have hereditary C1q deficiency, there is indirect evidence for the importance of C1q in the inflammatory processes of the disease, including hypocomplementemia as a result of activation via the classical pathway, deposition of C1q in affected tissues and the occurrence of autoantibodies against C1q (anti‐C1q). The growing body of knowledge on anti‐C1q led to the establishment of a biomarker that is used in the routine clinical care of SLE patients. Exploring the binding characteristics of anti‐C1q allows to understand the mechanisms, that lead to the expression of relevant autoantigenic structures and the role of genetic as well as environmental factors. Lastly, the analysis of the pathophysiological consequences of anti‐C1q is of importance because C1q, the target of anti‐C1q, is a highly functional molecule whose downstream effects are altered by the binding of the autoantibody. This review summarises current study data on anti‐C1q and their implications for the understanding of SLE.
Influence of a Maximal Incremental Test Until Exhaustion on the Urinary Excretion of Steroid Hormones in Trained Cyclists

Abstract This study aimed to assess the effect of a maximum incremental stress test through urinary concentrations of steroid hormones in trained cyclists. Twenty male cyclists participated in the study (23.83 ± 2.3 years; 1.76 ± 0.03 m; 66.94 ± 3.59 kg; training volume: 20.50 ± 2.35 h/week). Athletes performed a maximum incremental test until exhaustion on a cycle ergometer and urine samples were collected at three different time points: before, immediately after, and 48 h after the test. Lactate, creatinine and urinary concentrations of testicular and adrenal androgens were obtained as well as urinary concentrations of glucocorticoid hormones. An increase in lactate was observed after the test (p < 0.01). There were decreases in the urinary excretion of androgenic hormones after the test, which were significant in testosterone, androsterone, androstenedione, total adrenal androgen and total testicular androgen (p < 0.05). The values were restored after 48 h (p < 0.05). Urinary cortisol concentrations decreased after the test (p < 0.05). A decrease was also observed in the ratio of anabolic/catabolic hormones (p < 0.05) increasing 48 h after the test (p < 0.05). Increased acute physical exercise until exhaustion causes variations in the urinary excretions of steroid hormones which were restored 48 h after exercise. Urinary excretion of steroid hormones could be a valid method of monitoring training loads.
Compact and novel coupled line microstrip bandpass filter based on stepped impedance resonators for millimetre-wave communications

Abstract This paper presents a compact and the low-cost coupled line band-pass filter with application to future generation millimetre-waves and 5G communications. The proposed approach of the filter design is based on the coupled-line and centre tapped upper and lower stepped impedance resonators. These resonators generate the sharp rejection, wide bandwidth, and abet to realize the compact filter. A detailed theoretical as well as the numerical analysis of the filter has also been investigated. As a demonstration, the proposed band-pass filter configuration has been designed and fabricated at the 33.5 GHz frequency using a low-cost PCB technique. It has observed that the proposed filter, results in a better return loss and the low insertion loss. The experimental results has been presented and compared with the simulated results and has found quite satisfactory. Moreover the results obtained validate a good agreement with each other.
The Neuromuscular Characteristics of Gymnasts’ Jumps and Landings at Particular Stages of Sports Training

Abstract Safe and proper landings are crucial elements of gymnastics events. Long-term training leads to specific neuromuscular adaptations which are yet to be explored in terms of gymnastic landings. The aim of the study was to assess differences in landings’ neuromuscular characteristics between gymnasts at three subsequent gymnastic training stages and age-matched non-athletes. Forty-six gymnasts (G) and 58 controls (C) performed countermovement jumps on a force plate with simultaneous surface electromyography (SEMG) of lower body muscles, measured during the pre-(100 ms) and post-landing phase (0-100 and 0-200 ms). Three age groups participated in the study: 8–10 (G1, C1), 12–14 (G2, C2), 18–25 (G3, C3) years. Analysis included the normalized root mean square (NRMS) SEMG signal and ground reaction forces (GRFs). Gymnasts achieved 13% higher values (p = 0.04) of relative peak GRFs in comparison with controls. It was especially seen in 8–10-year-olds: G1 presented 33% higher (p = 0.03) results than C1 and G2. In SEMG analysis, gymnasts showed overall lower NRMS values in comparison with the controls. In the pre-landing phase, the NRMS in the rectus femoris was from 1.6 up to 3.4 times higher for C1 (p = 0.02) than for C2, G2, C3, and G3. Gymnasts across subsequent training stages exhibit different patterns of neuromuscular coordination during landings. The highest GRF observed in the youngest gymnasts may be a potential risk factor of injuries. Therefore, further injury-focused investigation is recommended to monitor landing strategies among gymnasts of different stages with particular emphasis on the beginners.
Enhancement of As(V) adsorption from aqueous solution by a magnetic chitosan/biochar composite

In this study, a magnetic chitosan/biochar composite (MCB) was prepared successfully, and characterized by SEM, TEM, VSM, XRD, FTIR, XPS, and zeta-potential to obtain its physical and chemical properties. The influence of different sorption parameters such as pH, contact time, initial As(V) concentration, and temperature on As(V) removal were studied thoroughly in order to optimize the reaction conditions. The results showed that the MCB adsorbed more As(V) (11.961 mg g−1) than pristine biochar (3.681 mg g−1). In addition, the adsorption behavior could be well described by a pseudo-second-order and Langmuir model. Thermodynamic parameters revealed that the sorption reaction was a spontaneous and endothermic process. The adsorbed MCB could be effectively regenerated by 1.0 mol L−1 NaOH, and the adsorption capacity decreased from 11.823 to 8.078 mg g−1 in five cycles. In conclusion, MCB could be used as an efficient adsorbent for environment purification and conveniently separated from aqueous solution after adsorption.
Effects of Nanoscale Heterogeneities on the Reactivity of Shocked Erythritol Tetranitrate

The mechanochemical effects of 5 nm spherical heterogeneities on the reactivity of shocked crystalline erythritol tetranitrate (ETN), an emerging improvised explosive, are revealed for the first time. Reactive molecular dynamics simulations in conjunction with the symmetric plate-impact technique, are employed to characterize different stages of the process, including shock–void interaction, void collapse, enhanced reactivity, and subsequent molecular decomposition. The formation of supersonic nanojets from the proximal void surface is observed to greatly enhance local heating following impact of the nanojets on the distant void surface. It is demonstrated that the nanovoid collapse closely matches a Rayleigh-type hydrodynamic bubble collapse, including a spherical-to-crescent shape transformation. The chemical decomposition mechanism of condensed phase ETN is analyzed and is shown to follow a unimolecular path. The molecular decomposition of a defect containing crystal occurs with significantly higher ra.
Emotional virtual agents: how do young people decode synthetic facial expressions?

Given the need of remote learning and the growing presence of virtual agents within online learning environments, the present research aims at investigating young people’ ability to decode emotional expressions conveyed by virtual agents. The study, involves 50 healthy participants aged between 22 and 35 years (mean age=27.86; SD= ±2.75; 30 females) which were required to label pictures and video clips depicting female and male virtual agents of different ages (young, middleaged and old) displaying static and dynamic expressions of disgust, anger, sadness, fear, happiness, surprise and neutrality. Depending on the emotional category, significant effects were observed for the agents’ age, gender, and type of administered (static vs dynamic) stimuli on the young people’ decoding accuracy of the virtual agents’ emotional faces. Anger was significantly more accurately decoded in male rather than female faces while the opposite result was observed for happy, fearful, surprised, and disgusted faces. Middle aged faces were generally more accurately decoded than young and old emotional faces except for sadness and disgust. Significantly greater accuracy was observed for dynamic vs static faces of disgust, sadness, and fear, in contrast to static vs dynamic neutral and surprised faces.
GPU Implementation of Quantum Secure ABC Cryptosystem on Cuda

In this paper, we consider the ABC cryptosystem based on multivariate polynomial systems which is one of the post-quantum cryptosystems. We review the theoretical structure of the ABC cryptosystem and implement it on the GPU by using the NVIDIA CUDA technology. We carry out the GPU and CPU implementation details of the ABC cryptosystem on three computers with different graphics cards. We also give a comprehensive comparison between the implementations. We compute the required number of arithmetic operations for each process: key generation, encryption and decryption. According to the experimental results, the GPU implementations have better memory performance than the CPU implementations. Moreover, the encryption process is faster in the GPU implementation. Due to the structure of ABC cryptosystem, the decryption process is slower in the GPU implementation.
Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Tremendous growth in the availability of judicial documents has demanded the rise of information extraction (IE) techniques that support the automatic extraction of relevant concepts or data from judicial texts. Among various approaches available for IE, ontology‐based IE has proven to be the most appropriate for extracting domain‐specific information from natural language text. Through this article, we propose a knowledge‐driven, semi‐supervised pattern‐based learning (bootstrapping) approach to extract domain‐specific facts from judicial text, starting with a small set of seed facts. In the semantic analysis of legal text, fact extraction is the next step to entity identification, which involves the identification of roles played by each entity in the judicial text. The proposed methodology learns extraction patterns for 12 classes of facts from the judicial text through the integration of the domain ontology called judicial case ontology (JCO). The experimental results were evaluated by human judges and found to be quite promising. One main feature of the proposed methodology is its portability across various domains (such as medical, banking, insurance, etc.) which in turn helps build expert systems in various sectors.
Irreducible Complexity: How Do Causal Bayes Nets Theories of Human Causal Inference Inform the Design of a Virtual Ecosystem?

: Recent computational theories on causal inference, developed by machine learning theorists and co-opted by psychologists and cognitive sciences, predict specific patterns of behavior when humans infer causal connections in simple systems. However, these theories may not be scalable to model complex causal systems, such as ecosystems. Said theories are reviewed herein, and future strands of research are suggested.
IMECA Based Assessment of Internet of Drones Systems Cyber Security Considering Radio Frequency Vulnerabilities

A cybersecurity model of a system for remote monitoring of critical infrastructure facilities based on the use of Internet of Drones (IoD), taking into account radio frequency cyber vulnerabilities, has been developed and discussed. The radio frequency vulnerabilities of infocommunication channels of such a system and possible methods of unauthorized intrusion using software-defined radio technology are analyzed. The consequences of such an invasion were assessed using the Intrusion Modes Effects and Criticality Analysis (IMECA) method. Result of the assessment is a risk matrix considering probability and severity of successful intrusions. It’s suggested recommendations of options to decrease risks of cyber failures of IoD systems.
Technology enhanced active and collaborative learning in distance higher education: students' perception

This paper focuses on the three year-degree e-Learning course in Psychological Sciences and Techniques delivered by Sapienza and Unitelma Sapienza University of Rome. In designing the course, a decision was made to integrate classic e-Learning with a participatory and collaborative approach, safeguarding the strengths of both models. To this end, the degree course is based on three fundamental pillars: a theoretically anchored Learning Design, an experienced and continuously supported team of teachers and tutors, a flexible and rich Learning Management System. The study presents the data relating to the opinions of the first graduates of the course (a.y. 2019-2020), collected through a semi-structured questionnaire in which different dimensions relating to the overall experience were investigated. 22 students answered the questionnaire so far. Results show how the students strongly appreciated the experience, considering the online course to be worthy of a face-to-face course, both from the point of view of the quality of teaching and the possibility of learning knowledge and skills useful both in academic and professional life.
Visible Spending: Information Visualization of Spending Behavior and the Thought-Act Gap

Most people know they ought not to spend beyond their means. Yet, many do so anyway, often without being aware of it. To explore how to narrow this thought-act gap, we conducted a three-month mixed-methods pilot study (N=25) of the effects of graphical vs. textual feedback on the relationship between estimated and actual spending. Results of a 2 x 2 x 3 repeated measures, doubly multivariate ANOVA indicated that high-debt participants experienced improvement in their estimations more than low-debt participants, and that the estimations of participants who received graphical visualizations became more accurate. Excerpts from interview data shed light on the learning processes underlying such changes.
How a New Actor Was Temporarily Enrolled Into the Network of Game Playing

: This short paper uses actor-network theory to describe how an add-on (a user-created modification) was temporarily used to diagnose problems a group of game players was having with a particular in-game activity. The players were engaged in a high-stakes collaborative activity called raiding in the massively multiplayer online game (MMOG) World of Warcraft (WoW). They met twice a week for a 10-month period to defeat common game-controlled monsters. The add-on’s use complicates the notion that tools are necessarily used in a way they were designed to be used. Instead, “in the wild” practice emerges out of the push-pull relationship of competing parties and objects.
Discretization based framework to improve the recommendation quality

Recommendation systems are information filtering software that delivers suggestions about relevant stuff from a massive collection of data. Collaborative filtering approaches are the most popular in recommendations. The primary concern of any recommender system is to provide favorable recommendations based on the rating prediction of user preferences. In this article, we propose a novel discretization based framework for collaborative filtering to improve rating prediction. Our framework includes discretization-based preprocessing, chi-square based attribution selection, and K-Nearest Neighbors (KNN) based similarity computation. Rating prediction affords some basis for the judgment to decide whether recommendations are generated or not, subject to the ratio of performance of any recommendation system. Experiments on two datasets MovieLens and BookCrossing, demonstrate the effectiveness of our method.
Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

Heart diseases are a major cause of human casualties each year. An accurate and efficient diagnosis is essential to minimize their risk. This paper presents a system for the classification of multiple cardiac disorders based on pulse plethysmographic (PuPG) signal analysis. In particular, the work focuses on the detection and classification of ischemic and rheumatic heart diseases using proposed 1D local ternary patterns of PuPG signals. The proposed methodology is applied on a self‐collected dataset consisting of 250 PuPG signals from 50 normal/healthy subjects, 140 signals from 28 ischemic patients, and 180 signals from 36 rheumatic patients. The effectiveness of proposed feature descriptors to precisely represent different classes of data is verified by several classifiers namely K‐nearest neighbours (KNN), support vector machines (SVM), and decision tree (DT) with 10‐fold cross‐validation. The proposed methodology achieves the best detection performance with 99% accuracy, 100% sensitivity, and 98% specificity using an SVM classifier with cubic kernel. The comparative analysis demonstrates that the proposed method is more accurate and reliable as compared to several existing works on cardiac disorders classification.