Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional Reasoning

Conditional reasoning refers to both a general theory of personality and an indirect measurement system used to assess personality. The basic tenets of the conditional reasoning theory of personality include: a) individuals’ wish to maintain a self-perception that their behavior is reasonable, logical, rational, and appropriate (versus unreasonable, illogical, irrational, and inappropriate); b) individuals with a strong implicit motive (desire or need) to pursue a behavior will develop biased patterns of reasoning that facilitates the conclusion that their behavior was indeed reasonable (i.e., logical, rational, appropriate); c) biases that serve to enhance the logical appeal of motive-based behaviors may be referred to as “justification mechanisms”; and d) distinct clusters or sets of justification mechanisms will be associated with each implicit motive. The basic tenets of the conditional reasoning measurement system include: a) it is possible to measure the extent to which justification mechanisms influence (i.e., bias) reasoning by asking individuals to solve inductive reasoning problems; b) individuals with stronger levels of the implicit motive (e.g., motive to aggress) will be more likely to select solutions to reasoning problems that are derived from the motive-relevant justification mechanisms (e.g., hostile attribution bias, retribution bias, derogation of target bias); and c) conditional reasoning is said to occur when the likelihood of judging a solution to an inductive reasoning problem as “correct” depends on the personality (i.e., the implicit motives and accompanying justification mechanisms) of the respondent. The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: James M. LeBreton is co-owner of Stonerowe LLC, which currently holds the rights to the conditional reasoning items/tests developed by Lawrence R. James to measure aggression (CRT-A), achievement motivation (CRT-RMS), and power/leadership (CRT-L), including all translations thereof. Jeremy L. Schoen is co-owner of JL Innovations LLC, which currently holds the rights to the conditional reasoning items and test used to measure creative personality (CRT-CP), including all translations thereof.
“I’m Doing Okay”: Strengths and Resilience of Children With and Without ADHD

Background: The body of research directed at understanding the strengths and resilience of this population is growing. Research has indicated there are strengths for individuals with ADHD, and found factors important for promoting good outcomes. This study investigates positive qualities by examining the strengths and resilience of children with and without ADHD. Methods: The final sample included 56 children between the ages of 10 and 17 years (ADHD: n = 38; without ADHD: n = 18). Results: Children in both groups tended to report average levels of strengths and resilience except for school functioning, where significant differences were found between groups. Significant correlations between strengths and resilience for both groups were found. Only family involvement was not significantly correlated with resilience for the without ADHD group. Conclusions: Results from this study emphasize the importance of taking a strength-based perspective when working with children diagnosed with ADHD.
Fast Algorithm for Scalar Multiplication in Elliptic Curve Cryptography

Scalar multiplication is the basic operation in realizing elliptic curve cryptography,at the same time is the most time-consuming operation.Its efficiency determines the performance of ECC.This paper analyzes the fast realization of scalar multiplication from three aspects,and applies the minimum-weight Left-to-right Recoding Algorithm proposed by Marc Joye and Sung-Ming Yen to scalar multiplication.The new algorithm is particularly applicable in the case that we are required to operate at high speed when both computational ability and memory of integrate circuit are limited.
WaveletBaced Denoising by a New Thresholding Function

Based on the study of the disadvantages of the traditional threshold functionthe hard threshold function and the soft threshold function,we propose a new threshold function that can take the place of the traditional ones.The simulation results show that the new threshold function presents better MSE performance and SNR.Besides,the denoised signals using the new function also follow the original signal very closely.
Towards a better similarity algorithm for host-based intrusion detection system

Abstract An intrusion detection system plays an essential role in system security by discovering and preventing malicious activities. Over the past few years, several research projects on host-based intrusion detection systems (HIDSs) have been carried out utilizing the Australian Defense Force Academy Linux Dataset (ADFA-LD). These HIDS have also been subjected to various algorithm analyses to enhance their detection capability for high accuracy and low false alarms. However, less attention is paid to the actual implementation of real-time HIDS. Our principal objective in this study is to create a performant real-time HIDS. We propose a new model, “Better Similarity Algorithm for Host-based Intrusion Detection System” (BSA-HIDS), using the same dataset ADFA-LD. The proposed model uses three classifications to represent the attack folder according to certain criteria, the entire system call sequence is used. Furthermore, this work uses textual distance and compares five algorithms like Levenshtein, Jaro–Winkler, Jaccard, Hamming, and Dice coefficient, to classify the system call trace as attack or non-attack based on the notions of interclass decoupling and intra-class coupling. The model can detect zero-day attacks because of the threshold definition. The experimental results show a good detection performance in real-time for Levenshtein/Jaro–Winkler algorithms, 99–94% in detection rate, 2–5% in false alarm rate, and 3,300–720 s in running time, respectively.
Following the Leader in Multiplayer Tabletop Games

In a two-player zero-sum game, players classically want to maximise their chance of winning. When a game has more than two players, using the binary win rate as an objective is no longer such an obvious choice. A player might instead have the objective of doing as well as possible in terms of ranked order, or in maximising their score. We investigate the impact of different game-agnostic objectives in several popular tabletop games, and whether it can be better to use the game score as a proxy for winning. We find that the games considered largely fall into two groups. In one it is helpful to focus just on one’s own score during the game, and then shift to beating opponents only in the end-game. In the other, larger, group it is better to ‘Follow the Leader’ and constantly track one’s relative position to the opponents throughout the game. Using the score as a proxy enables rollout simulations in Monte Carlo (Tree) Search to be terminated early, and we investigate the optimal length of these. Games in which long-term planning is required, or when the full score is only known at the end of the game benefit from a full rollout, while games with adversarial counter-moves benefit from a short rollout length.
Towards a Human-in-the-Loop System for Authoring Game AI using Behavior Languages

As games get more complicated, Artificially Intelligent (AI) agents need to be better developed to understand and replicate complex, goal-oriented, reactive behaviors. Many existing behavior language approaches do not often make the AI’s behavior transparent and readable to the human, making it difficult to debug and update the behavioral model. In this workshop paper, we present a prototype visualization system that uses a tree-based abstraction to present complex agent behavior to a human user, allowing them to understand and edit the agent’s behavioral logic in a visual manner.
Believability, Anticipation, and... Timing Improving believability through timing manipulation

Having believable interactions between synthetic characters and with people is crucial to the creation of immersive interactive experiences. The believability of such experiences is strongly connected to having the characters express emotions with the correct timing throughout their interaction while retaining the human participants’ agency. Unfortunately, most video games adopt static animations, which usually only convey a single emotion throughout, making their expression look artificial, or allowing for greater emotion expressiveness but sacrificing the player’s agency. With this work, we propose the creation of a model based on the principles of animation and anticipation that allows for emotion expression during gameplay while retaining the participants’ agency. We evaluate our approach using three models. In the first model, characters express emotions in reaction to what happens (this is similar to how games approach emotion expressions), in the second in anticipation of what could happen, and in the third in reaction to what happens given what was anticipated. We tested these models in Adfectus, a 3D arena fighting game, and found improvements in some dimensions relating to the believability of interactions in particular player demographics.
Data Privacy Protection Method of Smart IOT Platform Based on Differential Privacy

In recent years, with 5G and other key technology breakthroughs, IoT technology is accelerating penetration into various industries, and the era of Internet of Everything is coming. However, while the industry is promoting the rapid development of industrial IoT, IoT security issues are also coming up. Based on this, this paper proposes a data privacy protection method for the smart IoT platform, which first adopts zero-knowledge identity proof technology to complete the identity access authentication of a large number of IoT terminal devices; then the data is downscaled and aggregated in the intermediate data collection device, and then the processed data is added to the noise based on Laplace distribution, and finally the data is transmitted to the main server, thus realizing the smart IoT platform Data privacy protection in the process of data transmission.
A Framework for Analogue Game-modification Learning: Guidelines to Lower Barriers for Games in Education

Academic interest in games as learning and teaching tools has directed much scholarly work towards gaining a better understanding of game-based learning and analyzing the experiences of both the educators and the learners. Our knowledge of the effective use of games as integral parts of educational approaches has increased, yet game-based learning has not displaced the instructional practice of teacher presentation to a significant extent, particularly in formal post-primary education. This paper will outline how games are generally used for educational purposes and describe reasons that hinder the wider adoption of game-based learning methodologies. Analogue game-modification learning (aGML) will be described as offering potential solutions to these hindrances through a framework that serves as a set of guidelines for efficient implementation.
Optomechanical interfaces for hybrid quantum networks

Recent advances on optical control of mechanical motion in an optomechanical resonator have stimulated strong interests in exploring quantum behaviors of otherwise classical, macroscopic mechanical systems and especially in exploiting mechanical degrees of freedom for applications in quantum information processing. In an optomechanical resonator, an optically- active mechanical mode can couple to any of the optical resonances supported by the resonator via radiation pressure. This unique property leads to a remarkable phenomenon: mechanically-mediated conversion of optical fields between vastly different wavelengths. The resulting optomechanical interfaces can play a special role in a hybrid quantum network, enabling quantum communication between disparate quantum systems. In this review, we introduce the basic concepts of optomechanical interactions and discuss recent theoretical and experimental progresses in this field. A particular emphasis is on taking advantage of mechanical degrees of freedom, while avoiding detrimental effects of thermal mechanical motion.
A Novel Method for Subsurface Damage Measurement of Optical Components

Subsurface damages produced in grinding process will influence optical performance and lifetime of optical elements.The testing for subsurface damage of polished surface is research hot and difficult point now.Combination of confocal image,tomography technology,microscope optics,optical scatter and weak signal processing,a novel method based on laser confocal microscope tomography is proposed.The effects of different sizes of pinhole on measure accuracy are analyzed,and the cross section microstructure picture of the subsurface damage sample is given.The subsurface damage depth is about 45 μm with this novel method.For the same K9 glass,the subsurface damage depth is about 50～55 μm with the chemical corrosion treatment technology.The measurement results are basically consistent.Comparison with the destructive etching method shows that this novel method is good for polished surface in quantitative and non-destructive.
Procedural generation of challenges for personalized gait rehabilitation

Conventional gait rehabilitation methods have the risk of alienating the patient due to their monotonous nature, thus negatively impacting the effectiveness of gait training. Modern technologies can help provide patients with better support, safety and immersive experience during training. However, physiotherapists cannot be required to master those technologies, nor to spend much time designing a more varied and engaging treatment for each patient. In this paper, we argue that adaptive gamified gait rehabilitation based on procedural content generation (PCG) can effectively support physiotherapists in achieving such customized outcomes. We propose a generic adaptation scheme to steer the generation of movement challenge levels based on player modeling and therapists’ intervention. Our approach features two difficulty adjustment strategies: parameter progression schemes and integration of multiple therapy goals. These strategies are applicable to the personalization of a wide range of gait rehabilitation goals. We implemented this approach in a standalone prototype for supporting gait training with the RYSEN system, a three-dimensional overground body weight support system. From our assessment with physiotherapists, we conclude that our PCG-based adaptive method effectively assists therapists in (i) offering a broad diversity in gait exercises to a wide group of patients, and (ii) dynamically tailoring challenge levels for a variety of gait tasks.
Blogger Feature Extraction Optimization Algorithm Based on K-means and DPCNN

Art toy has become very popular among young people in recent years, and its market influence is rising rapidly. Based on the data of Weibo app, this paper classifies and analyzes the relevant characteristics of art toy bloggers. K-means can realize the clustering of various features, while DPCNN can effectively process text information. Therefore, this paper proposes a blogger classification model based on K-means, and uses the inertial contour coefficient method to verify the validity of the classification results. A prediction model of cultural identity based on DPCNN is proposed. The accuracy of verification set is used to verify the validity of the model. The results show that the prediction accuracy of this model is 97.2%, which can predict the cultural identity of bloggers.
Fault Knowledge Acquisition of Aircraft Based on Event Extraction Technology

In order to make full use of aircraft fault information of total life cycle and construct a knowledge base for intelligent fault diagnosis, this paper preliminarily explored extracting fault knowledge from massive unstructured texts by using the event extraction technology. According to the business requirements of fault diagnosis, we defined the elements of fault events, including trigger words, and fault time, argument roles such fault time, fault occasion, fault unit, signal indicate. To extract above elements, three models, including fault event identification model, trigger extraction model and Argument extraction model is developed in this paper. The results show that this method is effective.
A Comparative Study on Partial Discharge Characteristics for XLPE Cable

The stable operation of cross-linked polyethylene (XLPE) cable is essential to the safety of the power system. At present, the ultra-low frequency test method can effectively detect insulation fault of XLPE cable with the advantage of lightweight. In this paper, four kinds of defect models, including uneven fracture, insulation scratch, interlayer pollution and semi-conductive layer residue, were manually made. Then their partial discharge (PD) characteristics, such as phase resolved partial discharge (PRPD) pattern, PD pulse waveform and pulse-time statistical pattern, were tested under ultra-low frequency voltage and power frequency voltage. Results show that PD characteristics demonstrate distinct differences for each defect, and features extracted from the statistical pattern are able to characterize these differences, which expands the evaluation method of insulation fault of XLPE cable.
Coding of time with non-linear mixed selectivity in Prefrontal Cortex ensembles

Previous work has identified stimulus specific time cells as a potential mechanism for working memory maintenance. It has been proposed that populations of stimulus specific sequences of cells could support memory for many items in a list over long periods of time. This would require information about one stimulus to persist after the presentation of subsequent stimuli. However, it is not known if sequences triggered by one stimulus persist past the presentation of additional stimuli. It is possible that each new stimulus terminates preceding sequences, making memory for multiple stimuli impossible. To investigate this question, we utilized a data set originally published by (Warden & Miller, 2010), studying the firing of monkey prefrontal neurons during short lists of stimuli. We were able to decode “what happened when” throughout the list, using linear discriminant analysis. Additionally, we were able to decode the first stimulus after the presentation of the second stimulus. Furthermore, we found that stimulus modulated sequences of cells, with discrete temporal fields, continue after the second item was presented. Much of the information about the previous item was carried by neurons that responded to conjunctions of stimuli and the timing of late-firing cells was synchronized to the firing of the second stimulus rather than the first. These properties falsify a simple linear model of sequential time cells. These results suggest that non-linear mixed selectivity extends to continuous variables such as time, but that in this experiment at least, only the timing of the most recent stimulus was explicitly maintained in ongoing firing.
Binary classification machine-learning Unveils Sex-Dependent mutated gene Signatures in Melanoma

There are significant differences in the prevalence of cancer type, primary tumor body site, and mutation load between men and women, but the mechanisms underlying these sex-dependent differences is mostly unknown. Here we used binary classification machine-learning methodology to study sex-correlated somatic mutations signatures in cutaneous melanoma. We identified a number of genes that are more frequently mutated in females compared to males. Mutations in two genes, LAMA2 and TPTE, together with a set of specific genes that are not mutated, can predict sex of melanoma patients. Over representation analysis of genes clustered with LAMA2 revealed significant enrichment in androgen and estrogen biosynthesis and metabolism pathways, suggesting that mutation of LAMA2 might be involved in biased sex hormone synthesis in melanoma. Taken together, our analysis shows that gender can be predicted based on mutation status of genes in melanoma and that certain mutations are predictive of survival beyond sex differences. Our results will lead to better diagnosis and more effective personalized treatment of melanoma. Summary It was observed that between men and women there is a significant difference in the prevalence of cancer type, the primary tumor body site, and the number of mutations found in a given tumor type. However, the mechanisms behind these gender differences are mostly unknown. To investigate sex corelated somatic mutation signatures in cutaneous melanoma we used binary classification machine-learning methodology. We identified specific genes that are more frequently mutated in females compared to males, including LAMA2 and TPTE, which are predictive of gender. We also found a significant enrichment in androgen and estrogen biosynthesis and metabolism pathways clustered with LAMA2, suggesting that mutation of LAMA2 might be involved in biased sex hormone synthesis in melanoma. We showed that gender can be predicted based on mutation status of genes in melanoma and that certain mutations are predictive of survival. Our findings could lead to better diagnosis and more effective personalized treatment of melanoma.
Modeling neurodevelopmental disorder-associated hAGO1 mutations in C. elegans Argonaute ALG-1

MicroRNAs (miRNA) are endogenous non-coding RNAs important for post-transcriptional regulation of gene expression. miRNAs associate with Argonaute proteins to bind to the 3’ UTR of target genes and confer target repression. Recently, multiple de novo coding variants in the human Argonaute gene AGO1 (hAGO1) have been reported to cause a neurodevelopmental disorder (NDD) with intellectual disability (ID). Most of the altered amino acids are conserved between the miRNA-associated Argonautes in H. sapiens and C. elegans, suggesting the hAGO1 mutations could disrupt evolutionarily conserved functions in the miRNA pathway. To investigate how the hAGO1 mutations may affect miRNA biogenesis and/or functions, we genetically modeled four of the hAGO1 de novo variants (referred to as NDD mutations) by introducing the identical mutations to the C. elegans hAGO1 homolog, alg-1. This array of mutations caused distinct effects on C. elegans miRNA functions, miRNA populations, and downstream gene expression, indicative of profound alterations in aspects of miRNA processing and miRISC formation and/or activity. Specifically, we found that the alg-1 NDD mutations cause allele-specific disruptions in mature miRNA profiles both in terms of overall abundances and association with mutant ALG-1. We also observed allele-specific profiles of gene expression with altered translational efficiency and/or mRNA abundance. The sets of perturbed genes include human homologs whose dysfunction is known to cause NDD. We anticipate that these cross-clade genetic studies may advance the understanding of fundamental Argonaute functions and provide insights into the conservation of miRNA-mediated post-transcriptional regulatory mechanisms.
Integrating multiple single-cell multi-omics samples with Smmit

Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells and in multiple samples, facilitate the study of gene expression and gene regulatory activities on a population scale. Existing integration methods can integrate either multiple samples or multiple modalities, but not both simultaneously. To address this limitation, we developed Smmit, a computational pipeline that leverages existing integration methods to simultaneously integrate both samples and modalities and produces a unified representation of reduced dimensions. We demonstrate Smmit’s capability to integrate information across samples and modalities while preserving cell type differences in two real datasets. Smmit is an R software package that is freely available at Github: https://github.com/zji90/Smmit