Miracle-dz/UniFAR-FTUs huggingface dataset:
Dealing with temporal indeterminacy in relational databases: An AI methodology

Time is pervasive of the human way of approaching reality, so that it has been widely studied in many research areas, including AI and relational Temporal Databases (TDB). While temporally imprecise information has been widely studied by the AI community, only few approaches have faced temporal indeterminacy (in particular, “don’t know exactly when” indeterminacy) in TDBs. Indeed, as we will show in this paper, the treatment of time in general, and of temporal indeterminacy in particular, involves the introduction of implicit forms of data representation in TDBs. As a consequence, we propose a new AI-style methodology to cope with temporal indeterminacy in TDBs. Specifically, we show that typical AI notions and techniques, such as making explicit the semantics of the representation formalism, and adopting symbolic manipulation techniques based on such a semantics, can be fruitfully exploited in the development of a “ principled ” treatment of indeterminate time in relational databases.
Improvement of position and orientation measurement algorithm of monocular vision based on circle features

This paper improves a position and orientation algorithm of monocular vision based on circular features.The information of laser rangerfinder is used to select correct one from two solutions of the equations.When the radius of a spatial circle is unkown,the improved algorithm can measure all parameters of circular position and orientation according to an image.Compared with previous algorithms,the improved algorithm is more convenient and quicker because it does not need moving the camera to take two pictures.A computational model is built up,and the simulations are carried out.The simulation results show that the improved algorithm is effective and can be applied to robot localization,space rendezvous and docking.
VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System

High Occupancy Vehicle/High Occupancy Tolling (HOV/HOT) lanes are operated based on voluntary HOV declarations by drivers. A majority of these declarations are wrong to leverage faster HOV lane speeds illegally. It is a herculean task to manually regulate HOV lanes and identify these violators. Therefore, an automated way of counting the number of people in a car is prudent for fair tolling and for violator detection.In this paper, we propose a Vehicle Passenger Detection System (VPDS) which works by capturing images through Near Infrared (NIR) cameras on the toll lanes and processing them using deep Convolutional Neural Networks (CNN) models. Our system has been deployed in 3 cities over a span of two years and has served roughly 30 million vehicles with an accuracy of 97% which is a remarkable improvement over manual review which is 37% accurate. Our system can generate an accurate report of HOV lane usage which helps policy makers pave the way towards de-congestion.
Optimization for Evolving Soft Robots in Diﬀerent Environments

Evolutionary robotics is an approach for optimizing a robotic control system and structure based on the bio-inspired mechanism of adaptiogenesis. Conventional evolutionary robotics assigns a task and an evaluation to a virtual robot and acquires an optimal control system. In many cases, however, the robot is composed of a few rigid primitives and the morphology imitates that of real animals, insects, and artifacts. This paper proposes a novel approach to evolutionary robotics combining morphological evolution and soft robotics to optimize the control system of a soft robot. Our method calculates the relational dynamics among morphological changes and autonomous behavior for neuro-evolution (NE) with the development of a complex soft-bodied robot and the accomplishment of multiple tasks. We develop a soft-bodied robot composed of heterogeneous materials in two stages: a development stage and a loco-motion stage, and we optimize these robotic structures by combining an artiﬁcial neural network (ANN) and age-ﬁtness pareto optimization (AFP). These body structures of the robot are determined depending on three genetic rules and some voxels for evolving the ANN. In terms of our experimental results, our approach enabled us to develop some adaptive structural robots that simultaneously acquire behavior for crawling both on the ground and underwater. Subsequently, we discovered an unintentional morphology and behavior (e.g., walking, swimming, and crawling) of the soft robot through the evolutionary process. Some of the robots have high generalization ability with the ability to crawl to any target in any direction by only learning a one-directional crawling task.
Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks

Community detection is a fundamental problem in network science with various applications. The problem has attracted much attention and many approaches have been proposed. Among the existing approaches are the latest methods based on Graph Convolutional Networks (GCN) and on statistical modeling of Markov Random Fields (MRF). Here, we propose to integrate the techniques of GCN and MRF to solve the problem of semi-supervised community detection in attributed networks with semantic information. Our new method takes advantage of salient features of GNN and MRF and exploits both network topology and node semantic information in a complete end-to-end deep network architecture. Our extensive experiments demonstrate the superior performance of the new method over state-of-the-art methods and its scalability on several large benchmark problems.
Paraphrase Diversification Using Counterfactual Debiasing

The problem of generating a set of diverse paraphrase sentences while (1) not compromising the original meaning of the original sentence, and (2) imposing diversity in various semantic aspects, such as a lexical or syntactic structure, is examined. Existing work on paraphrase generation has focused more on the former, and the latter was trained as a fixed style transfer, such as transferring from positive to negative sentiments, even at the cost of losing semantics. In this work, we consider style transfer as a means of imposing diversity, with a paraphrasing correctness constraint that the target sentence must remain a paraphrase of the original sentence. However, our goal is to maximize the diversity for a set of k generated paraphrases, denoted as the diversified paraphrase (DP) problem. Our key contribution is deciding the style guidance at generation towards the direction of increasing the diversity of output with respect to those generated previously. As pre-materializing training data for all style decisions is impractical, we train with biased data, but with debiasing guidance. Compared to state-of-the-art methods, our proposed model can generate more diverse and yet semantically consistent paraphrase sentences. That is, our model, trained with the MSCOCO dataset, achieves the highest embedding scores, . 94/. 95/. 86, similar to state-of-the-art results, but with a lower mBLEU score (more diverse) by 8.73%.
Implementation of Boolean AND and OR Logic Gates with Biologically Reasonable Time Constants in Spiking Neural Networks

Latest developments in the field of power-efficient neural interface circuits provide an excellent platform for applications where power consumption is the primary concern. Developing neural networks to achieve pattern recognition on such hardware remains a daunting task owing to substantial computational complexity. We propose and demonstrate a Spiking Neural Network (SNN) with biologically reasonable time constants to implement basic Boolean Logic Gates. The same network can be further applied to more complex problem statements. We employ a frequency spike encoding for data representation in the model, and a simplified and computationally efficient model of a neuron with exponential synapses and Spike Timing Dependent Plasticity (STDP).
Coupling agent motivations and spatial behaviors for authoring multiagent narratives

Authoring behavior narratives for heterogeneous multiagent virtual humans engaged in collaborative, localized, and task‐based behaviors can be challenging. Traditional behavior authoring frameworks are either space‐centric, where occupancy parameters are specified; behavior‐centric, where multiagent behaviors are defined; or agent‐centric, where desires and intentions drive agents' behavior. In this paper, we propose to integrate these approaches into a unique framework to author behavior narratives that progressively satisfy time‐varying building‐level occupancy specifications, room‐level behavior distributions, and agent‐level motivations using a prioritized resource allocation system. This approach can generate progressively more complex and plausible narratives that satisfy spatial, behavioral, and social constraints. Possible applications of this system involve computer gaming and decision‐making in engineering and architectural design.
Development of an Intelligent Job Recommender System for Freelancers using Client's Feedback Classification and Association Rule Mining Techniques

Most of the freelancer's time is killed in finding suitable jobs due to the huge number of freelance marketplaces. Freelancing sites send email notifications or show in newsfeed about posted jobs but most of them are irrelevant. Recommending relevant jobs to freelancers to minimize job finding time has drawn the attraction of researchers. Here, in this paper, we propose a recommender system to find out appropriate jobs for freelancers using client’s feedback classification and Association rule mining techniques. After collecting the previous work history of freelancers, we analyze the sentiment of client's feedback using Logistic Regression and Linear Support Vector Machine model to classify the completed jobs into two categories: positive and negative. We apply the Association rule mining technique to find out freelancer's frequent skillsets used in both categories of completed jobs. Then, we find out the jobs matched with the positive frequent skillsets using set operations. We also discard jobs that contain negative frequent skillsets. Finally, a collaborative filtering algorithm is applied considering the client's overall rating, the minimum budget/ hourly rate, deadline, re-hire, etc. to generate a more accurate recommendation. After extensive experiments on the real dataset collected from different online marketplaces, we are able to prove that our proposed method correctly recommends the appropriate jobs with 83.40% (Logistic Regression) and 84.03% (Linear SVM) accuracy.
Robust Phoneme Recognition with Little Data

A common belief in the community is that deep learning requires large datasets to be effective. We show that with careful parameter selection, deep feature extraction can be applied even to small datasets.We also explore exactly how much data is necessary to guarantee learning by convergence analysis and calculating the shattering coefficient for the algorithms used. Another problem is that state-of-the-art results are rarely reproducible because they use proprietary datasets, pretrained networks and/or weight initializations from other larger networks. We present a two-fold novelty for this situation where a carefully designed CNN architecture, together with a knowledge-driven classifier achieves nearly state-of-the-art phoneme recognition results with absolutely no pretraining or external weight initialization. We also beat the best replication study of the state of the art with a 28% FER. More importantly, we are able to achieve transparent, reproducible frame-level accuracy and, additionally, perform a convergence analysis to show the generalization capacity of the model providing statistical evidence that our results are not obtained by chance. Furthermore, we show how algorithms with strong learning guarantees can not only benefit from raw data extraction but contribute with more robust results. 2012 ACM Subject Classification Computing methodologies → Speech recognition
Early-Stopping of Scattering Pattern Observation with Bayesian Modeling

This paper describes a new machine-learning application to speed up Small-angle neutron scattering (SANS) experiments, and its method based on probabilistic modeling. SANS is one of the scattering experiments to observe microstructures of materials; in it, two-dimensional patterns on a plane (SANS pattern) are obtained as measurements. It takes a long time to obtain accurate experimental results because the SANS pattern is a histogram of detected neutrons. For shortening the measurement time, we propose an earlystopping method based on Gaussian mixture modeling with a prior generated from B-spline regression results. An experiment using actual SANS data was carried out to examine the accuracy of the method. It was confirmed that the accuracy with the proposed method converged 4 minutes after starting the experiment (normal SANS takes about 20 minutes).
A Machine Learning Technique to Analyze Surface EMG Signals in Normal and Diabetic Subjects

The diabetes mellitus results in many musculoskeletal complications and detection of these problems is important in treatment, prevention of disability and improving the quality of life. Many attempts have been made to effectively acquire and analyze the surface electromyogram (sEMG) signals to understand the musculoskeletal problems in diabetes. The development of methodologies to extract the effective features from sEMG still remains a primary challenge. Previous studies have demonstrated that the sEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time-series analysis and the time-frequency domain methods, we propose the wavelet based method to extract the effective features of sEMG signals. The sEMG signals were first acquired and analyzed by the wavelet transform and the features were obtained. Then, these features were used as the input vectors to artificial neural network (ANN) classifier to discriminate diabetic or non-diabetic subject. The results show that significance of proposed feature extraction method with classification of diabetic and non-diabetic subjects with an accuracy of 97.06%. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of sEMG signals and is suitable for classifying the normal subjects from diabetic subjects. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.
Development of Cell Penetrating Bax Inhibiting Peptides (BIP)

By JOSE A. GOMEZ Bax is a pro-apoptotic protein that mediates intrinsic cell-death signaling. Using a yeastbased functional screening approach, interferon gamma receptor beta chain (IFNγR2) and the DNA repair protein Ku70 were identified as Bax suppressors. IFNγR2 is a component of the IFNγ receptor complex along with the IFNγR alpha chain (IFNγR1). Upon IFNγ binding, a conformational change in the receptor complex occurs that activates the Jak2/STAT1 signaling cascade. The Bax binding domain is located in the C-terminal region IFNγR2296-337. This portion does not contain the Jak2-binding domain; therefore, the antiapoptotic function of IFNγR2 is independent of JAK/STAT signaling. IFNγR2296-337 rescued human cells from apoptosis induced by Bax overexpression but not Bak. IFNγR2 inhibited apoptosis induced by the BH3-only protein Bim-EL, suggesting that IFNγR2 inhibits Bax activation induced by a BH3-only protein. Bax and IFNγR2 were co-immunoprecipitated from cell lysates prepared from HEK293 and DAMI cells. Addition of recombinant Bcl-2 protein to cell lysates significantly reduced the interaction of IFNγR2 and Bax, suggesting that Bcl-2 and IFNγR2 bind a similar domain of Bax.
Proposal of a new mouse with realtime Mickey Ratio adjuster controlled by grasping force

According to Fitts' law, pointing tasks are easier when the target is larger and the distance is shorter. A large Mickey Ratio provides a short distance but a small target, whereas a small Mickey Ratio means a large target but a long distance. This paper proposes a realtime adaptive Mickey Ratio adjustment for a solution to this dilemma.
Application of meta-heuristic interval optimization algorithms to technical system design problems

This article provides information about meta-heuristic optimization algorithms which use interval analysis. Strategies of interval genetic algorithm and adaptive interval algorithm are given. Efficiency of the developed algorithms is demonstrated by the solution of technical system design problems: pressure vessel and speed reducer designs. Mathematics Subject Classification: 90C30, 65G99
DDoS Attacks and Their Types

Billions of people rely on internet to discover and share ideas with the world. However, the websites are vulnerable to deliver the attacks, preventing people to access them. The recent study of global surveys showed that DDoS Attacks evolved in strategy and tactics. A Distributed Denial of Service (DDoS) attack is a new emerging bigger threat that target organization’s business critical services such as e-commerce transactions, financial trading, email or web site access. A DDoS attack is a large-scale, coordinated attack on the availability of services of a victim system or network resource, launched indirectly through many compromised computers on the Internet. To create attacks, attackers first discover vulnerable sites or hosts on the network. Then vulnerable hosts are exploited by attackers who use their vulnerability to gain access to these hosts. This chapter deals with the introduction, architecture and classification of DDoS Attacks.
Marked Enhancement of Roll-Off Frequency in FeCoN Synthetic Antiferromagnetic Films Deposited by Oblique Incidence

A series of FeCoN films were successfully deposited on glass substrates in a magnetron sputtering system. Using oblique incidence method and FeCoN/Ru/FeCoN synthetic antiferromagnetic (SAF) structure, two additional anisotropies energy were introduced: oblique incidence anisotropy and exchange anisotropy energy, which marked enhancement of the effective magnetic anisotropy (Hk). The increment of Hk results in a significant improvement in the roll-off frequency of these films. The roll-off frequency of FeCoN/Ru/FeCoN films with SAF structure can reach up to 8.6 GHz. A feasible approach to conveniently controlling Hk of soft magnetic thin films by using oblique deposition and SAF structure can further improve their properties for the potential applications in the high frequency region.
A Randomized, Double-blind, Active-controlled Phase III Trial of a Cell Culture-derived Quadrivalent Inactivated Influenza Vaccine in Healthy South Korean Children and Adolescents 6 Months to 18 Years of Age.

BACKGROUND Cell culture-derived influenza vaccines have several important advantages over egg-based influenza vaccines. The quadrivalent influenza vaccine may offer broader protection against seasonal influenza than trivalent influenza vaccine by containing 1 more B strain. The purpose of this study was to evaluate the immunogenicity and safety of NBP607-QIV, a novel cell culture-derived inactivated quadrivalent influenza vaccine (cIIV4), in children and adolescents. METHODS This phase III, randomized, double-blind, multicenter trial in children/adolescents (6 mo to 18 yr) was conducted in South Korea during 2014-2015 season. Subjects were randomized 4:1 to receive either NBP607-QIV or control inactivated trivalent influenza vaccine. Hemagglutination inhibition antibody titers were assessed in prevaccination and 28 days postvaccination sera. Safety data were collected for up to 6 months postvaccination. RESULTS A total of 454 participants completed the study. Three-hundred sixty-six subjects received cIIV4 and 88 subjects received inactivated trivalent influenza vaccine. Overall, NBP607-QIV met the immunogenicity criteria of Committee for Medicinal Products for Human Use for each of the 4 strains. Between the NBP607-QIV and control groups, immunogenicity endpoints were comparable. Participants younger than 3 years of age had lower immunologic responses to 2 influenza B strains in both NBP607-QIV and control group. No deaths, vaccine-related serious adverse events (AEs) or withdrawals because of AEs were reported. The solicited AEs reported were generally of mild intensity. CONCLUSIONS NBP607-QIV, a novel cIIV4, showed good immunogenicity to all 4 influenza strains and had tolerable safety profiles in children and adolescents. Moreover, NBP607-QIV was more immunogenic against influenza B compared to the control, an egg-based subunit vaccine.
Probiotics in Food Systems: Significance and Emerging Strategies Towards Improved Viability and Delivery of Enhanced Beneficial Value

Preserving the efficacy of probiotic bacteria exhibits paramount challenges that need to be addressed during the development of functional food products. Several factors have been claimed to be responsible for reducing the viability of probiotics including matrix acidity, level of oxygen in products, presence of other lactic acid bacteria, and sensitivity to metabolites produced by other competing bacteria. Several approaches are undertaken to improve and sustain microbial cell viability, like strain selection, immobilization technologies, synbiotics development etc. Among them, cell immobilization in various carriers, including composite carrier matrix systems has recently attracted interest targeting to protect probiotics from different types of environmental stress (e.g., pH and heat treatments). Likewise, to successfully deliver the probiotics in the large intestine, cells must survive food processing and storage, and withstand the stress conditions encountered in the upper gastrointestinal tract. Hence, the appropriate selection of probiotics and their effective delivery remains a technological challenge with special focus on sustaining the viability of the probiotic culture in the formulated product. Development of synbiotic combinations exhibits another approach of functional food to stimulate the growth of probiotics. The aim of the current review is to summarize the strategies and the novel techniques adopted to enhance the viability of probiotics.
Genome-wide discovery of the daily transcriptome, DNA regulatory elements and transcription factor occupancy in the monarch butterfly brain

The Eastern North American monarch butterfly, Danaus plexippus, is famous for its spectacular seasonal long-distance migration. In recent years, it has also emerged as a novel system to study how animal circadian clocks keep track of time and regulate ecologically relevant daily rhythmic activities and seasonal behavioral outputs. However, unlike in Drosophila and the mouse, little work has been undertaken in the monarch to identify rhythmic genes at the genome-wide level and elucidate the regulation of their diurnal expression. Here, we used RNA-sequencing and Assay for Transposase-Accessible Chromatin (ATAC)-sequencing to profile the diurnal transcriptome, open chromatin regions, and transcription factor (TF) footprints in the brain of wild-type monarchs and of monarchs with impaired clock function, including Cryptochrome 2 (Cry2), Clock (Clk), and Cycle-like loss-of-function mutants. We identified 217 rhythmically expressed genes in the monarch brain; many of them were involved in the regulation of biological processes key to brain function, such as glucose metabolism and neurotransmission. Surprisingly, we found no significant time-of-day and genotype-dependent changes in chromatin accessibility in the brain. Instead, we found the existence of a temporal regulation of TF occupancy within open chromatin regions in the vicinity of rhythmic genes in the brains of wild-type monarchs, which is disrupted in clock deficient mutants. Together, this work identifies for the first time the rhythmic genes and modes of regulation by which diurnal transcription rhythms are regulated in the monarch brain. It also illustrates the power of ATAC-sequencing to profile genome-wide regulatory elements and TF binding in a non-model organism for which TF-specific antibodies are not yet available.