Miracle-dz/UniFAR-FTUs huggingface dataset:
Inductive Logic Programming meets Relational Databases : An Application to Statistical Relational Learning

With the increasing amount of relational data, scalable approaches to faithfully model this data have become increasingly important. Statistical Relational Learning (SRL) approaches have been developed to learn in presence of noisy relational data by combining probability theory with first order logic. However most learning approaches for these models do not scale well to large datasets. While advances have been made on using relational databases with SRL models (Niu et al. 2012), they have not been extended to handle the complex model learning (structure learning task). We present a scalable structure learning approach that combines the benefits of relational databases with search strategies that employ rich inductive bias from Inductive Logic Programming. We empirically show the benefits of our approach on boosted structure learning for Markov Logic Networks.
DDoS Attack Detection Using Flow Entropy and Clustering Technique

Distributed Denial of Service (DDoS) has already been one of the most serious threats to network security, and entropy-based approaches for DDoS attack detection are appealing since they provide more detailed insights than traditional traffic volume-based methods. In this paper, we propose a novel entropy-based DDoS attack detection approach by constructing entropy vectors of different features from traffic flows, modeling normal patterns using clustering analysis algorithm, and then detecting deviations from the created models. Compared to traditional work, our method differs by selecting more comprehensive features to construct clustering models, and setting detection thresholds automatically based on the traffic models. The experimental results demonstrate that the proposed approach can not only outperform traditional methods in terms of detection accuracy, but also show certain availability in actual application environment.
Treatment with Rhizoma Dioscoreae Extract Has Protective Effect on Osteopenia in Ovariectomized Rats

The aims of this study were to evaluate the osteoprotective effect of aqueous extract from Rhizoma Dioscoreae (RDE) on rats with ovariectomy- (OVX-) induced osteopenia. Our results show that RDE could inhibit bone loss of OVX rats after a 12-week treatment. The microarray analysis showed that 68 genes were upregulated and that 100 genes were downregulated in femurs of the RDE group rats compared to those in the OVX group. The Ingenuity Pathway Analysis (IPA) showed that several downregulated genes had the potential to code for proteins that were involved in the Wnt/β-catenin signaling pathway (Sost, Lrp6, Tcf7l2, and Alpl) and the RANKL/RANK signaling pathway (Map2k6 and Nfatc4). These results revealed that the mechanism for an antiosteopenic effect of RDE might lie in the synchronous inhibitory effects on both the bone formation and the bone resorption, which is associated with modulating the Wnt/β-catenin signaling and the RANKL/RANK signaling.
Problem Evolution: A new approach to problem solving systems

In this paper we present a novel tool to evaluate problem solving systems. Instead of using a system to solve a problem, we suggest using the problem to evaluate the system. By finding a numerical representation of a problem's complexity, one can implement genetic algorithm to search for the most complex problem the given system can solve. This allows a comparison between different systems that solve the same set of problems. In this paper we implement this approach on pattern recognition neural networks to try and find the most complex pattern a given configuration can solve. The complexity of the pattern is calculated using linguistic complexity. The results demonstrate the power of the problem evolution approach in ranking different neural network configurations according to their pattern recognition abilities. Future research and implementations of this technique are also discussed.
CUBu: Universal Real-Time Bundling for Large Graphs

Visualizing very large graphs by edge bundling is a promising method, yet subject to several challenges: speed, clutter, level-of-detail, and parameter control. We present CUBu, a framework that addresses the above problems in an integrated way. Fully GPU-based, CUBu bundles graphs of up to a million edges at interactive framerates, being over 50 times faster than comparable state-of-the-art methods, and has a simple and intuitive control of bundling parameters. CUBu extends and unifies existing bundling techniques, offering ways to control bundle shapes, separate bundles by edge direction, and shade bundles to create a level-of-detail visualization that shows both the graph core structure and its details. We demonstrate CUBu on several large graphs extracted from real-life application domains.
An improved scheme for privacy-preserving collaborative anomaly detection

The ubiquity of mobile sensing devices in the Internet of Things (IoT) enables an emerging data crowdsourcing paradigm called participatory sensing, where multiple individuals collect data and use a cloud service to analyse the union of the collected data. An example of such collaborative analysis is collaborative anomaly detection. Given the possibility that the cloud service is honest but curious, a major challenge is how to protect the participants' privacy. The scheme called Random Multiparty Perturbation (RMP) addresses this challenge by allowing each participant to perturb his/her tabular data by passing the data through a nonlinear function, and projecting the data to a lower dimension using a participant-specific random matrix. Here, we propose an improvement to RMP by introducing a new nonlinear function. The improved scheme is assessed in terms of its recovery resistance to the maximum a priori (MAP) estimation attack. Experimental results and preliminary theoretical analysis indicate that RMP is resistant to collusion attacks and has better recovery resistance to MAP estimation attacks compared to the original scheme. It also achieves a good trade-off between accuracy and privacy.
Eye location method based on Haar features

Eye location is the imported reference of face normalization in face recognition system. This paper describes a novel method to improve the eye location accuracy in the face image, which detected eyes by combining Haar features, dual-orientation Gabor filters and eye templates. First, introduce Haar features to detect face region from face images, and then detect the binocular region based on Haar features in the face region. Finally accurately locate eye coordinates by combining dual-orientation Gabor filters and eye templates from the preliminary binocular region or face region. The experimental results on images of Face Recognition Technology (FERET) database demonstrate that this proposed method was able to achieve a location accuracy of 86.34% in 1477∗2 eyes, and it is effective in video images by experiments. Using this method can enhance the performance of face detection and recognition in the video surveillance system.
Air Pollution Exposure Estimation and Finding Association with Human Activity using Wearable Sensor Network

Air quality and pollution monitoring services are provided by many countries and cities. However, individuals are more concerned about personal exposure and dosage, which can rarely be estimated due to the low spatial resolution of air pollution data and lack of personal data. In recent years, an increasing number of research groups, including ours, have focused on increasing the spatial resolution of air pollution data using ubiquitous sensor networks. These works did raise the spatial granularity compared with data from fixed air pollution monitoring sites. In this paper, we combine air pollution and human energy expenditure data to give individuals real-time personal air pollution exposure estimates. In particular, this paper describes our experiences with developing a personal air pollution exposure estimation system utilising participatory air pollution monitoring system and energy expenditure data collected from wearable activity sensors. Our system and applications will benefit the understanding of the relationship between air pollution exposure and personal health. We also conducted a trial to get a full day's air pollution inhalation dosage for one participant, and applied multiple data mining techniques to find out associations between activity mode, location, and the inhaled pollution. Results show that sleep, having meals, working in a campus, and general home activities like reading books will lead to a low air pollution dosage, while working out, walking and driving will cause higher inhaled dose. Furthermore, classification results in our study based on activity modes, locations and dosage data which is collected in the trial show that up to 94% classification accuracy can be achieved.
EFEKTIVITAS TEKNIK MODELING UNTUK MENINGKATKAN EMPATI MAHASISWA PRODI BK UNIVERSITAS AHMAD DAHLAN

Student college of guidance and counseling program as a counselors candidate is required to have concern for others, including empathy. This study aims to determine effectiveness modeling techniques to increase empathy in students majoring in guidance and counseling University of Ahmad Dahlan in Yogyakarta. The method used in this research is pre experiment with one group pretest-posttest with sampling through purposive sampling technique. The samples are 10 students majoring in guidance and counseling Ahmad Dahlan University Yogyakarta. Data collected by using a scale of empathy that has been proven valid and reliable and by using observation sheet of communicative empathy. The results showed that modeling techniques can effectively increase empathy in students. The results of Wilcoxon statistical test showed that the value of Z = -2810 with p = 0.005 (<0.05), which means there is a significant difference level of empathy before treatment (pre-test) and after the treatment (post-test). The results could be used as consideration for educators in universities in order to develop empathy in students.
Application of Boolean expression minimization to learning via hierarchical generalization

Concept learning through hierarchical generalization is an important technique in machine learning. The main result of this paper shows that this particular type of learning can be done using the well-known technique of boolean expression minimization. The boolean formulation unifies the various techniques suggested previously for hierarchical generalizations. It gives better conceptual clarity and a computationally efficient method for this type of learning. In particular, learning from relational databases can also be cast in the framework of boolean minimization.
Protection against Th17 Cells Differentiation by an Interleukin-23 Receptor Cytokine-Binding Homology Region

Th17 cells have been reported to produce proinflammatory cytokines like Interleukin-17, IL-22, and regarded as important players in various inflammatory diseases. One of the IL-12 cytokine family cytokines, IL-23, composed of p19 and p40 subunit, is known for its potential to promote Th17 development and IL-17 producing, and the IL-23/IL-17 pathway is considered to be potential therapeutic target for autoimmune inflammation responses. Knockout mice deficient in either IL-23 or IL-17 related genes can suppress the allergic responses. Several IL-23 or IL-17 neutralizing agents are being evaluated in vitro or in vivo to disrupt the IL-23/IL-17 axis. Herein, we report that prokaryotically expressed soluble IL-23 receptor cytokine-binding homology region as an endogenous extracellular receptor analogue could be a natural antagonist against IL-23/IL-17 axis. We provide evidence that IL23R-CHR can bind to IL-23 in a dose-dependent manner in vitro, and block IL-23 signal by IL23R-CHR reducing the RORγt expression, which in turn lowers the expression of IL-17/IL-22, thus protecting naive CD4+ T cells against Th17 development. Together, this study indicates the importance of IL-23 pathway in Th17 development and the negative regulation of Th17 development by IL23R-CHR, and highlights the important roles of the soluble receptor extracellular region in the therapeutic strategy of neutralizing IL-23.
The Effect of the Prison Industry Enhancement Certification Program on Labor Market Outcomes of Prison Releasees

This article examines the effects of the federal Prison Industry Enhancement Certificate Program (PIE) on unemployment duration, length of employment, and earnings of inmates released between 1996 and 2001 across 5 states. This is the first nationally representative dataset of PIE and this is the first comprehensive study to analyze the effect of this program on labor market outcomes of the inmate. The results indicate that the PIE program may significantly decrease unemployment duration and increase the length of employment duration for both men and women. In addition, the PIE program may also significantly increase employment and earnings of the former male inmate.
Waddington, Dynamic Systems, and Epigenetics

Waddington coined the term “epigenetic” to attempt to explain the complex, dynamic interactions between the developmental environment and the genome that led to the production of phenotype. Waddington's thoughts on the importance of both adaptability and canalization of phenotypic development are worth recalling as well, as they emphasize the available range for epigenetic action and the importance of environmental feedback (or lack thereof) in the development of complex traits. We suggest that a dynamic systems view fits well with Waddington's conception of epigenetics in the developmental context, as well as shedding light on the study of the molecular epigenetic effects of the environment on brain and behavior. Further, the dynamic systems view emphasizes the importance of the multi-directional interchange between the organism, the genome and various aspects of the environment to the ultimate phenotype.
An exploration from virtual to augmented reality gaming

Computer games are continuously improving graphics capabilities and game play, but the market demands show that more compelling gaming applications are required. In this article, the requirements of modern gaming applications are investigated and a classification of the most significant game design issues is presented. To understand the issues related to video and virtual reality gaming, an interactive game engine is designed and, as a case study, a traditional two-dimensional arcade game, called Breakout, is ported. Collision detection is supported between the graphics components of the application based on Newtonian laws of physics. To test the effectiveness of our approach, a tangible platform for playing interactive three-dimensional games using video see-through augmented reality techniques is proposed. To evaluate the effectiveness of each application, a pilot study was performed and the initial results of this study are presented.
The effects of protracted graphomotor tasks on tripod pinch strength and handwriting performance in children with dysgraphia

Purpose. To examine the impact of prolonged graphomotor tasks on tripod-pinch strength and on handwriting process and product measures of children with dysgraphia and typical peers. Method. Participants were 51 children in third to fifth grades, divided into two groups: 23 children with dysgraphia and 28 typical peers, as determined by the Handwriting Proficiency Screening Questionnaire. The procedure included two sessions, with a 15-min break between sessions. In each session, the participants performed two tasks: the visual-motor control subtest of Bruininks-Oseretsky and a handwriting copying task, both performed on an electronic tablet as part of the Computerised Penmanship Evaluation Tool. Tripod pinch strength was evaluated before and after each session. Results. Significantly lower tripod-pinch strength was found among children with dysgrphia in comparison to typically developed peers. This deterioration in tripod-pinch strength was associated with a significant deterioration in handwriting process and product measures along the protracted task. Conclusions. Pinch force that is required for mastering the handwriting tool needs to be considered in the evaluation process of children with dysgraphia. Combining the pinch factor with an evaluation of handwriting's process and product may supply a better insight about the child's deficits and assist in focusing treatment objectives.
Tilburg University Centrality in Policy Network Drawings

We report on first results of a cooperation aiming at the usage of raph drawing techniques to convey domain-specific information c tained in policy or, more general, social networks. Policy network analysis is an approach to study policy makin g processes, structures and outcomes, thereby concentrating on the analysis o f relations between policy actors. An important operational concept for the ana lysis of policy networks is centrality, i.e. the distinction of actors accordi ng to their importance in a relational structure. Matching structural with geometric centrality we incorporate the aggregated values of centrality measures into a layout m del of the network.
Improving HEVC intra prediction with PDE-based inpainting

In this paper, we propose an intra prediction algorithm for High Efficiency Video Coding (HEVC) to improve its coding efficiency. In the suggested technique, in addition to the standard intra prediction we also incorporate some of the partial differential equations (PDEs) based inpainting modes into HEVC for intra prediction. The experimental results reveal that average 4.2% BDBR reduction can be achieved in the proposed algorithm, compared to the standard intra prediction in HEVC. The results also demonstrate that the proposed algorithm outperforms other distinct algorithms.
A fundamental characterization of stability in broadcast queueing systems

Stability with respect to a given scheduling policy has become an important issue for wireless communication systems; but hard to prove in particular scenarios. In this paper two sufficient conditions for stability in a broadcast setting are derived, which are often very easy to check. Moreover, it is shown that if the given scheduling policy complies with both conditions the resulting throughput region of the policy equals the ergodic achievable rate region and the system is stable in a strong sense. This extends results presented in [1] where the statement was shown for scheduling policies fulfilling so-called integrability condition. Additionally, in this paper we also present a partial converse to the statement which is demonstrated by an application example.
The Interplay Between Stability and Regret in Online Learning

This paper considers the stability of online learning algorithms and its implications for learnability (bounded regret). We introduce a novel quantity called {\em forward regret} that intuitively measures how good an online learning algorithm is if it is allowed a one-step look-ahead into the future. We show that given stability, bounded forward regret is equivalent to bounded regret. We also show that the existence of an algorithm with bounded regret implies the existence of a stable algorithm with bounded regret and bounded forward regret. The equivalence results apply to general, possibly non-convex problems. To the best of our knowledge, our analysis provides the first general connection between stability and regret in the online setting that is not restricted to a particular class of algorithms. Our stability-regret connection provides a simple recipe for analyzing regret incurred by any online learning algorithm. Using our framework, we analyze several existing online learning algorithms as well as the "approximate" versions of algorithms like RDA that solve an optimization problem at each iteration. Our proofs are simpler than existing analysis for the respective algorithms, show a clear trade-off between stability and forward regret, and provide tighter regret bounds in some cases. Furthermore, using our recipe, we analyze "approximate" versions of several algorithms such as follow-the-regularized-leader (FTRL) that requires solving an optimization problem at each step.
A dynamic bandwidth guaranteed routing using heuristic search for clustered topology

The increased popularity of the Internet in recent years, has witnessed an upsurge in the number of traffic types (e.g. Multimedia). This has led to dynamic traffic behaviour that has to be efficiently managed by the Internet service providers (ISPs) in coordination with the underlying carrier network. In this paper, we propose a genetic algorithm based routing solution for carrier networks that efficiently supports multiple ISP's dynamic traffic demands. The solution is based on the genetic algorithm-local search hybrid (GA-LS) algorithm that is executed on a clustered network topology. Performance evaluations have also been presented to demonstrate comparisons with existing evolutionary algorithms.
Study on Tele-immersive Communication with Multi-video Streaming on Tiled Display Wall

In tele-communication via WAN, the existence of participant by video streaming in real scene is important. In addition, we consider that non-verbal communication play an important role. In order to realize realistic communication between remote places, the display of video image in remote site with the large-scale screen is effective. However, the video image on wide-area display with a projector and large screen is low-resolution, and sufficient presence is not obtained. In past, we have constructed tele-immersive environment with tiled display wall to realize realistic remote communication with video streaming. However, the video image by single small camera is difficult to display with realistic high-resolution on tiled display wall, because there is a limit in the display resolution of video image captured by a small camera. In this paper, we have proposed the technique to realize high-speed display of realistic high-resolution video image on tiled display wall by transmitting each video image data which captured by multiple cameras and compositing as a high-resolution video image. From the experimental result, the video image could display more high-quality than expanded video image by single small camera. In addition, we have measured the network throughput performance for each transmission video in the system, and steady throughput performance has been gained in the case of each video image.
State space geometry of a spatio-temporally chaotic Kuramoto-Sivashinsky flow

The continuous and discrete symmetries of the Kuramoto-Sivashinsky system restricted to a spatially periodic domain play a prominent role in shaping the invariant sets of its spatiotemporally chaotic dynamics. The continuous spatial translation symmetry leads to relative equilibria (traveling wave) and relative periodic orbit solutions. The discrete symmetries lead to existence of equilibrium and periodic orbit solutions, induce decomposition of state space into orthogonal invariant subspaces, and enforce certain structurally stable heteroclinic connections between equilibria. We show, on example of a particular small-cell Kuramoto-Sivashinsky system, how the geometry of its dynamical state space is organized by a rigid ‘cage’ built by heteroclinic connections between equilibria, and demonstrate the preponderance of unstable relative periodic orbits and their likely role as the skeleton underpinning spatiotemporal turbulence in systems with continuous symmetries. We also offer novel visualizations of the high-dimensional Kuramoto-Sivashinsky state space flow through projections onto low-dimensional, PDE representation independent, dynamically invariant intrinsic coordinate frames, as well as in terms of the physical, symmetry invariant energy transfer rates.