Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrusion Detection System by Using Hybrid Algorithm of Data Mining Technique

The aim of a network-based intrusion detection system (NIDS) is to detect malicious activity that targets a network and its resources. Abnormal activities or behaviors on the network systems could be identified by security systems. But, conventional security systems such as firewall and anti-virus cannot be successful in many abnormal actions. To overcome this problem, accurate and more intelligent intrusion detection systems (IDS) are required. In the last decades, various techniques and solutions have been presented to address the weakness of IDS such as high false alarm rate, low accuracy and time-consuming. Among the variety of anomaly detection approaches, Decision Tree (DT) and k nearest neighbor (k-NN) are known to be two of the best machine learning algorithms to classify normal from abnormal behaviors (such as DoS, U2R, R2L and Probe). In this paper, a hybrid anomaly-based intrusion detection approach is proposed that is based on DT and k-NN. For improving the performance of proposed approach a feature selection technique is used for extracting optimized information from NSL-KDD dataset. The experimental results showed that the proposed approach has achieved a positive detection rate of 99.7%, the false alarm rate of 0.2% and the accuracy of 99.6%.
N utrient variability for distillers grains plus solubles and dry matter determination of ethanol

Three experiments were conducted to evaluate nutrient content and DM determination methods of dry milling byproducts. In Exp. 1, nutrient composition was determined for wet distillers grains plus solubles (WDGS) and modified distillers grains plus solubles (MDGS) from 6 ethanol plants with 10 samples collected per day, across 5 d, and sampling was repeated over 4 separate months. Mean composition was 31.0% CP, 11.9% fat, 0.84% P, and 0.77% S (DM basis). Coefficients of variation for DM content were greater for some plants than others, and variation occurred within and across days. Variability was small for CP and P, whereas fat differed among ethanol plants. Large variation in means and CV were observed for S in period 1, but variation subsequently decreased. Coefficients of variation for S were similar for samples collected within the same day and across days. In Exp. 2, samples of WDGS, MDGS, Dakota Bran Cake, and distillers solubles were used to determine DM content by drying samples at 105°C for 3, 8, and 24 h and 60°C for 24 and 48 h, vacuum oven drying, toluene distillation, and Karl Fischer titration. Compared with toluene distillation, drying at 105°C resulted in less DM (P ≤ 0.10) and vacuum drying and Karl Fischer titration resulted in greater DM (P < 0.01). In Exp. 3, additional WDGS, MDGS, and wet grains with no solubles were used to determine DM with oven drying at 60°C for 48 h, oven drying at 105°C for 3 h, or toluene distillation. Drying at 60°C for 48 h was similar to toluene distillation (P ≥ 0.60).
Adaptive Neighborhood Resizing for Stochastic Reachability in Multi-Agent Systems

We present DAMPC, a distributed, adaptive-horizon and adaptive-neighborhood algorithm for solving the stochastic reachability problem in multi-agent systems, in particular flocking modeled as a Markov decision process. At each time step, every agent calls a centralized, adaptive-horizon model-predictive control (AMPC) algorithm to obtain an optimal solution for its local neighborhood. Second, the agents derive the flock-wide optimal solution through a sequence of consensus rounds. Third, the neighborhood is adaptively resized using a flock-wide, cost-based Lyapunov function V. This way DAMPC improves efficiency without compromising convergence. We evaluate DAMPC's performance using statistical model checking. Our results demonstrate that, compared to AMPC, DAMPC achieves considerable speed-up (two-fold in some cases) with only a slightly lower rate of convergence. The smaller average neighborhood size and lookahead horizon demonstrate the benefits of the DAMPC approach for stochastic reachability problems involving any controllable multi-agent system that possesses a cost function.
Robot Path Planning Algorithms: A Review of Theory and Experiment

The aim of this book is to introduce different robot path planning algorithms and suggest some of the most appropriate ones which are capable of running on a variety of robots and are resistant to disturbances. Being real-time, being autonomous, and the ability to identify high-risk areas and risk management are the other features that will be mentioned throughout these methods.
Creating a Decision-Making Model Using Association Rules

ABSTRACT Being a highly significant and complex function of management, decision making requires methods and techniques that simplify the process of selecting one choice among all available options. Decision making is therefore selection of that particular choice over any of several alternatives. Because of the process complexity, a continuous research and improvement of the methods and techniques modern decision making involves is required. One of many modern business challenges is to discover any possible improvement in the decision-making process managers shall use in making the right decision. Any decision made by managers directly impacts the realized profit, business, and company’s position on the market. The fact is that mankind faces the decision-making problem in each phase of its social development, which has resulted in increased need for learning more about it. In this work, both the significance and application of association rules will be analyzed on an example of car sales business. The research was conducted on a sample of 1728 transactions in order to recognize and establish the association rules and then determine their impact on the sales and profit. For the purpose of this research, a large car sales database was used as a source of information, which is also described in this work. Once these association rules were established, they were then used to create a better and more complete market supply. The main contribution of the paper is providing business intelligence model for performing association rules in real-term business settings.
Positional and dimensional temporomandibular joint changes after correction of posterior crossbite in growing patients: A systematic review.

OBJECTIVE To elucidate the positional and dimensional temporomandibular joint (TMJ) changes after correction of posterior crossbite in growing patients. MATERIALS AND METHODS A systematic unrestricted search was done in six databases until June 27, 2017. A manual search in the reference lists of the included studies and gray literature was also performed. The eligibility criteria included randomized controlled trials and prospective nonrandomized controlled clinical trials and a sample of growing subjects with posterior crossbite that required maxillary expansion. Risk of bias assessment was performed using Cochrane's Risk of Bias Tool. The literature search, study inclusion, risk of bias assessment, evaluation of quality of evidence (GRADE), and data extraction were performed by two reviewers independently. RESULTS Only two articles were finally eligible to be included in the qualitative analysis. Both studies were RCTs and were assessed as having unclear risk of bias. Meta-analysis was not possible since one study used cone-beam computed tomography as an assessment tool while the other used ultrasonography. One study reported significant reduction in the condylar positional difference between centric and habitual occlusion in the treatment group, while no spontaneous correction of condylar asymmetric position occurred in the control group. The other study reported minor changes of condylar position in both treatment and control groups. CONCLUSIONS The current available data provide insufficient and weak evidence to form a solid and firm conclusion. There is poor, very low-quality evidence regarding the positional and dimensional effects of posterior crossbite correction on the TMJs.
Reliability and validity of the Zebris FDM-THQ instrumented treadmill during running trials

Abstract Little is known about the reliability, validity and smallest detectable differences of selected kinetic and temporal variables recorded by the Zebris FDM-THQ instrumented treadmill especially during running. Twenty male participants (age = 31.9 years (±5.6), height = 1.81 m (±0.08), mass = 80.2 kg (±9.5), body mass index = 24.53 kg/m2 (±2.53)) walked (5 km/h) and ran (10 and 15 km/h) on an instrumented treadmill, wearing running shoes fitted with Pedar-X insoles. A test-double retest protocol was conducted over two consecutive days. Maximal vertical force (Fmax), contact time (CT) and flight time (FT) data from 10 consecutive steps were collected. Within- and between-day reliability, smallest detectable differences (SDD) and validity (95% limits of agreement (LOA)) were calculated. ICC values for the Zebris for Fmax were acceptable (ICC ≥ 0.7) while CT and FT reliability indices were predominantly good (ICC ≥ 0.8) to excellent (ICC ≥ 0.9). The Zebris significantly underestimated Fmax when compared with the Pedar-X. The 95% LOA increased with speed. SDD ranged between 96 N and 169 N for Fmax, 0.017s and 0.055s for CT and 0.021s and 0.026s for FT. In conclusion, Zebris reliability was acceptable to excellent for the variables examined, but inferior in comparison with Pedar-X. With increased running speeds, a bias effect (underestimation) existed for the Zebris compared with Pedar-X.
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach

Adding datatypes to ontology-mediated queries (OMQs) often makes query answering hard. As a consequence, the use of datatypes in OWL 2 QL has been severely restricted. In this paper we propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes. Instead of restricting the ontology language we aim at a classification of the patterns of datatype atoms in OMQs into those that can occur in non-tractable OMQs and those that only occur in tractable OMQs. To this end we establish a close link between OMQ answering with datatypes and constraint satisfaction problems over the datatypes. In a case study we apply this link to prove a P/coNP-dichotomy for OMQs over DL-Lite extended with the datatype (Q,<=). The proof employs a recent dichotomy result by Bodirsky and Kára for temporal constraint satisfaction problems.
Approximate Bayesian inference in spatial environments

We propose to learn a stochastic recurrent model to solve the problem of simultaneous localisation and mapping (SLAM). Our model is a deep variational Bayes filter augmented with a latent global variable---similar to an external memory component---representing the spatially structured environment. Reasoning about the pose of an agent and the map of the environment is then naturally expressed as posterior inference in the resulting generative model. We evaluate the method on a set of randomly generated mazes which are traversed by an agent equipped with laser range finders. Path integration based on an accurate motion model is consistently outperformed, and most importantly, drift practically eliminated. Our approach inherits favourable properties from neural networks, such as differentiability, flexibility and the ability to train components either in isolation or end-to-end.
Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic features of the input and filter the non-semantic perturbation. FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping between the high-dimensional data space and the low-dimensional semantic space; also mutual information is applied to disentangle the semantically meaningful features. After the bidirectional mapping, the adversarial data can be reconstructed to denoised data, which could be fed into any pre-trained classifier. We empirically show the quality of reconstruction images and the effectiveness of defense.
Ubiquitin-dependent proteolysis of CXCL7 leads to posterior longitudinal ligament ossification

Ossification of the posterior longitudinal ligament (OPLL), a spinal ligament, reduces the range of motion in limbs. No treatment is currently available for OPLL, which is why therapies are urgently needed. OPLL occurs in obesity, is more common in men, and has an onset after 40 years of age. The mechanisms underlying OPLL remain unclear. In this study, we performed a serum proteomic analysis in both OPLL patients and healthy subjects to identify factors potentially involved in the development of OPLL, and found reduced levels of a protein that might underlie the pathology of OPLL. We isolated the protein, determined its amino acid sequence, and identified it as chemokine (C-X-C motif) ligand 7 (CXCL7). Based on these proteomics findings, we generated a CXCL7 knockout mouse model to study the molecular mechanisms underlying OPLL. CXCL7-null mice presented with a phenotype of OPLL, showing motor impairment, heterotopic ossification in the posterior ligament tissue, and osteoporosis in vertebrate tissue. To identify the mechanisms of CXCL7 deficiency in OPLL, we searched for single nucleotide polymorphisms and altered DNA exons, but no abnormalities were found. Although miR-340 levels were found to be high in an miRNA array, they were insufficient to reduce CXCL7 levels. Ubiquitin C-terminal hydrolase1 (UCHL1) was found to be overexpressed in CXCL7-null mice and in the sera of patients with OPLL, and was correlated with OPLL severity. Post-translational modifications of proteins with ubiquitin and ubiquitin-like modifiers, orchestrated by a cascade of specialized ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2), and ubiquitin ligase (E3) enzymes, are thought to control a wide range of cellular processes, and alterations in the ubiquitin–proteasome system have been associated with several degenerative disorders. In addition, the OPLL tissue of CXCL7-null mouse and its primary cells expressed the antibody to ubiquitin (linkage-specific K48). Our data clearly show decreased CXCL7 levels in patients with OPLL, and that OPLL developed in mice lacking CXCL7. Tumor necrosis factor receptor-associated factor (TRAF)6 expression was decreased in CXCL7-null mouse primary cells. Furthermore, K48 polyubiquitination was found in posterior longitudinal ligament ossified tissue and primary cells from CXCL7-null mice. We performed a phosphoproteomics analysis in CXCL7-deficient mice and identified increased phosphorylation of mitogen-activated protein kinase kinase (ME3K)15, ubiquitin protein ligase E3C (UBE3C) and protein kinase C (PKC) alpha, suggesting that ubiquitin-dependent degradation is involved in CXCL7 deficiency. Future studies in the CXCL7-null mouse model are, therefore, warranted to investigate the role of ubiquitination in the onset of OPLL. In conclusion, CXCL7 levels may be useful as a serum marker for the progression of OPLL. This study also suggests that increasing CXCL7 levels in patients can serve as an effective therapeutic strategy for the treatment of OPLL.
Chaotic Time Series Prediction Using a Photonic Reservoir Computer with Output Feedback

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals (Jaeger andHaas 2004; Maass, Natschläger, and Markram 2002). It canbe easily implemented in hardware. The performance ofthese analogue devices matches digital algorithms on a series of benchmark tasks (see e.g. (Soriano et al. 2015) fora review). Their capacities could be extended by feedingthe output signal back into the reservoir, which would allow them to be applied to various signal generation tasks(Antonik et al. 2016b). In practice, this requires a high-speed readout layer for real-time output computation. Herewe achieve this by means of a field-programmable gate array (FPGA), and demonstrate the first photonic reservoircomputer with output feedback. We test our setup on theMackey-Glass chaotic time series generation task and obtain interesting prediction horizons, comparable to numerical simulations, with ample room for further improvement.Our work thus demonstrates the potential offered by the output feedback and opens a new area of novel applications forphotonic reservoir computing.
Multivariate Hawkes Processes for Large-Scale Inference

In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems, both in the number of events in the observed history n and the number of event types d (i.e. dimensions). The proposed Scalable Low-Rank Hawkes Process (SLRHP) framework introduces a low-rank approximation of the kernel matrix that allows to perform the nonparametric learning of the d2 triggering kernels in at most O(ndr2) operations, where r is the rank of the approximation (r ≪ d, n). This comes as a major improvement to the existing state-of-the-art inference algorithms that require O(nd2) operations. Furthermore, the low-rank approximation allows SLRHP to learn representative patterns of interaction between event types, which is usually valuable for the analysis of complex processes in real-world networks.
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms

The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multi-layer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves state-of-the-art performances compared with several baselines.
Sexual dimorphism in body size and shape in Pavlovian upper Paleolithic group: A population approach

Pavlovian skeletal remains (DV 3, 13 to 16 and Pavlov 1) represent one of the largest and best preserved Upper Paleolithic human samples, permitting analysis of their body size, body shape and sexual dimorphism. To assessthese, we have estimated stature, body mass, skeletal ponderal indexes plus epiphyseal and diaphyseal robusticity and shape from their long bones for these individuals and other earlier Upper Paleolithic human remains. Stature was computed as a mean value of 8 most reliable equations using femoral (6 equations) and humeral (2 equations) lengths only. Body mass was estimated on the basis of the femoral head, subtrochanteric, distal femoral, proximal and distal tibial products, and as a function of body height. BMI and Rohrer's indexes expressing general shape of the body were computed. The first step was the characterization of the Pavlovian sample using these parameters. Unfortunately, there are four undoubtedly male skeletons (DV 13, DV 14, DV 16 and Pavlov 1), one clearly female skeleton (DV 3) and one pathological individual of uncertain sex (DV 15). The males are relatively tall and slim. DV 3 and 15 are significantly smaller. DV 3 is relatively gracile. DV 15 is significantly more robust than the males. Despite many pathological features DV 15 is remarkably similar to the Sungir 3 girl which supports a female diagnosis of this pathological individual. To assess sexual dimorphism we compared the Pavlovian remains with the Předmosti sample. Předmosti males are smaller and more gracile. Both DV 3 and DV 15 do not differ significantly from the Předmosti females. However, the Předmosti females are relatively gracile but they are significantly more robust in comparison with the Předmosti males. Similarly, as in the Pavlovian sample, there is one unusually slim female (late adolescent Předmosti 5). To assess variability in body size and shape of the Moravian Gravettian populations, we compared the Central European and Mediterranean Gravettian samples. The Mediterranean Upper Paleolithic populations were probably taller and somewhat more robust but the direction of sexual dimorphism is the same. There are also hyper-robust females and one very slim female in the Mediterranean sample. The specific character of sexual dimorphism, i. e. small robust and hyper-robust females, was probably caused by the strong sexual selection of the somatotype. One model to explain the presence of slim females and the origin of the unusual robusticity can be based on our study of ontogeny of macaques and apes, in which the timing and rate of the adolescent growth spurt can have profound effects on body build. Such variance in the adolescent spurt in females could cause the specific body built in these Upper Paleolithic females.
Overexpression of C-sis inhibits H2O2-induced Buffalo rat liver cell apoptosis in vitro and alleviates liver injury in a rat model of fulminant hepatic failure

The present study aimed to investigate the role of the C-sis gene in the apoptosis of hepatocytes in vitro and in the liver function of a rat model of fulminant hepatic failure (FHF). Buffalo rat liver (BRL) cells were treated with hydrogen peroxide (H2O2) to induce apoptosis and then transfected with a C-sis overexpression vector. A rat model of FHF was established, and C-sis was overexpressed. The mRNA and protein expression of C-sis were examined using reverse transcription-polymerase chain reaction and western blot analyses, respectively. Cell viability was assessed by CCK8, and a TUNEL assay was used to examine cell apoptosis. Flow cytometry was used for cell cycle detection. Hematoxylin and eosin staining was used for histological examination. The levels of alanine transaminase (ALT) and aspartate transaminase (AST) were also examined in the rats. The results showed that C-sis was successfully overexpressed in the cells and rat model. Compared with H2O2-treated BRL cells, the overexpression of C-sis significantly inhibited cell apoptosis, promoted cell viability, and decreased the expression of cleaved caspase-3. Similar results were observed in the FHF rats treated with the C-sis overexpression plasmid, compared with those treated with empty plasmids. In addition, in the FHF rats overexpressing C-sis, histological examination showed that liver injury was alleviated, the levels of ALT and AST were significantly decreased, and mortality rate was significantly decreased, compared with those observed in the rats treated with empty plasmids. In conclusion, the overexpression of C-sis inhibited the H2O2-induced apoptosis of BRL cells in vitro, and alleviated liver injury, improved liver function, and decreased mortality rates in rat models of FHF.