Miracle-dz/UniFAR-FTUs huggingface dataset:
Malware Characterization through Alert Pattern Discovery

We present a novel alert correlation approach based on the factor analysis statistical technique for malware characterization. Our approach involves mechanically computing a set of abstract quantities, called factors, for expressing the intrusion detection system (IDS) alerts pertaining to malware instances. These factors correspond to patterns of alerts, and can be used to succinctly characterize malware. Unlike most existing alert correlation approaches for multistep attacks, our approach does not require predefined attack models for characterizing complex multistep attacks, and discovers potentially unknown relationships among alert types. Moreover, it requires relatively little alert information. As such, this approach is suitable for analysis pertaining to large-scale, privacy-preserving alert repositories. Initial experimental results indicate that our approach is useful in facilitating automated IDS alert pattern discovery, and in characterizing malware that manifests as multiple attack steps. Also, it may be used in identifying redundant signatures, enabling IDS performance tuning. Specifically, we examined the Snort rule identifiers (SIDs) of the alerts generated by the BotHunter tool, developed in the Cyber-Threat Analytics project, considering which SIDs co-occur pertaining to the same identified bot instance. Our exploratory analysis indicates that IDS alerts corresponding to bots can be expressed in terms of a small number of factors. Also, some bot families have distinguishing factor patterns.
Axonal Regrowth after Spinal Cord Injury via Chondroitinase and the Tissue Plasminogen Activator (tPA)/Plasmin System

Spinal cord injury (SCI) causes permanent debilitation due to the inability of axons to grow through established scars. Both the sugar chains and core proteins of chondroitin sulfate proteoglycans (CSPGs) are inhibitory for neurite regrowth. Chondroitinase ABC (ChABC) degrades the sugar chains and allows for synaptic plasticity, suggesting that after the sugar chain cleavage additional steps occur promoting a permissive microenvironment in the glial scar region. We report that the clearance of the core protein by the tissue plasminogen activator (tPA)/plasmin proteolytic system partially contributes to ChABC-promoted plasticity. tPA and plasmin are upregulated after SCI and degrade the deglycosylated CSPG proteins. Mice lacking tPA (tPA−/−) exhibit attenuated neurite outgrowth and blunted sensory and motor recovery despite ChABC treatment. Coadministration of ChABC and plasmin enhanced the tPA−/− phenotype and supported recovery in WT SCI mice. Collectively, these findings show that the tPA/plasmin cascade may act downstream of ChABC to allow for synergistic sensory and motor improvement compared with each treatment alone and suggest a potential new approach to enhance functional recovery after SCI.
Comparision of two optimization criteria in antenna problems with free phase pattern

Two criteria of antenna synthesis by the amplitude radiation pattern are compared. They use the reactive power and weighted current norm, respectively, both supplying the mean square difference between the desired and obtained amplitude patterns. The technique based on the generating polynomial concept is applied to nonlinear integral equations in both eases. Numerical results are presented.
WISECODE: wise image segmentation based on community detection

Abstract Image segmentation is one of the fundamental problems in image processing and computer vision, since it is the first step in many image analysis systems. This paper presents a new perspective to image segmentation, namely, segmenting input images by applying efficient community detection algorithms common in social and complex networks. First, a common segmentation algorithm is used to fragment the image into small initial regions. A weighted network is then constructed. Each initial region is mapped to a vertex, and all these vertices are connected to each other. The similarity between two regions is calculated from colour information. This similarity is then used to assign weights to the edges. Afterwards, a community detection algorithm is applied, and communities are extracted such that the highest modularity measure is achieved. Finally, a post-processing algorithm merges very small regions with the greater ones, further enhancing the final result. One of the most striking features of the proposed method, is the ability to segment the input image without the need to specify a predefined number of segments manually. This remarkable feature results from the optimal modularity value, which is utilised by this method. It is also able to segment the input image into a user defined number of segments. Extensive experiments have been performed, and the results show that the proposed scheme can reliably segment the input colour image into good subjective criteria.
Linked Legal Data : A SKOS Vocabulary for the Code of Federal Regulations

This paper describes the application of Semantic Web and Linked Data techniques and principles to regulatory information for the development of a SKOS vocabulary for the Code of Federal Regulations (in particular of Title 21, Food and Drugs). The Code of Federal Regulations is the codification of the general and permanent enacted rules generated by executive departments and agencies of the Federal Government of the United States, a regulatory corpus of large size, varied subject-matter and structural complexity. The CFR SKOS vocabulary is developed using a bottom-up approach for the extraction of terminology from text based on a combination of syntactic analysis and lexico-syntactic pattern matching. Although the preliminary results are promising, several issues (a method for hierarchy cycle control, expert evaluation and control support, named entity reduction, and adjective and prepositional modifier trimming) require improvement and revision before it can be implemented for search and retrieval enhacement of regulatory materials published by the Legal Information Institute. The vocabulary is part of a larger Linked Legal Data project, that aims at using Semantic Web technologies for the representation and management of legal data.
SODA: Software defined FPGA based accelerators for big data

FPGA has been an emerging field in novel big data architectures and systems, due to its high efficiency and low power consumption. It enables the researchers to deploy massive accelerators within one single chip. In this paper, we present a software defined FPGA based accelerators for big data, named SODA, which could reconstruct and reorganize the acceleration engines according to the requirement of the various dataintensive applications. SODA decomposes large and complex applications into coarse grained single-purpose RTL code libraries that perform specialized tasks in out-of-order hardware. We built a prototyping system with constrained shortest path Finding (CSPF) case studies to evaluate SODA framework. SODA is able to achieve up to 43.75X speedup at 128 node application. Furthermore, hardware cost of the SODA framework demonstrates that it can achieve high speedup with moderate hardware utilization.
A Validation of Refactorings Impact on Maintainability: A Case Study

Software refactorings are used commonly to fix problems such as the code smells. In this case study, we use a quality model to subjectively validate the consequences of resolving bad smells on software maintainability. The model is used to help software developers in their endeavor to improve software quality. This model can be used to search for the best alternative refactorings that can lead to a better quality. The results have shown that some bad smell resolutions may make improvements on the software quality, whereas others may not.
Sleep behaviour relates to language skills in children with and without communication disorders

Background: Sleep problems are common in children with autism spectrum disorder (ASD). There is also emerging evidence that sleep quality influences language learning in typical development. However, there is a gap within the literature in regards to sleep and developmental language disorder in children (DLD). Objectives: The aims of this study were to: (i) compare sleep patterns of children with communication disorders to the sleep patterns of their typical peers, and (ii) ascertain whether sleep patterns related to language in this sample. Method: The relationship between sleep and language was investigated via parental questionnaires. There were 65 child participants in total aged between 3 and 18 years. Parents reported on 28 children with a developmental communication disorder (CD; ASD n = 8 or DLD n = 20) and 37 who were typical developing. Results: The children with a developmental communication disorder showed more sleep problems than their typical peers particularly in terms of getting to sleep and early waking. Furthermore, significant correlations were found between sleep behavior and language for children in both groups. Conclusion: Children with CD may have poorer sleep patterns than their typical peers which could compound developmental language difficulties.
Well-typed narrowing with extra variables in functional-logic programming

Narrowing is the usual computation mechanism in functional-logic programming (FLP), where bindings for free variables are found at the same time that expressions are reduced. These free variables may be already present in the goal expression, but they can also be introduced during computations by the use of program rules with extra variables. However, it is known that narrowing in FLP generates problems from the point of view of types, problems that can only be avoided using type information at run-time. Nevertheless, most FLP systems use static typing based on Damas-Milner type system and they do not carry any type information in execution, thus ill-typed reductions may be performed in these systems. In this paper we prove, using the let-narrowing relation as the operational mechanism, that types are preserved in narrowing reductions provided the substitutions used preserve types. Based on this result, we prove that types are also preserved in narrowing reductions without type checks at run-time when higher order (HO) variable bindings are not performed and most general unifiers are used in unifications, for programs with transparent patterns. Then we characterize a restricted class of programs for which no binding of HO variables happens in reductions, identifying some problems encountered in the definition of this class. To conclude, we use the previous results to show that a simulation of needed narrowing via program transformation also preserves types.
Building efficient topological maps for mobile robot localization: An evaluation study on COLD benchmarking database

Topological localization is a qualitative solution approach that can assist obtaining a faster quantitative metric solution by limiting the searchable space. Consequently, its efficiency is an essential requirement in hierarchical localization frameworks. This paper presents a topological map generation method with a localization scheme. Good compromise of performance measures - accuracy, memory and processing time - indicates the method's efficiency. The suggested implementations rely on information-theoretic selection of local features for node distinctive representation, and a visual codebook for compression. Testing the proposed approach on the COLD database, a recent specific benchmarking database for robotic topological mapping and localization, reveals its customization according to the vision sensor and environment characteristics. The approach guarantees over 90% localization accuracy with more than 50% overhead reduction, and is suitable for application in highly unstructured cluttered environments that are influenced by dynamics and illumination variations.
Exploiting Background Information Networks to Enhance Bilingual Event Extraction Through Topic Modeling

—In this paper we describe a novel approach of biased propagation based topic modeling to exploit global background knowledge for enhancing both the quality and portability of event extraction. The distributions of event triggers and arguments in topically-related documents are much more focused than those in a heterogeneous corpus. Based on this intuition, we apply topic modeling to automatically select training documents for annotation, and demonstrate it can signiﬁcantly reduce annotation cost in order to achieve comparable performance for two different languages and two different genres. In addition, we conduct cross-document inference within each topic cluster and show that our approach advances state-of-the-art.
Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods

We propose a set of consistency conditions that frontier efficiency measures should meet to be most useful for regulatory analysis or other purposes. The efficiency estimates should be consistent in their efficiency levels, rankings, and identification of best and worst firms, consistent over time and with competitive conditions in the market, and consistent with standard nonfrontier measures of performance. We provide evidence on these conditions by evaluating and comparing efficiency estimates on U.S. bank efficiency from variants of all four of the major approaches -- DEA, SFA, TFA, and DFA -- and find mixed results.
On-line Stable Evolutionary Recognition Based on Unit Quaternion Representation by Motion-Feedforward Compensation

Abstract This paper presents a pose measurement method of a 3D object. The proposed method utilizes an evolutionary search technique of the genetic algorithm (GA) and a fitness evaluation based on a stereo model matching whose pose is expressed by unit quaternion. To improve the dynamics of recognition, a motion-feedforward compensation method is proposed for the hand-eye system. The effectiveness of the proposed method is confirmed by the simulation experiments.
Modeling and Forecasting the Urban Volume Using Stochastic Differential Equations

Traffic flow prediction can be used for the management of traffic control systems and can be applied toward improving traffic light split times at intersections. In this paper, we developed a methodology for the short-term prediction of traffic flow using the stochastic differential equation (SDE). Since the current volume depends on the previous short-term volume and time, we used the Hull-White model. With the proposed method, a flexible short-term prediction of volume is suggested. It is possible to simulate traffic conditions easily and also detect incidents precisely. This method is applied in Tehran's highways, and the obtained results are compared with previous artworks. Our results offered a better fit to the traffic volume.
Music mood tracking based on HCS

Music mood present the inherent emotional state of music on certain duration of music segment. The mood may vary in an entire piece of music, thus tracking the mood changing is an effect way to automatically analyze the music mood for better music understanding. HCS (Hierarchical Classification Scheme), which automatically generates tree structure hierarchical classifier driven by training data to adaptive different recognition applications, is proposed in our paper to build recognition model of music mood. The duration of training music segments is set as 8 seconds, which is assumed to contain stable mood state within a single segment to ensure the accuracy of the HCS model. The mood in an entire piece of music is then divided into 50% overlapping 8 seconds frames, which are the same duration with the training segments in HCS model, to accomplish the mood tracking. The automatic tracking result show good accordance with general music reviews and manually labeled ground truth, and proves the effectiveness of the HCS scheme and the rational selection of 8 seconds segment duration.
Narrativity and clinical reasoning: A case study of the psychiatric assessment of an Iraqi refugee

This paper highlights the problem of ‘place’ for an Iraqi refugee who, for years, had been tortured and imprisoned in his home country. The paper presents a case study of a clinical encounter with this refugee, whose relatives sought to have him ‘put away’ for ‘threatening’ behaviour through the psychiatric intervention of an Australian Crisis Assessment and Treatment Team (CAT Team). Our analytical starting point is that the narrativity of the encounter is situated and realized within the insititutional circumstances of a risk-oriented, psychiatric assessment. We construct and analyse the case study to illustrate the interpretive machinery of ‘clinical reasoning’ and in particular the tension and play between ‘paradigmatic thinking’ and narrative thinking’ (Mattingly 1991). Our analysis follows the work of social scientists (notably Behar 1996; Frank 2001; Kleinman 1995; Hyden 1997; Skultans 2004; Wilkinson 2005) who seek to expand methodologies for writing about human suffering.
A QoS Opportunistic Routing Based on Directional Transmission for Wireless Sensor Networks

For mission-critical application of WSN, it's essential to guarantee multi-QoS requirements including of delay and reliability as far as possible. The existing multipath routing methods fulfill QoS demands by disseminating the redundant replica over multiple paths, leading to low energy efficiency and higher delay. In this paper, a novel Directional Transmission based QoS-aware Opportunistic Routing, DTQOR is proposed for timely and reliable delivery. At first, by partitioning the end-to-end delay and reliability into each hop on a path, the QoS routing metric subjected to reliability and delay is formulated as a multi-objective multi-constraint optimization problem. Based on the analysis about impact of Directional Transmission on connectivity and constraint relationship of forwarding candidates, an approach is designed to reduce selecting scope of next hop candidate nodes. A heuristic algorithm to construct prioritized permutation of forwarding candidates is further proposed to achieve sufficient suboptimal aim and whole routing mechanism are provided finally. The simulations have been conducted to evaluate the performance of DTGOR through NS-2. The results have demonstrated that the proposed DTQOR reduces the end-to-end delay significantly and improve the energy efficiency.
Measuring the direction and the strength of coupling in nonlinear Systems-a modeling approach in the State space

We present a novel signal processing methodology to determine the direction and the strength of coupling between coupled nonlinear systems. The methodology is based on multivariate local linear prediction in the reconstructed state spaces of the observed variables from each multivariable nonlinear system. Application of the method is illustrated with systems of coupled Rossler and Lorenz oscillators in various coupling configurations. The obtained results are compared with ones produced by the use of the directed transfer function, a model-based method in the time domain. Through a surrogate analysis, it is shown that the proposed method is more reliable than the directed transfer function in identifying the direction and strength of the involved interactions.
Detection of MYD88 L265P in patients with lymphoplasmacytic lymphoma/Waldenstrom macroglobulinemia and other B-cell non-Hodgkin lymphomas

Background Recent studies have identified a high prevalence of the MYD88 L265P mutation in lymphoplasmacytic lymphoma (LPL)/Waldenstrom macroglobulinemia (WM) cases, whereas low frequencies have been observed in other B cell non-Hodgkin lymphomas (NHLs). Methods We evaluated the sensitivity of the mutant enrichment 3'-modified oligonucleotide (MEMO)-PCR technique, a new detection method. We examined the MYD88 L265P mutation in a series of Korean patients with LPL/WM and other B cell NHLs in bone marrow aspirates, using the MEMO-PCR technique. Results The sensitivity of MEMO-PCR was estimated to be approximately 10-16.7%. MYD88 L265P was detected in 21 of 28 LPL cases (75%) and only three of 69 B cell NHL cases (4.3%). Conclusion Although MEMO-PCR had relatively low sensitivity, we confirmed the high prevalence of the MYD88 L265P mutation in Korean LPL patients. Our study suggests the diagnostic value of MYD88 L265P for differentiating B-cell NHLs.
Image Similarity Learning through Rankboost Mechanism Based On Keywords and Content Queries

Different types of search engines are used to search image and text contents. Two types of image search methods are available in the Internet. They are query keyword based model and content based image retrieval models. Text query strings are used in the textual image retrieval model. Content based image retrieval (CBIR) model uses the visual information of the images. Image search methods use the text annotation and image visual features. Google image search and Bing image search engines are used to fetch images from the web. Image query string is used to search image on Internet. One click query image selection method is used to submit user intention for image retrieval. Content based image re-ranking is performed with visual and textual similarity metrics. Adaptive Weight Schema is used for the similarity analysis. Feature weight learning algorithm is applied to estimate feature weights for the images and its category. Query is expanded with keyword and visual information. Rank boost framework algorithm is enhanced to rank images with photographic quality. Content similarity and visual quality factors are used for the re-ranking process. In this paper, we propose an image indexing and retrieval using speech annotations based on a predefined structured syntax. To improve the retrieval effectively, N-best lists for index generation is used . so, a query expansion technique is explored to enhance the query terms. All this process is automatic, without extra effort from the user. This is critically important in web-based image search engine for any commercial, where the user interface has to be extremely simple.
Fusion of occupancy grid mapping and model based object tracking for driver assistance systems using laser and radar sensors

in this paper we present a novel environment perception system based on an occupancy grid mapping and a multi-object tracking. The goal of such a system is to create a harmonic, consistent and complete representation of the vehicle environment as a base for future advanced driver assistance systems. In addition to a mathematical formulation of the problem we present a robust algorithm to detect dynamic obstacles from the occupancy map and show how both, the mapping process and the tracking can benefit from each other. Therefore, the concept of moving objects with associated dynamic cells is introduced. The presented techniques are applicable to both 2D and 3D mapping and can be also extended to correct the ego motion from the occupancy map and the object tracks. Unlike many publications over the last years our work provides real time performance and an accurate detection of obstacles with real laser and radar sensors and can fulfill the requirements of future driver assistance systems.