Miracle-dz/UniFAR-FTUs huggingface dataset:
Rating-based collaborative filtering combined with additional regularization

The collaborative filtering (CF) approach to recommender system has received much attention recently. However, previous work mainly focuses on improving the formula of rating prediction, e.g. by adding user and item biases, implicit feedback and time-aware factors, etc, to reach a better prediction by minimizing an objective function. However, little effort has been made on improving CF by incorporating additional regularization to the objective function. Regularization can further bound the searching range of predicted ratings. In this paper, we improve the conventional rating-based objective function by using ranking constraints as the supplementary regularization to restrict the searching of predicted ratings in smaller and more likely ranges, and develop a novel method, called RankSVD++, based on the SVD++ model. Experimental results show that RankSVD++ achieves better performance than existing main-streaming methods due to the addition of informative ranking-based regularization. The idea proposed here can also be easily incorporated to the other CF models.
Improved face recognition system using probability distribution functions extracted from wavelet subbands

In this paper, we propose a high performance face recognition system based on the probability distribution functions (PDF) extracted from discrete wavelet transform in different colour channels. The PDFs of the equalized and segmented faces in different subband images obtained from discrete wavelet transform (DWT) are used as statistical feature vectors for the recognition of faces by minimizing the Kull-back- Leibler Divergence (KLD) between the PDF of a given face and the PDFs of faces in the database. Majority voting (MV) and feature vector fusion (FVF) methods have been employed to improve the recognition performance by combining feature vectors in HSI and YCbCr colour spaces of LL, LH, HL, and HH subband images. The system has been tested on the FERET and the Head Pose (HP) face databases. The results have been compared with conventional PCA, and three state-of-art face recognition techniques, namely, adaptive local binary pattern (LBP) PDF based face recognition, nonnegative matrix factorization (NMF), and supervised incremental NMF (INMF).
Change impact analysis for requirement evolution using use case maps

Changing customer needs and computer technology are the driving factors influencing software evolution. There is a need to assess the impact of these changes on existing software systems. Requirement specification is gaining increasingly attention as a critical phase of software systems development process. In particular for larger systems, it quickly becomes difficult to comprehend what impact a requirement change might have on the overall system or parts of the system. Thus, the development of techniques and tools to support the evolution of requirement specifications becomes an important issue. In this paper we present a novel approach to change impact analysis at the requirement level. We apply both slicing and dependency analysis at the use case map specification level to identify the potential impact of requirement changes on the overall system. We illustrate our approach and its applicability with a case study conducted on a simple telephony system.
The SCFDia2 Ubiquitin E3 Ligase Ubiquitylates Sir4 and Functions in Transcriptional Silencing

In budding yeast, transcriptional silencing, which is important to regulate gene expression and maintain genome integrity, requires silent information regulator (Sir) proteins. In addition, Rtt106, a histone chaperone involved in nucleosome assembly, functions in transcriptional silencing. However, how transcriptional silencing is regulated during mitotic cell division is not well understood. We show that cells lacking Dia2, a component of the SCFDia2 E3 ubiquitin ligase involved in DNA replication, display defects in silencing at the telomere and HMR locus and that the F-box and C-terminal regions of Dia2, two regions important for Dia2's ubiquitylation activity, are required for proper transcriptional silencing at these loci. In addition, we show that Sir proteins are mislocalized in dia2Δ mutant cells. Mutations in Dia2 and Rtt106 result in a synergistic loss of silencing at the HMR locus and significant elevation of Sir4 proteins at the HMR locus, suggesting that silencing defects in dia2Δ mutant cells are due, at least in part, to the altered levels of Sir4 at silent chromatin. Supporting this idea, we show that SCFDia2 ubiquitylates Sir4 in vitro and in vivo. Furthermore, Sir4 binding to silent chromatin is dynamically regulated during the cell cycle, and this regulation is lost in dia2Δ mutant cells. These results demonstrate that the SCFDia2 complex is involved in transcriptional silencing, ubiquitylates Sir4, and regulates transcriptional silencing during the cell cycle.
A Comparative Study on CT Image Segmentation Using FCM-based Clustering Methods

Identifying specific CT-image regions is an im- portant process in medical diagnosis. Clustering is a simple and useful means for automatic image segmentation. However, clustering results vary with the features of image pixels and the settings of parameters of the clustering methods. This study compares the results of CT image segmentation using FCM- based clustering algorithms running with intensity- and texture- based image features. Three types of image features, grayscale, LBP, and grayscale+LBP, are investigated. KM, FCM, and their medoid-variations are tested with various parameter settings. The results show that FCM and the grayscale+LBP feature can produce reasonable and satisfactory clustering results for CT-image segmentation. Index Terms—image feature, image segmentation, local bi- nary pattern (LBP), FCM-based clustering.
Development of a curriculum analysis tool

A good curriculum is crucial for a successful university education. When developing a curriculum, topics, such as economics, natural science, informatics, etc. are set first, and course syllabi are written accordingly. However, the topics actually covered by the course syllabi are not guaranteed to be identical to the initially set topics. To find out if the actual topics covered by the developed course syllabi, we developed a method of systematically analyzing course syllabi that uses latent Dirichlet allocation (LDA) and Isomap. In this paper, we propose the web-based curriculum analysis tool with this method, and demonstrate an example of the way the tool is used for analyzing computer science curricula.
A Low-Complexity Method for Chase-Type Decoding of Reed-Solomon Codes

In this work we present a low-complexity implementation of Chase-type decoding of Reed-Solomon codes. In such, we first use the soft-information available at the channel output to construct a test-set of 2eta vectors, equivalent in all except the eta Lt eta least reliable coordinate positions. We then give an interpolation procedure to construct a set of 2eta bivariate polynomials, with the roots of each specified by its corresponding test-vector. Here, test-vector similarity is exploited to share much of the required computation. Finally, we obtain the candidate message from the single z-linear factor of each bivariate polynomial. Although we provide an expression for the direct computation of each candidate message, the complexity of repeating this computation for each interpolation polynomial is prohibitive. We, thus, also present a reduced-complexity factorization (RCF) method to select a single polynomial that, with high probability, contains the correctly decoded message in its z-linear factor. Although suboptimal, the loss in performance of RCF decreases rapidly with increasing code length. We provide extensive simulation results showing that a significant performance increase over traditional hard-decision decoding is achievable with a comparable computational complexity (as implemented with the Berlekamp-Massey algorithm)
The Question Concerning Empowerment: Subjects of the Enterprise or Enterprising Subjects?

Enterprise Systems (ES), historically justified as means of control, are paradoxically also becoming integral to empowerment. Some researchers suggest that achieving the right balance between control and empowerment is a key challenge for today’s organizations. Others argue that such simple dichotomous conceptualizations fail to account for the co-existence in practice of a high degree of control and empowerment. To account for these seemingly paradoxical effects, we argue that empowerment does not result from the relaxation of organizational controls over particular functions but rather from the application of techniques of control, including the ES, aimed at shaping employee identities in accordance with organizational goals and desires. Understanding control as intrinsic and not separate from empowerment, we suggest that the empowered employee is not merely a subject of the enterprise but more so continually constructed as an enterprising subject. We discuss implications for theorizing the role of ES in empowerment initiatives.
Stability of Markovian processes III: Foster–Lyapunov criteria for continuous-time processes

In Part I we developed stability concepts for discrete chains, together with Foster–Lyapunov criteria for them to hold. Part II was devoted to developing related stability concepts for continuous-time processes. In this paper we develop criteria for these forms of stability for continuous-parameter Markovian processes on general state spaces, based on Foster-Lyapunov inequalities for the extended generator. Such test function criteria are found for non-explosivity, non-evanescence, Harris recurrence, and positive Harris recurrence. These results are proved by systematic application of Dynkin's formula. We also strengthen known ergodic theorems, and especially exponential ergodic results, for continuous-time processes. In particular we are able to show that the test function approach provides a criterion for f-norm convergence, and bounding constants for such convergence in the exponential ergodic case. We apply the criteria to several specific processes, including linear stochastic systems under non-linear feedback, work-modulated queues, general release storage processes and risk processes.
Efficient Sequence Classification with Spatial Representations

Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of music data, or biological sequences. We present a general, simple feature representation of sequences, spatial representation, that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently introduced for the problem of biological sequence classification, exploits a novel multi-scale representation of strings. We demonstrate that the developed approach is applicable to modeling of sequences in a wide range of sequence domains, both discreteand continuous-valued. Experiments using the new features and algorithms on text document categorization, music genre and artist recognition show excellent predictive performance, while demonstrating significant improvements in running time over the existing state-of-the-art sequence classification methods on these large alphabet, large sequence datasets. Background. A number of state-of-the-art approaches to classification of sequences over finite alphabet Σ rely on measuring sequence similarity using fixed-length representations Φ(X) of sequences as the spectra (|Σ|-dimensional histogram) of counts of short substrings (k-mers), contained, possibly with up to m mismatches, in a sequence, c.f. , spectrum/mismatch methods [3, 4, 2]. However, computing similarity scores, or kernels, K(X,Y )=Φ(X) Φ(Y ) using these representations can be challenging, e.g., efficient O(k|Σ|(|X| + |Y |)) trie-based mismatch kernel algorithm [4] strongly depends on the alphabet size and the number of mismatches m. On the other hand, the gapped [3] and subsequence [6] kernels have complexity independent of |Σ|, but quadratic in the sequence length (subsequence method) or show suboptimal performance compared to other methods (e.g., mismatch). Method. Similarity evaluation under spatial representation amounts to sampling the sequence features at different resolutions and comparing the resulting spectra; similar sequences will have similar spectra at one or more resolutions. Each sampled spatial feature consists of t substrings of length k, with each substring no more than d positions away from its neighbors. We illustrate the spatial features in Figure 1(a). The upper panel shows a typical contiguous spectrum 6-mer and the lower panel shows how a spatial sample method with k=2,t=3 would extract features from the string. Much like the spectrum features, the spatial feature ”AR ND CQ“ shown has the value proportional to the number of times it occurs in string X . While spectrum/mismatch representations rely on contiguous string fragments of length k, the spatial representation, on the other hand, is multi-dimensional, made of variably distanced string fragment combinations (Figure 1(b)). In the figure, we show a spatial embedding (t = 2) with string fragments as single symbols (k=1) displaced by d (e.g., row ”AA“ and column d = 2 shows number of occurrences of ”A A“).
Heavy weight ontology learning using text documents

Ontology plays an important role not only for data processing in knowledge based systems but also, provide interoperability in heterogeneous environment and is a cornerstone of semantic web technology. The required technology is used for knowledge representation in OWL/RDF format and facilitate faster access of concepts in domain of interest. Development of ontology is a tedious job and requires a lot of man power in terms of experts' time and knowledge. Although there are various tools and techniques for light weight ontology learning; yet full automation of heavy weight ontology learning from text documents is a distant dream. In this paper we have proposed a framework for learning heavy weight ontology, using text documents written in English language. Initial experimental results are shown for demonstration of our on going research.
Prevention of autoimmune diabetes in non-obese diabetic mice by treatment with a class II major histocompatibility complex-blocking peptide

The role of antigen presentation as a possible mechanism underlying major histocompatibility complex (MHC) association of autoimmune disease has been studied in non-obese diabetic (NOD) mice. By screening for inhibition of antigen presentation to NOD T cell hybridoma, we have selected a synthetic peptide, yTYTVHAAHAYTYt (small letters denote D amino acids), that efficiently blocks antigen presentation by the NOD class II MHC molecule A alpha g7A beta g7 (Ag7) in vitro. The inhibition is MHC selective, in that it does not affect antigen presentation by the E(d) and E(k) molecules, and has only a marginal effect on presentation by the A(d) molecule. This peptide also inhibits the priming for Ag7-restricted T cell responses in vivo, and prevents the spontaneous development of diabetes in female NOD mice, when administered chronically from 3 wk of age on. Chronic treatment with a control peptide, KMKMVHAAHAKMKM, that fails to bind to Ag7 has no effect on the disease. These data indicate that antigen presentation by the Ag7 molecule plays a pivotal role in the induction of autoimmune diabetes. Furthermore, the results demonstrate that interference with antigen presentation by a class II molecule can prevent the onset of spontaneous autoimmune disease associated with the same molecule.
The Antioxidant Response Induced by Lonicera caerulaea Berry Extracts in Animals Bearing Experimental Solid Tumors

Lonicera caerulea is a species of bush native to the Kamchatka Peninsula (Russian Far East) whose berries have been extensively studied due to their potential high antioxidant activity. The aim of our work was to investigate the in vivo effects of the antioxidant action of Lonicera caerulea berry extracts on the dynamics of experimentally-induced tumors. Our data showed that aqueous Lonicera caerulaea extracts reduced the tumor volume when administered continuously during the tumor growth and development stages, but augmented the tumor growth when the administration of extracts started three weeks before tumor grafting. Prolonged administration of Lonicera caerulaea berry extracts induced the antioxidant defense mechanism in the tumor tissues, while surprisingly amplifying the peripheral oxidative stress.
Automatic Animation Skeleton Extraction Method Based on Vertex Gauss Sphere Representation

Skeletonization, or automatic skeleton extraction, is a technology in 3DCG which automatically extract skeletons (i.e. bones, joints and their hierarchical structures) from 3D models. Such skeletons are important shape and pose descriptors for object representation, object recognition etc. Some existing skeletonization methods have difficulties in correctly extracting the position of joints. Some other methods are able to extract joints correctly to some extent, but controlling the number of bones and joints in their structure is not allowed. Therefore applying motion data acquired from motion capture devices to 3D models still involves a lot of manual work. In this paper, we propose a novel animation skeletonization method suited for CG animation based on Gauss sphere representation. By applying vertex Gauss sphere representation first and then applying template matching approach regardless of the object's shape, the proposed method is able to extract the same numbers of joints or bones in the same structure as in given motion data, i.e. one can directly apply existing motion data without the need of manual adjustment. Experimental results showed that the proposed method achieves 90% accuracy of pose estimation and 73% accuracy of joint estimation.
An Intention-Aware Interface for Services Access Enhancement

An intention-aware services query tool is proposed to enhance the access of services-oriented systems in more efficient and convenient way. The user can request the services by entering a phrase to interact with the system. The system capabilities are represented by a goal structure composed of computable goal models. The request string will be parsed, interpreted and mapped to candidate goal models for representing the user requests. Three types of background knowledge, domain ontology, user's profile and goal structure, are used in the mapping process. The concrete actions of the system, such as user interaction, Web services request generation, and service returned results processing are stored in apian library. The system could refer to the selected goal model and the plan library to generate a plan for user request satisfaction
A Testing Method for the Image Crosstalk of Stereoscopic Television

Cross talk is the key factor in determining the quality of stereoscopic television image. There is no normal distribution law of the stereoscopic television image cross talk value, so the traditional black and white image signal is not suitable for this 3DTV cross talk test. in this paper, we select a few points as the measuring points and the 256 gray-scale changes of the image have been considered. We use a multi-level gray signal as the measurement signal and propose a reliable method to evaluate the values of 3DTV cross talk. the proposed method may be helpful for the comprehensive evaluation and optimization design of the 3D display.
Linked Topic and Interest Model for Web Forums

In Web forum analysis, both the discussion topics and author interests are greatly concerned. We introduce a linked topic and interest model based on latent Dirichlet allocation (LDA) to explore discussion topics and author interests. Rather than having two separate models or modeling combined topics and interests with just one hidden topic assignment variable, the proposed model has separate but linked hidden variables for topic and interest exploration. As exact model parameter inference is intractable, Gibbs sampling is employed to estimate topic, author, and interest distributions. The joint distribution of the linked hidden variables also provides an interpretation of an interest in terms of weighted topics or vice versa. We apply the model to a NIPS data set and a corpus containing text contents of a popular digital camera Web forum. Topics and interests discovered by using the model is demonstrated. The model generalization capability is also assessed by means of perplexity and the results show that the linked topic and interest model has performance exceeding that of LDA document topic model and author topic model.
Managing Security in Aspect Oriented Recommender System

Recommender systems are widely used in intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. A recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach) or a combination of both (Hybrid-filtering approach). Recent research shows that collaborative recommender systems are highly vulnerable to profiles injection attacks. Therefore, security mechanisms are needed for protecting the recommender systems against these attacks. Aspect Oriented Recommender System (AORS) is a proposed multi agent system (MAS) that uses the concept of Aspect Oriented Programming (AOP) for building security aspect. Implementing the security in recommender system using a conventional agent oriented approach results not only with the problem of code scattering and code tangling, but also results in weaker enforcement of security concern. In this paper, security crosscutting is handled as aspect in AORS in a modular way to remove scattering and tangling problems. The prototype of AORS has been designed and developed for a book recommender system.
Tumor-targeting Salmonella typhimurium A1-R arrests growth of breast-cancer brain metastasis

Brain metastasis is a morbid, treatment-resistant, end-stage frequent occurrence in breast cancer patients. The aim of this study was to evaluate the efficacy of tumor-targeting Salmonella typhimurium A1-R on breast cancer brain metastases. High brain-metastatic variants of murine 4T1 breast cancer cells expressing red fluorescent protein (RFP) were injected orthotopically in the mammary fat pad in non-transgenic nude mice or in the left ventricle of non-transgenic nude mice and transgenic nude mice expressing nestin-driven green fluorescent protein (ND-GFP). ND-GFP mice express GFP in nascent blood vessels. In the orthotopically-injected mice, the primary tumor was surgically-resected in order to allow brain metastasis to develop. At various time points, the tumors and vasculature in the brain were imaged by confocal and stereo fluorescence microscopy. Some of the breast cancer cells that reached the brain extravasated and grew perivascularly and some of the cells proliferated within the vasculature. S. typhimurium A1-R significantly inhibited brain metastasis in both metastatic models and increased survival of the orthotopically-transplanted, primary-tumor-resected mice (p<0.05). The results of the present study suggest the clinical potential of bacterial therapy of breast cancer brain metastasis.