Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted aspect-based collaborative filtering

Existing work on collaborative filtering (CF) is often based on the overall ratings the items have received. However, in many cases, understanding how a user rates each aspect of an item may reveal more detailed information about her preferences and thus may lead to more effective CF. Prior work has studied extracting/quantizing sentiments on different aspects from the reviews, based on which the unknown overall ratings are inferred. However, in that work, all the aspects are treated equally; while in reality, different users tend to place emphases on difference aspects when reaching the overall rating. For example, users may give a high rating to a movie just for its plot despite its mediocre performances. This emphasis on aspects varies for different users and different items. In this paper, we propose a method that uses tensor factorization to automatically infer the weights of different aspects in forming the overall rating. The main idea is to learn, through constrained optimization, a compact representation of a weight tensor indexed by three dimensions for user, item, and aspect, respectively. Overall ratings can then be predicted using the obtained weights. Experiments on a movie dataset show that our method compares favorably with three baseline methods.
Comparison of Distributed Fusion Filters for Linear Dynamic System with Uncertainty

In this paper, a distributed fusion filtering problem for a linear discrete-time dynamic system with uncertainty is considered. All fusion filtering algorithms are based on fusion formulas which represent a weighted sum of the local Kalman estimates with matrix or scalar weights. The fusion weights are calculated by using four algorithms: convex combination, optimal fusion, covariance intersection, and median fusion. The comparison results of the fusion algorithms are discussed in terms of estimation accuracy and computation cost.
Comb-Type Grafted Hydrogels of PNIPAM and PDMAEMA with Reversed Network-Graft Architectures from Controlled Radical Polymerizations

Dual thermo- and pH-responsive comb-type grafted hydrogels of poly(N,N-dimethylaminoethyl methacrylate) (PDMAEMA) and poly(N-isopropylacrylamide) (PNIPAM) with reversed network-graft architectures were synthesized by the combination of atom transfer radical polymerization (ATRP), reversible addition-fragmentation chain transfer (RAFT) polymerization and click chemistry. Two kinds of macro-cross-linkers with two azido groups at one chain-end and different chain length [PNIPAM–(N3)2 and PDMAEMA–(N3)2] were prepared with N,N-di(β-azidoethyl) 2-halocarboxylamide as the ATRP initiator. Through RAFT copolymerization of DMAEMA or NIPAM with propargyl acrylate (ProA) using dibenzyltrithiocarbonate as a chain transfer agent, two network precursors with different content of alkynyl side-groups [P(DMAEMA-co-ProA) and P(NIPAM-co-ProA)] were obtained. The subsequent azido-alkynyl click reaction of macro-cross-linkers and network precursors led to the formation of the network-graft hydrogels. These dual stimulus-sensitive hydrogels exhibited rapid response, high swelling ratio and reproducible swelling/de-swelling cycles under different temperatures and pH values. The influences of cross-linkage density and network-graft architecture on the properties of the hydrogels were investigated. The release of ceftriaxone sodium from these hydrogels showed both thermal- and pH-dependence, suggesting the feasibility of these hydrogels as thermo- and pH-dependent drug release devices.
Using heterogeneous patent network features to rank and discover influential inventors

Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not applicable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different research fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor’s interesting topics using a latent Dirichlet allocation (LDA) model. Compared with the traditional methods such as PageRank, our approach takes full advantage of the information in the heterogeneous network, including the relationship between inventors and the relationship between the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.
Low Power Embedded Speech Enhancement System Based on a Fixed-Point DSP

In this paper, we introduce a practical real-time speech enhancement system based on the TI TMS320VC5509A digital signal processor (DSP). A stereo audio codec is used as the analog input/output (I/O). The algorithm for noise reduction is an improved and optimized spectral-subtraction method. The detailed design of the hardware and software is described and the experiments show an improved performance. Low power consumption and high efficiency make the whole system suitable for portable devices.
An extensive library of synthetic spectra covering the far red, RAVE and GAIA wavelength ranges

A library of 183 588 synthetic spectra based on Kurucz's ATLAS9 models is presented for the far red spectral interval (7653-8747 A). It is characterized by $3500~{\rm K} \le T_{\rm eff} \le 47\,500$ K, $0.0 \le \log g \le 5.0$, $-3.0 \le [{\rm M}/{\rm H}] \le +0.5$, $0 \le V_{\rm rot} \le 500$ km s -1 , $\xi = 2$ km s -1 . The whole grid of spectra is calculated for resolving powers 8500, 11 500 and 20 000. A section of the grid is also computed for [ $\alpha/{\rm Fe}]=+0.4$ and for microturbulent velocities 0 and 4 km s -1 . The library covers the wavelength ranges and resolutions of the two ambitious spectroscopic surveys by the ground experiment RAVE and the space mission GAIA. Cross-sections across the multi-dimensional data-cube are used to illustrate the behaviour of the strongest spectral lines. Interpretation of real data will have to include interpolation to grid substeps. We present a simple estimate of the accuracy of such a procedure.
A Toolkit for Exploring Affective Interface Adaptation in Videogames

From its humble beginnings back in the early 1960’s the videogame has become one of the most successful form of HCI to date. However if we look more closely at the interactions between the game and gamer it becomes evident little has changed since the advent of SpaceWar back in 1961. These interactions are for the most part static and thus predictable, given a particular set of circumstances a game will always react in one particular manner despite anything the player may actually do. Because of this the expected lifespan of a videogame is inherently dependant on the choices the videogame provides; once all possible avenues have been explored the game loses its appeal. In this paper we focus on adapting techniques used in the field of Affective Computing to solve this stagnation in the videogames market. We describe the development of a software development kit (SDK) that allows the interactions between man and machine to become dynamic entities during play by means of monitoring the player’s physiological condition.
A Semantic Metadata Generator for Web Pages Based on Keyphrase Extraction

The annotation of documents and web pages with semantic metatdata is an activity that can greatly increase the accuracy of Information Retrieval and Personalization systems, but the growing amount of text data available is too large for an extensive manual process. On the other hand, automatic keyphrase generation and wikification can significantly support this activity. In this demonstration we present a system that automatically extracts keyphrases, identifies candidate DBpedia entities, and returns as output a set of RDF triples compliant with the Opengraph and the Schema.org vocabularies.
Performance analysis of private information retrieval scheme based on homomorphic encryption

Private Information Retrieval (PIR) is the fact that a client retrieves a certain record from a remote database managed by untrusted parties without letting them know which record has actually been requested, thus preserving the privacy of the client. Ostrovsky and Skeith showed in [1] that any homomorphic encryption can actually be used to achieve PIR. In this paper we use their protocol as basis to compare five well known homomorphic encryption schemes by providing simulation results.
A new generation algorithm of fault tree minimal cut sets and its application in CBTC system

Fault Tree Analysis (FTA) is a classic method in reliability and safety engineering. With the increase of system scale and complexity, the amount of work is heavily loaded. Thus the computer aided FTA method is important for related analysis. Existing computer aided FTA analysis methods invariably confront combinatorial problems which are computationally expensive and often with exponential increase of complexity, especially when it comes to large scale fault trees. In this paper, we propose a method based on the Binary Decision Diagram (BDD) with new conversion rules which are derived from the Component Connection Method. Besides, the Depth First Search (DFS) is applied to generate MCSs (Minimal Cut Sets). In the end, the Zone Controller of the CBTC (Communication Based Train Control) system is analyzed in case study. The result proves that the application of our new algorithm, of linear complexity, is more efficient than the algorithm of verifying all possible basic event combinations, both in computation speed and memory usage.
Anomaly-based bug prediction, isolation, and validation: an automated approach for software debugging

Software defects, commonly known as bugs, present a serious challenge for system reliability and dependability. Once a program failure is observed, the debugging activities to locate the defects are typically nontrivial and time consuming. In this paper, we propose a novel automated approach to pin-point the root-causes of software failures. Our proposed approach consists of three steps. The first step is bug prediction, which leverages the existing work on anomaly-based bug detection as exceptional behavior during program execution has been shown to frequently point to the root cause of a software failure. The second step is bug isolation, which eliminates false-positive bug predictions by checking whether the dynamic forward slices of bug predictions lead to the observed program failure. The last step is bug validation, in which the isolated anomalies are validated by dynamically nullifying their effects and observing if the program still fails. The whole bug prediction, isolation and validation process is fully automated and can be implemented with efficient architectural support. Our experiments with 6 programs and 7 bugs, including a real bug in the gcc 2.95. 2 compiler, show that our approach is highly effective at isolating only the relevant anomalies. Compared to state-of-art debugging techniques, our proposed approach pinpoints the defect locations more accurately and presents the user with a much smaller code set to analyze.
Testing Satisfaction with a Group-based Social Work Field Liaison Model: A Controlled Mixed Methods Study

ABSTRACT Social work field education programs rely on the liaison process to oversee and support students during their placement and to assist them to integrate theory and practice. However, the role and importance of liaison has been threatened as social work programs seek alternative and cost-effective ways to undertake these functions. This paper presents findings from a pilot study investigating levels of satisfaction among students and field educators with an innovative group liaison model compared to a control group who experienced the standard liaison practice. Fifty-three students and 47 field educators completed the survey. Twenty-one of the students and 10 of the educators received the intervention (the group liaison); the remaining 32 students and 37 educators received the standard liaison process (the control group). Students in the intervention group reported significantly higher levels of satisfaction with all aspects of the liaison experience than students who received the standard liaison process. Field educators were more positive about liaison than students. This study reaffirms the contribution of face-to-face liaison contact to the student learning experience on placement and suggests that strengthening, rather than devaluing, the liaison role could be a useful strategy to encourage the field’s ongoing commitment to provision of quality placement-based learning opportunities. IMPLICATIONS Highlights the importance of face-to-face liaison contact to the student learning experience on placement. Examines social work student and field educator perceptions of and satisfaction with the liaison process and content. Describes a group liaison model that potentially offers a cost effective way to undertake the role.
Adaptive strategy selection in differential evolution

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization. Different strategies have been proposed for the offspring generation; but the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, the probability matching technique is employed in DE to autonomously select the most suitable strategy while solving the problem. Four credit assignment methods, that update the known performance of each strategy based on the relative fitness improvement achieved by its recent applications, are analyzed. To evaluate the performance of our approach, thirteen widely used benchmark functions are used. Experimental results confirm that our approach is able to adaptively choose the suitable strategy for different problems. Compared to classical DE algorithms and to a recently proposed adaptive scheme (SaDE), it obtains better results in most of the functions, in terms of the quality of the final results and convergence speed.
Gendered Opportunity and School-Based Victimization

Opportunity theory suggests that adolescents’ risks for school-based theft and assault victimization are related to low self-control and school-based routine activities, such as playing sports, joining extracurricular clubs, and engaging in unsupervised activities. Peer research indicates that friends’ characteristics may also create opportunities for victimization. Additional research supports that gender moderates the effects that lifestyles and friends have on victimization. We integrate these lines of inquiry by exploring how gender moderates the relationship among low self-control, routine activities, friends’ characteristics, and school-based victimization using a sample of 10th-grade public school students who participated in the Education Longitudinal Study of 2002. Using structural equation models, our results suggest that friends’ characteristics tend to matter more for females across both types of victimization. Other gendered effects exist—indicating that the effects of certain friends’ characteristics vary by gender according to the extent to which they influence participation in school misconduct.
Research on LXI Bus Based on Wireless Technology

In the past few years, with the development of short range communication, wireless network in the office had advanced quickly. Meantime, in the T&M(test and measurement) industry, distributed architecture becomes more and more popular, this paper tries to illustrate the idea to combine the LXI(LAN extensions for instrumentation) bus technology for instrumentation with the wireless network, and presents a Wireless LXI architecture which includes the synchronization and trigger mechanism for the first time. Finally performance of such an architecture in terms of achievable transmission rate and multiple-access capability based on WLAN and ultra-wideband (UWB) communication technology are estimated for the T&M application.
An Analytic Framework for Maritime Situation Analysis

Maritime domain awareness is critical for protecting sea lanes, ports, harbors, offshore structures and critical infrastructures against common threats and illegal activities. Limited surveillance resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this article, we propose a novel situation analysis framework to analyze marine traffic data and differentiate various scenarios of vessel engagement for the purpose of detecting anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. The proposed framework views vessel behavior as probabilistic processes and uses machine learning to model common vessel interaction patterns. We represent patterns of interest as left-to-right Hidden Markov Models and classify such patterns using Support Vector Machines.
The National Football League Combine: A Reliable Predictor of Draft Status?

The performance of 326 collegiate football players attending the 2000 National Football League combine was studied to determine whether draft status could be predicted from performance measurements. The combine measured height and weight along with 9 performance tests: 225-lb bench press test, 10-yd dash, 20-yd dash, 40-yd dash, 20-yd proagility shuttle, 60-yd shuttle, 3-cone drill, broad jump, and vertical jump. Prediction equations were generated for 7 position categories with varying degrees of accuracy—running backs (RBs), r2 = 1.00; wide receivers (WRs), r2 = 1.00; offensive linemen, r2 = 0.70; defensive linemen, r2 = 0.59; defensive backs (DBs), r2 = 1.00; linebackers, r2 = 0.22; and quarterbacks, r2 = 0.84. The successes of the prediction equations are related to the ability of the individual tests to assess the necessary skills for each position. This study concludes that the combine can be used to accurately predict draft status of RBs, WRs, and DBs. The equations can also be used as a good to fair estimate for other positions.
Optimistic Concurrency Control for Inverted Files in Text Databases

Inverted files are frequently used as index data structures for very large text databases. Most applications of th is data structure are for read-only query operations. However , the problem of introducing update operations has deserved little attention so far and yet it has important application s. In this paper we propose an optimistic concurrency control algorithm devised to handle mixes of update operations and read-only queries efficiently. We work on top of the BSP model of parallel computing and take advantage of a BSP Time Warp realization to formulate the proposed algorithm. We present a comparison with the traditional lock-based approach and the results show that optimism is particularly efficient in this case.
A study on the number of Hierarchical Rectangular Partitions of Order k

Given a rectangle R, a Rectangular Dissection (RD) is a subdivision of R into smaller rectangles by non-intersecting vertical or horizontal segments. A basic rectangle in a rectangular dissection is a rectangle which does not contain any other rectangles inside. In this paper we study the number of rectangular partitions which can be decomposed hierarchically. A rectangular partition is said to be a Hierarchical Rectangular Dissection (HRD) of order k if the rectangular dissection can be obtained by starting from a single rectangle by embedding rectangular dissections of at most k basic rectangles hierarchically. When k = 2 this is exactly the class of guillotine rectangular dissections. Rectangular dissections are studied in VLSI circuit design under the name of floorplans. Ackerman et al. proved that point-free rectangular dissections are in bijective correspondence with Baxter permutations. We characterize hierarchical rectangular dissections of order k, a sub-class of point-free rectangular dissections, based on the Baxter permutations corresponding to them. We provide a recurrence relation for the distinct number of hierarchical rectangular partitions of order k with n rooms by proving that they are in bijective correspondence with a family of rooted trees called generating trees of order k. Based on this recurrence relation we give a polynomial time algorithm for generating the number of such distinct rectangular dissections. This gives rise to family of integer sequences I = {Ik} where Ik,i correspond to the number different HRDk with i rooms. Based on the characterization of permutations corresponding to HRD’s of order k we prove that there are at least 3 n k which are in-decomposable, that is it is not HRDk for
Heteroscedastic Gaussian processes for data fusion in large scale terrain modeling

This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain modeling for a mobile robot. Building a model of large scale and complex terrain that can adequately handle uncertainty and incompleteness in a statistically sound manner is a very challenging problem. To obtain a comprehensive model of such terrain, typically, multiple sensory modalities as well as multiple data sets are required. This work uses Gaussian processes to model large scale terrain. The model naturally provides a multi-resolution representation of space, incorporates and handles uncertainties appropriately and copes with incompleteness of sensory information. Gaussian process regression techniques are applied to estimate and interpolate (to fill gaps in unknown areas) elevation information across the field. In this work, the GP modeling approach is extended to fuse multiple, multi-modal data sets to obtain a best estimate of the elevation given the individual data sets. The individual data sets are treated as different noisy samples of the same underlying terrain. Experiments performed on sparse GPS based survey data and dense laser scanner data taken at mine-sites are reported.
Low-molecular-weight fucoidan and high-stability fucoxanthin from brown seaweed exert prebiotics and anti-inflammatory activities in Caco-2 cells

Background The aim of this study is to investigate the anti-inflammatory effects of low-molecular-weight fucoidan (LMF) and high-stability fucoxanthin (HS-Fucox) in a lipopolysaccharide-induced inflammatory Caco-2 cell line co-culture with B. lactis. Methods We used various methods such as transepithelial resistance (TER) assay, cytokine secretion assay, and tight junction protein mRNA expression assay to examine LMF and HS-Fucox anti-inflammatory properties. Results LMF and HS-Fucox activated probiotic growth and reduced the inflammation of the intestinal epithelial cells. Moreover, the combination of LMFHS-Fucox dramatically enhanced the intestinal epithelial barrier and immune function against the lipopolysaccharide effect by inhibiting IL-1β and TNF-α and promoting IL-10 and IFN-γ. Conclusion These findings suggested that LMF and HS-Fucox, alone or in combination, could be the potential natural compounds to enhance the immune system and have an anti-inflammatory effect on the intestinal cells.