Fuzzy clustering with learnable cluster-dependent kernels

We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster-dependent Kernels (FLeCK), that learns the underlying cluster-dependent dissimilarity measure while seeking compact clusters. The learned dissimilarity is based on a Gaussian kernel function with cluster-dependent parameters. Each cluster’s parameter learned by FLeCK reflects the relative intra-cluster and inter-cluster characteristics. These parameters are learned by optimizing both the intra-cluster and the inter-cluster distances. This optimization is achieved iteratively by dynamically updating the partition and the local kernel. This makes the kernel learning task takes advantages of the available unlabeled data and reciprocally, the categorization task takes advantages of the learned local kernels. Another key advantage of FLeCK is that it is formulated to work on relational data. This makes it applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. Using synthetic and real data sets, we show that FLeCK learns meaningful parameters and outperforms several other algorithms. In particular, we show that when data include clusters with various inter- and intra-cluster distances, learning cluster-dependent parameters is crucial in obtaining a good partition.     

Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.     

Emotional Loneliness and Coping Strategies: A Reference to Older Malaysians at Nursing Homes

This article presents an in-depth qualitative study using a phenomenological approach to understand loneliness among elderly individuals in Malaysia. The objective of the study was to understand how the Malaysian elderly perceive and understand social isolation as well as loneliness, with the aim of identifying the factors that cause emotional loneliness among the elderly in nursing homes. In addition, this study also explored their coping strategies when dealing with loneliness. Semi-structured interviews were conducted with ten elderly participants from two different nursing homes in Kuala Lumpur and Selangor with representatives from the three major ethnic groups of Malaysia. Based on the results, there are several factors that cause the elderly to feel lonely – health factors, lack of family ties, and the lack of communication and cognitive factors, such as memory and perception. It was also found that internal (expectations and optimism) and external (work and activities) coping strategies play major roles in overcoming loneliness. In conclusion, some recommendations are made to respective party families and the government to consider when developing plans to help the elderly overcome loneliness, which could strengthen the family and social support system in Malaysia.     

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Collaborative learning in the clouds

Cloud computing has emerged as one of the most highly discussed topics both in the academic community and in the computing industry. While most of the work that has been conducted to explore this field focuses either on establishing the basis for cloud computing or almost exclusively on the issues surrounding security and data privacy, this paper takes the first exploratory step into exploring the actual internal working of cloud computing and demonstrates its viability for organizations, more specifically educational establishments . The paper starts by introducing the most important key clouds computing concepts, including virtualization technologies, Web services and Service Oriented Architectures (SOA), and distributed computing. Light will be then shed on the impact and potential benefits of cloud computing on teaching and learning in educational institutions. The paper closes by describing building a private cloud inside educational institution and highlights its offerings for students, staff and lecturers.     

Related Fritz Carlson type inequalities for Sugeno integrals

In this paper, two general related inequalities to Carlson type inequality for the Sugeno integrals on an abstract fuzzy measure space $(X, \mathcal{F})$ are studied. Several examples are given to illustrate the validity of these inequalities.     

Spatial and seasonal variations and ecotoxicological significance of sediment trace metal concentrations in Kebir-Rhumel basin (Northeast of Algeria)

This study sought to assess sediment contamination by trace metals (cadmium, chromium, cobalt, copper, manganese, nickel, lead and zinc), to localize contaminated sites and to identify environmental risk for aquatic organisms in Wadis of Kebir Rhumel basin in the Northeast of Algeria. Water and surficial sediments (0-5 cm) were sampled in winter, spring, summer and autumn from 37 sites along permanent Wadis of the Kebir Rhumel basin. Sediment trace metal contents were measured by Flame Atomic Absorption Spectroscopy. Trace metals median concentrations in sediments followed a decreasing order: Mn > Zn > Pb > Cr > Cu > Ni > Co > Cd. Extreme values (dry weights) of the trace metals are as follows: 0.6-3.4 microg/g for Cd, 10-216 microg/g for Cr, 9-446 microg/g for Cu, 3-20 microg/g for Co, 105-576 microg/g for Mn, 10-46 microg/g for Ni, 11-167 microg/g for Pb, and 38-641 microg/g for Zn. According to world natural concentrations, all sediments collected were considered as contaminated by one or more elements. Comparing measured concentrations with American guidelines (Threshold Effect Level: TEL and Probable Effect Level: PEL) showed that biological effects could be occasionally observed for cadmium, chromium, lead and nickel levels but frequently observed for copper and zinc levels. Sediment quality was shown to be excellent for cobalt and manganese but medium to bad for cadmium, chromium, copper, lead, nickel and zinc regardless of sites.     

Multiple responses analysis and modeling of Fenton process for treatment of high strength landfill leachate

Landfill leachate is one of the most recalcitrant wastes for biotreatment and can be considered a potential source of contamination to surface and groundwater ecosystems. In the present study, Fenton oxidation was employed for degradation of stabilized landfill leachate. Response surface methodology was applied to analyze, model and optimize the process parameters, i.e. pH and reaction time as well as the initial concentrations of hydrogen peroxide and ferrous ion. Analysis of variance showed that good coefficients of determination were obtained (R2 > 0.99), thus ensuring satisfactory agreement of the second-order regression model with the experimental data. The results indicated that, pH and its quadratic effects were the main factors influencing Fenton oxidation. Furthermore, antagonistic effects between pH and other variables were observed. The optimum H2O2 concentration, Fe(II) concentration, pH and reaction time were 0.033 mol/L, 0.011 mol/L, 3 and 145 min, respectively, with 58.3% COD, 79.0% color and 82.1% iron removals.     

Enhanced fuzzy-filtered neural networks for material fatigue prognosis

Although fuzzy-filtered neural networks (FFNN) have been used in pattern classification because of their unique characteristics in feature extraction, they usually have poor performance in forecasting applications due to their structure complexities especially in their consequent reasoning part. In this paper, an enhanced FFNN, EFFNN, is proposed for time series forecasting and material fatigue prognosis. A novel neural network scheme is developed to facilitate computation implementation. A new conjugate technique is proposed to improve training efficiency. The effectiveness of the developed EFFNN scheme and the related training technique is demonstrated by a series of simulation tests. The EFFNN is also implemented for material fatigue prognosis. Test results show that the developed EFFNN predictor is an effective forecasting tool; it can capture system dynamics effectively and track system characteristics accurately.     

Interfacial Connections between Organic Perovskite/n+ Silicon/Catalyst that Allow Integration of Solar Cell and Catalyst for Hydrogen Evolution from Water

The rapidly increasing solar conversion efficiency (PCE) of hybrid organic–inorganic perovskite (HOIP) thin-film semiconductors has triggered interest in their use for direct solar-driven water splitting to produce hydrogen. However, application of these low-cost, electronic-structure-tunable HOIP tandem photoabsorbers has been hindered by the instability of the photovoltaic-catalyst-electrolyte (PV+E) interfaces. Here, photolytic water splitting is demonstrated using an integrated configuration consisting of an HOIP/n⁺silicon single junction photoabsorber and a platinum (Pt) thin film catalyst. An extended electrochemical (EC) lifetime in alkaline media is achieved using titanium nitride on both sides of the Si support to eliminate formation of insulating silicon oxide, and as an effective diffusion barrier to allow high-temperature annealing of the catalyst/TiO₂-protected-n⁺silicon interface necessary to retard electrolytic corrosion. Halide composition is examined in the (FA_1-xCs_x)PbI₃ system with a bandgap suitable for tandem operation. A fill factor of 72.5% is achieved using a Spiro-OMeTAD-hole-transport-layer (HTL)-based HOIP/n⁺Si solar cell, and a high photocurrent density of −15.9 mA cm⁻² (at 0 V vs reversible hydrogen electrode) is attained for the HOIP/n⁺Si/Pt photocathode in 1 m NaOH under simulated 1-sun illumination. While this thin-film design creates stable interfaces, the intrinsic photo- and electro-degradation of the HOIP photoabsorber remains the main obstacle for future HOIP/Si tandem PEC devices.