NOWADAYS, THE EXPANDED USE OF LBSS INVOLVES OPPORTUNITIES TO THE ADVERSARIES THREATENING THE LOCATION PRIVACY OF MOBILE USERS. SEVERAL APPROACHES HAVE BEEN PROPOSED TO TACKLE EITHER LOCATION PRIVACY, LOCATION SAFETY, AND QUERY PRIVACY INDEPENDENTLY. IN THIS PAPER, WE PRESENT A WORK IN PROGRESS, WHICH AIMS TO PROPOSE A UNIFIED FRAMEWORK TO PROTECT PRIVACY IN ALL THESE DIMENSIONS SIMULTANEOUSLY. THE DEMAND FOR QUERY-PRIVACY PROTECTION FOR MANY USERS WILL BE ADDRESSED IN BATCH.

IN THIS PAPER WE PRESENT A CONTEXTUAL MODELING APPROACH FOR MODEL-BASED RECOMMENDER SYSTEMS THAT INTEGRATES AND EXPLOITS BOTH USER PREFERENCES AND CONTEXTUAL SIGNALS IN A COMMON VECTOR SPACE. DIFFERENTLY TO PREVIOUS WORK, WE CONDUCT A USER STUDY ACQUIRING AND ANALYZING A VARIETY OF REALISTIC CONTEXTUAL SIGNALS ASSOCIATED TO USER PREFERENCES IN SEVERAL DOMAINS. MOREOVER, WE REPORT EMPIRICAL RESULTS EVALUATING OUR APPROACH IN THE MOVIE AND MUSIC DOMAINS, WHICH SHOW THAT ENHANCING MODEL-BASED RECOMMENDER SYSTEMS WITH TIME, LOCATION AND SOCIAL COMPANION INFORMATION IMPROVES THE ACCURACY OF GENERATED RECOMMENDATIONS.

ITEM SPLITTING IS A CONTEXT-AWARE RECOMMENDATION TECHNIQUE BASED ON COLLABORATIVE FILTERING (CF), WHICH GROUPS AND EXPLOITS RATINGS ACCORDING TO THE CONTEXTS IN WHICH THEY WERE GENERATED. IT SHOWS POSITIVE EFFECTS ON RECOMMENDATION ACCURACY IN THE PRESENCE OF SIGNIFICANT DIFFERENCES BETWEEN THE USERS' PREFERENCES FROM DISTINCT CONTEXTS. TO DETERMINE WHETHER SUCH DIFFERENCES ARE SIGNIFICANT, IN THIS PAPER WE PROPOSE A NOVEL IMPURITY CRITERION BASED ON THE FISHER'S EXACT TEST, WHICH RETURNS A SCORE ON THE DIFFERENCE BETWEEN RATINGS GIVEN TO AN ITEM. EXPERIMENTAL RESULTS ON A DATASET OF MOVIE RATINGS SHOW A LOWER RATING PREDICTION ERROR WITH RESPECT TO OTHER IMPURITY CRITERIA - IN PARTICULAR, RELATED WITH TIME CONTEXT SIGNALS - , LETTING US IMPROVE THE RECOMMENDATION PERFORMANCE OF A STATE-OF-THE-ART CF ALGORITHM IN AN OFFLINE EVALUATION SETTING THAT SIMULATES REAL-WORLD CONDITIONS.

SOFTWARE DEVELOPMENT METHODOLOGIES ARE FUNDAMENTAL FOR THE PROPER DEVELOPMENT OF SOFTWARE PROJECTS. HOWEVER, THE MOST PERTINENT ONES ARE NOT ALWAYS SELECTED DEPENDING ON THE RESOURCES AVAILABLE, WHICH COULD RESULT IN FAILED PROJECTS. IN FACT, SEVERAL REPORTS SHOW A HIGH RATE OF FAILURE IN SOFTWARE PROJECTS. THE TYPE OF DEVELOPMENT METHODOLOGY CHOSEN IS ONE IMPORTANT FACTOR WHEN DIFFERENTIATING BETWEEN SUCCESSFUL FROM UNSUCCESSFUL PROJECTS. FOR EXAMPLE, IT HAS BEEN REPORTED THAT PROJECTS THAT USE AGILE PRACTICES HAVE A GREATER PERCENTAGE OF SUCCESS. HOWEVER, AGILE METHODOLOGIES ARE NOT NECESSARILY APPROPRIATE FOR ANY TYPE OF PROJECT. GIVEN THE ABOVE, A DEVELOPMENT METHODOLOGIES RECOMMENDATION SYSTEM PROTOTYPE WAS DEVELOPED, THAT ALLOWS GUIDING DEVELOPERS WITH LOW EXPERIENCE OR KNOWLEDGE TO SELECT MORE APPROPRIATE METHODOLOGIES ACCORDING TO DIFFERENT CRITERIA. THESE CRITERIA WERE OBTAINED FROM BIBLIOGRAPHIC SOURCES AND THEN VALIDATED AND COMPLEMENTED THROUGH AN EMPIRICAL STUDY WHERE SURVEYS WERE APPLIED TO PROFESSIONALS OF COMPANIES THAT DEVELOP SOFTWARE IN CHILE.

THIS PAPER PRESENTS AN APPROACH TO TAKE ADVANTAGE OF REVIEWS WRITTEN IN SPANISH TO GENERATE ASPECT-BASED RECOMMENDATIONS. ALTHOUGH DIVERSE APPROACHES HAVE BEEN PROPOSED AND EVALUATED FOR REVIEWS WRITTEN IN ENGLISH, THERE IS A LACK OF PROPOSALS TO ACCOMPLISH THIS TASK USING REVIEWS IN SPANISH. THE PROPOSED APPROACH USES TEXT MINING TECHNIQUES AND TOOLS TO EXTRACT ITEM ASPECTS AND ESTIMATE USER PREFERENCES FOR EACH OF THEM. THE ESTIMATED PREFERENCES THEN FEED A MULTI-CRITERIA RECOMMENDER SYSTEM, CONSIDERING EACH EXTRACTED ASPECT AS A CRITERION REGARDING USER PREFERENCE. PRELIMINARY RESULTS SHOW THAT, USING ASPECTS EXTRACTED FROM REVIEWS WRITTEN IN SPANISH, IT IS POSSIBLE TO IMPROVE THE QUALITY OF RECOMMENDATIONS GENERATED WITH A TRADITIONAL ALGORITHM. DUE TO THE SIMPLICITY OF THE PROPOSED APPROACH, IT CAN BE EASILY USED BY LOCAL COMPANIES TO INCORPORATE ASPECT-BASED RECOMMENDATIONS.

A VARIETY OF RETROFIT OPTIONS CAN BE UNDERTAKEN TO REDUCE THE GROWING ENERGY CONSUMPTION AND ENSURE INDOOR COMFORT LEVELS IN HOUSING. THESE OFFER SPECIFIC SOLUTIONS WHOSE ENERGY PERFORMANCE CAN BE DETERMINED USING BUILDING THERMAL PERFORMANCE SIMULATIONS. THIS ARTICLE PRESENTS A COMPUTER TOOL TO HELP ANALYSE ALTERNATIVES TO IMPROVE ENERGY PERFORMANCE FOR SINGLE-FAMILY HOUSING IN THE CITY OF CONCEPCIÓN, CHILE. THIS SOFTWARE PROVIDES A NOVEL PROCEDURE TO MANAGE THERMAL SIMULATIONS, DEFINING RETROFIT PACKAGES BASED ON SIMULATION RESULTS AND THAT INCLUDE PRICE CALCULATIONS. THE SOFTWARE ALSO ALLOWS ESTIMATES TO BE MADE USING DATA FROM PREVIOUSLY STUDIED HOUSING IN THE REGION. BACKGROUND DATA ARE PRESENTED ON THE HOUSING TYPE UNDER STUDY AS WELL AS THE PROPOSED RETROFIT OPTIONS AND THE ANALYSIS OF SOME CASE STUDIES. THE SOFTWARE CAN SUGGEST RETROFIT PACKAGES WHICH PROVIDE REDUCTIONS THAT RANGE FROM A QUARTER TO HALF THE TOTAL ENERGY DEMAND AT A COST OF BETWEEN 5 AND 15% OF TOTAL HOUSE VALUE. THESE RESULTS SHOW THAT THE PROPOSED TOOL WORKS PROPERLY AND PROVIDES GOOD ENERGY-EFFICIENCY RETROFIT RECOMMENDATIONS FOR THIS TYPE OF HOUSING, ALLOWING IT TO BE USED AS AN INNOVATIVE PUBLIC DISSEMINATION INSTRUMENT. FURTHERMORE, IT CAN BE EXTENDED TO MANAGE SIMULATIONS OF OTHER DWELLING TYPOLOGIES OR REGIONS TO SUPPORT PROFESSIONAL WORK.

THE APPLICATION OF DEEP LEARNING MODELS IN FRUIT ANALYSIS HAS GARNERED SIGNIFICANT ATTENTION DUE TO ITS POTENTIAL TO REVOLUTIONIZE THE AGRICULTURAL SECTOR AND ENHANCE CROP MONITORING. THIS PAPER PRESENTS A COMPREHENSIVE REVIEW OF RECENT RESEARCH EFFORTS IN FRUIT ANALYSIS USING DEEP LEARNING TECHNIQUES. THE STUDY DELVES INTO MODEL SELECTION, DATASET CHARACTERISTICS, EVALUATION METRICS, CHALLENGES, AND FUTURE DIRECTIONS IN THIS DOMAIN. VARIOUS MODEL ARCHITECTURES, INCLUDING CLASSICAL CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND ADVANCED DETECTION MODELS LIKE R-CNN AND YOLO, HAVE BEEN EXPLORED FOR TASKS RANGING FROM FRUIT CLASSIFICATION TO DETECTION. EVALUATION METRICS SUCH AS PRECISION, RECALL, F1-SCORE, AND MEAN AVERAGE PRECISION (MAP) HAVE BEEN COMMONLY USED TO ASSESS MODEL PERFORMANCE. CHALLENGES, INCLUDING DATA SCARCITY, LABELING COMPLEXITIES, VARIATIONS IN FRUIT CHARACTERISTICS, AND COMPUTATIONAL EFFICIENCY, HAVE BEEN IDENTIFIED AND DISCUSSED. THE PAPER ALSO PRESENTS AN OVERVIEW OF AVAILABLE DATASETS, ENCOMPASSING BOTH PROPRIETARY AND PUBLICLY ACCESSIBLE SOURCES. FUTURE RESEARCH DIRECTIONS INVOLVE EXPLORING ENHANCED DATA AUGMENTATION, MULTI-MODAL INTEGRATION, KNOWLEDGE TRANSFER ACROSS SPECIES, ROBUSTNESS IN DYNAMIC ENVIRONMENTS, IMPROVED COMPUTATIONAL EFFICIENCY, AND PRACTICAL INTEGRATION OF MODELS INTO REAL-WORLD AGRICULTURAL SYSTEMS. THIS REVIEW PROVIDES VALUABLE INSIGHTS FOR RESEARCHERS AND PRACTITIONERS AIMING TO LEVERAGE DEEP LEARNING FOR FRUIT ANALYSIS IN THE PURSUIT OF SUSTAINABLE AGRICULTURE AND FOOD PRODUCTION.

CURRENTLY, SEVERAL COUNTRIES ARE SEEKING TO CHANGE THEIR ENERGY MATRICES TOWARDS MORE SUSTAINABLE SOURCES. IN CHILE, ONE OF THE RENEWABLE SOURCES WITH INCREASED PARTICIPATION IS PHOTOVOLTAICS. HOWEVER, PHOTOVOLTAIC ENERGY SOURCES HAVE AN INTRINSIC VARIABILITY, WHICH COMBINED WITH VARIABLE DEMAND IMPOSES A CHALLENGE FOR PROPER DESIGN. CURRENTLY, TOOLS AVAILABLE FOR THE STUDY OF THIS VARIABILITY ARE EITHER COMPLEX OR EXPENSIVE. WITH THE ADVENT OF DIGITALIZATION, THERE IS AN OPPORTUNITY TO INCORPORATE TOOLS BASED ON ARTIFICIAL INTELLIGENCE TO IMPROVE FORECASTING FOR MEDIUM AND LOW POWER INSTALLATIONS. THIS WORK PRESENTS AN APPLICATION OF MACHINE LEARNING TOOLS FOR IRRADIANCE AND POWER CONSUMPTION FORECASTING. THE METHODOLOGY IS INTENDED TO BE IMPLEMENTED AS A LOW COST SOLUTION FOR SMALL SCALE GENERATION. THE RESULTS SHOW THAT IT IS POSSIBLE TO PREDICT IRRADIANCE AND ENERGY CONSUMPTION THROUGH HISTORICAL DATA, CONCLUDING THAT THE METHODOLOGY BASED ON MACHINE LEARNING IS ABLE TO SUPPORT THE DECISION MAKING FOR THE IMPROVEMENT OF PHOTOVOLTAIC SYSTEMS.

ENERGY CONSUMPTION REPRESENTS A HIGH OPERATIONAL COST IN MINING OPERATIONS. ORE SIZE REDUCTION STAGE IS THE MAIN CONSUMER IN THAT PROCESS, WHERE THE SEMIAUTOGENOUS MILL (SAG) IS ONE OF THE MAIN COMPONENTS. THE IMPLEMENTATION OF CONTROL AND AUTOMATION STRATEGIES THAT CAN ACHIEVE PRODUCTION GOALS ALONG WITH ENERGY EFFICIENCY ARE A COMMON GOAL IN CONCENTRATOR PLANTS; HOWEVER, DESIGNING SUCH CONTROLS REQUIRES A PROPER UNDERSTANDING OF PROCESS DYNAMICS WHICH ARE HIGHLY COMPLEX. THIS WORK STUDIES MACHINE LEARNING AND DEEP LEARNING STRATEGIES THAT CAN BE USED TO GENERATE MODELS FOR PREDICTING ENERGY CONSUMPTION, USING KEY PROCESS VARIABLES. IN PARTICULAR, THE APPLICATION OF K-NEAREST NEIGHBORS REGRESSOR (KNN-REG), POLYNOMIAL REGRESSION (PR), SUPPORT VECTOR REGRESSION (SVR) AND LONG-SHORT TERM MEMORY (LSTM) STRATEGIES FOR ENERGY PREDICTION OVER SAG MILL PROCESS DATA IS DEVELOPED IN ORDER TO IDENTIFY CONFIGURATIONS SUITABLE TO BE IMPLEMENTED FOR REAL-TIME PREDICTION INTEGRATED OVER INDUSTRIAL DATA INFRASTRUCTURES. ALL TECHNIQUES ARE COMPARED IN TERMS OF ROOT MEAN SQUARE ERROR (RMSE) WHERE, ALTHOUGH ALL THE MODELS ACHIEVED ACCEPTABLE PERFORMANCE, BEST RESULTS WERE OBTAINED BY A LSTM IMPLEMENTATION WHICH YIELDED AN ERROR OF LESS THAN 4% ASSOCIATED TO ENERGY PREDICTION.

CONTEXT-AWARE RECOMMENDER SYSTEMS HAVE BEEN PROVEN TO IMPROVE THE PERFORMANCE OF RECOMMENDATIONS IN A WIDE ARRAY OF DOMAINS AND APPLICATIONS. DESPITE INDIVIDUAL IMPROVEMENTS, LITTLE WORK HAS BEEN DONE ON COMPARING DIFFERENT APPROACHES, IN ORDER TO DETERMINE WHICH OF THEM OUTPERFORM THE OTHERS, AND UNDER WHAT CIRCUMSTANCES. IN THIS PAPER WE ADDRESS THIS ISSUE BY CONDUCTING AN EMPIRICAL COMPARISON OF SEVERAL PRE-FILTERING, POST-FILTERING AND CONTEXTUAL MODELING APPROACHES ON THE MOVIE RECOMMENDATION DOMAIN. TO ACQUIRE CONFIDENT CONTEXTUAL INFORMATION, WE PERFORMED A USER STUDY WHERE PARTICIPANTS WERE ASKED TO RATE MOVIES, STATING THE TIME AND SOCIAL COMPANION WITH WHICH THEY PREFERRED TO WATCH THE RATED MOVIES. THE RESULTS OF OUR EVALUATION SHOW THAT THERE IS NEITHER A CLEAR SUPERIOR CONTEXTUALIZATION APPROACH NOR AN ALWAYS BEST CONTEXTUAL SIGNAL, AND THAT ACHIEVED IMPROVEMENTS DEPEND ON THE RECOMMENDATION ALGORITHM USED TOGETHER WITH EACH CONTEXTUALIZATION APPROACH. NONETHELESS, WE CONCLUDE WITH A NUMBER OF CUES AND ADVICES ABOUT WHICH PARTICULAR COMBINATIONS OF CONTEXTUALIZATION APPROACHES AND RECOMMENDATION ALGORITHMS COULD BE BETTER SUITED FOR THE MOVIE RECOMMENDATION DOMAIN.

THE LONG-TERM MINE PLANNING MODEL (LTMP) AND SHORT-TERM MINE PLANNING MODEL (STMP) ARE TWO APPROACHES THAT DESCRIBE THE ORE CONTENT IN A MINE; THEY ARE ESSENTIAL INTANGIBLE RESOURCES THAT DETERMINE A MINING OPERATION AND ITS FEASIBILITY. THESE MODELS ARE OBTAINED WITH GEOSTATISTICAL METHODS AND, GIVEN THEIR NATURE, ARE PRONE TO DISCREPANCIES WITH ONE ANOTHER. TO REDUCE THESE DIFFERENCES, WE STUDIED THE PERFORMANCE OF DEEP LEARNING (DL)-BASED MODELS IN ORE GRADE ESTIMATION FOR A COPPER MINE IN CHILE. SPECIFICALLY, FEEDFORWARD NEURAL NETWORK (FNN), ONE-DIMENSIONAL (1D) CONVOLUTIONAL NEURAL NETWORK (CNN), AND LONG SHORT-TERM MEMORY (LSTM) MODELS WERE ANALYZED. THE EXPERIMENT CONSISTED OF A DATASET WITH 732,870 SAMPLES, OBTAINED AFTER DATA CLEANING AND SELECTION. THE USE OF SPATIAL INFORMATION IN THE SAMPLES WAS ALSO STUDIED, ADDING CONTEXTUAL INFORMATION FOR ESTIMATION. THIS LED TO A DATASET OF 545,768 SAMPLES THAT WERE USED TO EVALUATE 1D CNN AND LSTM MODELS. ARCHITECTURE TUNING WAS PERFORMED BY THE K-FOLD CROSS-VALIDATION (CV) METHOD, AND HYPERPARAMETERS SUCH AS THE NUMBER OF LAYERS, NUMBER OF NEURONS, ACTIVATION FUNCTION, AND KERNEL SIZE WERE OPTIMIZED. THE RESULTING HYPERPARAMETERS WERE USED TO PERFORM A FINAL EVALUATION. PERFORMANCE WAS MEASURED BASED ON THE MEAN ABSOLUTE ERROR (MAE), MEAN SQUARED ERROR (MSE), AND COEFFICIENT OF DETERMINATION (R2) METRICS. A BASELINE IS CREATED USING LTMP AND STMP ESTIMATES TO QUANTIFY THE IMPROVEMENT IN THE PERFORMANCE OF DL-BASED MODELS. THE EXPERIMENTAL RESULTS REVEALED THE ABILITY OF DL-BASED MODELS TO SIGNIFICANTLY IMPROVE COPPER GRADE ESTIMATES PROVIDED BY STANDARD MINING INDUSTRY METHODS IN THE CONTEXT UNDER STUDY. FOR MSE IN THE FINAL TESTS, FNN IMPROVED BY 21%, CNN BY 37%, AND LSTM BY 39% OVER BASELINES.

WAVE ENERGY HAS THE POTENTIAL TO PROVIDE A SUSTAINABLE SOLUTION FOR GLOBAL ENERGY DEMANDS, PARTICULARLY IN COASTAL REGIONS. THIS STUDY EXPLORES THE USE OF REINFORCEMENT LEARNING (RL), SPECIFICALLY THE Q-LEARNING ALGORITHM, TO OPTIMISE THE ENERGY EXTRACTION CAPABILITIES OF A WAVE ENERGY CONVERTER (WEC) USING A SINGLE-BODY POINT ABSORBER WITH RESISTIVE CONTROL. EXPERIMENTAL VALIDATION DEMONSTRATED THAT Q-LEARNING EFFECTIVELY OPTIMISES THE POWER TAKE-OFF (PTO) DAMPING COEFFICIENT, LEADING TO AN ENERGY OUTPUT THAT CLOSELY ALIGNS WITH THEORETICAL PREDICTIONS. THE STABILITY OBSERVED AFTER APPROXIMATELY 40 EPISODES HIGHLIGHTS THE CAPABILITY OF Q-LEARNING FOR REAL-TIME OPTIMISATION, EVEN UNDER IRREGULAR WAVE CONDITIONS. THE RESULTS ALSO SHOWED AN IMPROVEMENT IN EFFICIENCY OF 12% FOR THE THEORETICAL CASE AND 11.3% FOR THE EXPERIMENTAL CASE FROM THE INITIAL TO THE OPTIMISED STATE, UNDERSCORING THE EFFECTIVENESS OF THE RL STRATEGY. THE SIMPLICITY OF THE RESISTIVE CONTROL STRATEGY MAKES IT A VIABLE SOLUTION FOR PRACTICAL ENGINEERING APPLICATIONS, REDUCING THE COMPLEXITY AND COST OF DEPLOYMENT. THIS STUDY PROVIDES A SIGNIFICANT STEP TOWARDS BRIDGING THE GAP BETWEEN THE THEORETICAL MODELLING AND EXPERIMENTAL IMPLEMENTATION OF RL-BASED WEC SYSTEMS, CONTRIBUTING TO THE ADVANCEMENT OF SUSTAINABLE OCEAN ENERGY TECHNOLOGIES.

OPINIONS ABOUT ITEM?S ASPECTS IN USER REVIEWS MAY BE A VALUABLE SOURCE OF INFORMATION FOR IMPROVING RECOMMENDER SYSTEMS PERFORMANCE. DESPITE IMPORTANT ADVANCES IN NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING FIELDS THAT ALLOW THE EXTRACTION OF SUCH INFORMATION FROM TEXTS WRITTEN IN ENGLISH, THERE IS STILL WORK TO DO IN THE CASE OF TEXTS IN OTHER LANGUAGES. IN THIS WORK, WE ADAPT KNOWN UNSUPERVISED ASPECT EXTRACTION APPROACHES TO EXTRACT OPINIONS FROM REVIEWS WRITTEN IN SPANISH. OUR RESULTS SHOW THAT THE EXPLOITATION OF ASPECTS EXTRACTED AUTOMATICALLY FROM REVIEWS WRITTEN IN SPANISH ALLOW TO IMPROVE CONSIDERABLY THE TOP-K RECOMMENDATION LISTS GENERATED BY ASPECT-BASED RECOMMENDER SYSTEMS.

IN THIS PAPER WE DESCRIBE A NOVEL APPROACH TO EXTRACT CONTEXTUAL INFORMATION FROM USER REVIEWS, WHICH CAN BE EXPLOITED BY CONTEXT-AWARE RECOMMENDER SYSTEMS. THE APPROACH MAKES USE OF A GENERIC, LARGE-SCALE CONTEXT TAXONOMY THAT IS COMPOSED OF SEMANTIC ENTITIES FROM DBPEDIA, THE CORE ONTOLOGY AND KNOWLEDGE BASE OF THE LINKED DATA INITIATIVE. THE TAXONOMY IS BUILT IN A SEMI-AUTOMATIC FASHION THROUGH A SOFTWARE TOOL WHICH, ON THE ONE HAND, AUTOMATICALLY EXPLORES DBPEDIA BY ONLINE QUERYING FOR RELATED ENTITIES AND, ON THE OTHER HAND, ALLOWS FOR MANUAL ADJUSTMENTS OF THE TAXONOMY. THE PROPOSED APPROACH PERFORMS A APPING BETWEEN WORDS IN THE REVIEWS AND ELEMENTS OF THE TAXONOMY. IN THIS CASE, OUR TOOL ALSO ALLOWS FOR THE MANUAL VALIDATION AND CORRECTION OF EXTRACTED CONTEXT ANNOTATIONS. WE DESCRIBE THE TAXONOMY CREATION PROCESS AND THE DEVELOPED TOOL, AND FURTHER PRESENT SOME PRELIMINARY RESULTS REGARDING THE EFFECTIVENESS OF OUR APPROACH.

IN ORDER TO REDUCE THE INCREASING ENERGY CONSUMPTION FOR THE DOMESTIC DEMANDS OF EXISTING SINGLE-FAMILY HOUSING AND TAKE ADVANTAGE OF FREQUENT BUILDING ENLARGEMENTS, THIS PAPER PRESENTS A METHODOLOGY AND SUPPORTING SOFTWARE TOOL FOR DETERMINING THE OPTIMAL DESIGN CONFIGURATION OF AN ATTIC WITH INTEGRATED SOLAR COLLECTORS. THE ANALYSIS PROCEDURE IS BASED ON PARAMETRIC MODELING, ENERGY SIMULATION AND THE USE OF EVOLUTIONARY ALGORITHMS FOR FINDING OPTIMAL DESIGNS. IT HAS BEEN IMPLEMENTED AS A WEB-PLATFORM FOR PUBLIC USE THAT PROVIDES USERS WITH A PROPOSAL OF AN ATTIC SHAPE WITH MAXIMUM SOLAR ENERGY COLLECTION, MAXIMUM LIVING SPACE AND MINIMUM CONSTRUCTION ENVELOPE FOR EACH HOUSE ACCORDING ITS SIZE AND ORIENTATION. THE ATTIC INTEGRATES PV, THERMAL AND HYBRID SOLAR PANELS ON ONE SIDE OF THE ROOF. THIS PAPER DESCRIBES THE METHODOLOGY AND SOFTWARE DESIGN, ASSESSMENT OF THE WEB-PLATFORM USAGE AND CASE-STUDIES TO VERIFY ITS BEHAVIOR. IN A MATTER OF MINUTES, THE WEB-PLATFORM ENABLES USERS TO SELECT A SPECIFIC ATTIC DESIGN FOR EACH HOUSE THAT HAS INTEGRATED SOLAR COLLECTORS THAT CAN PRODUCE ENERGY TO COVER ALMOST 100% OF DOMESTIC ENERGY CONSUMPTION. THE ATTICS DESIGNED PROVIDE A NEARLY 30% INCREASE IN LIVING SPACE THROUGH THE EXTENSION OF ONE TO FOUR ROOMS, AND THE CONSTRUCTION COST OF THE ENVELOPE IS SIMILAR TO THAT OF A STANDARD HOUSING EXTENSION.

ENSURING A CERTAIN LEVEL OF QUALITY OF LEARNING OBJECTS USED IN E-LEARNING IS CRUCIAL TO INCREASE THE CHANCES OF SUCCESS OF AUTOMATED SYSTEMS IN RECOMMENDING OR FINDING THESE RESOURCES. THIS PAPER AIMS TO PRESENT A PROPOSAL FOR IMPLEMENTATION OF A QUALITY MODEL FOR LEARNING OBJECTS BASED ON ISO 9126 INTERNATIONAL STANDARD FOR THE EVALUATION OF SOFTWARE QUALITY. FEATURES INDICATORS ASSOCIATED WITH THE CONFORMANCE SUB-CHARACTERISTIC ARE DEFINED. SOME INSTRUMENTS FOR FEATURE EVALUATION ARE ADVISED, WHICH ALLOW COLLECTING EXPERT OPINION ON EVALUATION ITEMS. OTHER QUALITY MODEL FEATURES ARE EVALUATED USING ONLY THE INFORMATION FROM ITS METADATA USING SEMANTIC WEB TECHNOLOGIES. FINALLY, WE PROPOSE AN ONTOLOGY-BASED APPLICATION THAT ALLOWS AUTOMATIC EVALUATION OF A QUALITY FEATURE. IEEE LOM METADATA STANDARD WAS USED IN EXPERIMENTATION, AND THE RESULTS SHOWN THAT MOST OF LEARNING OBJECTS ANALYZED DO NOT COMPLAIN THE STANDARD.

REINFORCEMENT LEARNING (RL) TECHNIQUES ARE APPLIED IN DIFFERENT AREAS TO OPTIMIZE PARAMETERS, ONE APPLICATION IS THE USE OF RL IN THE ENERGY MAXIMIZATION OBTAINED FROM WAVE ENERGY CONVERTERS (WEC). THE MAIN ADVANTAGE OF RL IS THAT IT CAN OPTIMIZE THE GENERATION EVEN WHEN THERE ARE CHANGES IN THE WAVE AND IN THE WEC CHARACTERISTICS. Q-LEARNING AND SARSA RL-BASED APPROACHES ARE PRESENTED IN THIS WORK, IN ORDER TO OPTIMIZE A REACTIVE AND A RESISTIVE CONTROL APPLIED TO A LABORATORY-SCALE POINT ABSORBER WEC. THE PROPOSED APPROACHES ARE EVALUATED ON THREE REGULAR WAVE CONDITIONS USING A MODEL BASED ON A ONE-DEGREE OF FREEDOM SYSTEM, WHERE THE POWER TAKE OFF FORCES INCLUDE THE VARIABLE DAMPING AND STIFFNESS THAT ARE REGULATED BY THE CONTROL AND OPTIMIZED BY THE RL. RESULTS SHOWN A CORRECT BEHAVIOR OF THE RL ALGORITHMS OPTIMIZING BOTH CONTROL TECHNIQUES. NEVERTHELESS, REACTIVE CONTROL ACHIEVE UP TO 239% HIGHER ENERGY THAN THE RESISTIVE CONTROL FOR THE SAME CONDITIONS. IN RELATION WITH THE COMPARISON BETWEEN THE TWO RL ALGORITHMS, Q-LEARING PRESENT A FASTER CONVERGENCE THAN SARSA, BUT THE RESULTS FROM BOTH ALGORITHMS ARE PRACTICALLY THE SAME.

ONLINE SERVICES ARE USUALLY ACCESSED VIA HOUSEHOLD ACCOUNTS. A HOUSEHOLD ACCOUNT IS TYPICALLY SHARED BY VARIOUS USERS WHO LIVE IN THE SAME HOUSE. THIS REPRESENTS A PROBLEM FOR PROVIDING PERSONALIZED SERVICES, SUCH AS RECOMMENDATION. IDENTIFYING THE HOUSEHOLD MEMBERS WHO ARE INTERACTING WITH AN ONLINE SYSTEM (E.G. AN ON-DEMAND VIDEO SERVICE) IN A GIVEN MOMENT, IS THUS AN INTERESTING CHALLENGE FOR THE RECOMMENDER SYSTEMS RESEARCH COMMUNITY. PREVIOUS WORK HAS SHOWN THAT METHODS BASED ON THE ANALYSIS OF TEMPORAL PATTERNS OF USERS ARE HIGHLY ACCURATE IN THE ABOVE TASK WHEN THEY USE RANDOMLY SAMPLED TEST DATA. HOWEVER, SUCH EVALUATION METHODOLOGY MAY NOT PROPERLY DEAL WITH THE EVOLUTION OF THE USERS? PREFERENCES AND BEHAVIOR THROUGH TIME. IN THIS PAPER WE EVALUATE SEVERAL METHODS? PERFORMANCE USING TIME-AWARE EVALUATION METHODOLOGIES. RESULTS FROM OUR EXPERIMENTS SHOW THAT THE DISCRIMINATION POWER OF DIFFERENT TIME FEATURES VARIES CONSIDERABLY, AND MOREOVER, THE ACCURACY ACHIEVED BY THE METHODS CAN BE HEAVILY PENALIZED WHEN USING A MORE REALISTIC EVALUATION METHODOLOGY.

EXPLOITING TEMPORAL CONTEXT HAS BEEN PROVED TO BE AN EFFECTIVE APPROACH TO IMPROVE RECOMMENDATION PERFORMANCE, AS SHOWN, E.G. IN THE NETFLIX PRIZE COMPETITION. TIME-AWARE RECOMMENDER SYSTEMS (TARS) ARE INDEED RECEIVING INCREASING ATTENTION. A WIDE RANGE OF APPROACHES DEALING WITH THE TIME DIMENSION IN USER MODELING AND RECOMMENDATION STRATEGIES HAVE BEEN PROPOSED. IN THE LITERATURE, HOWEVER, REPORTED RESULTS AND CONCLUSIONS ABOUT HOW TO INCORPORATE AND EXPLOIT TIME INFORMATION WITHIN THE RECOMMENDATION PROCESSES SEEM TO BE CONTRADICTORY IN SOME CASES. AIMING TO CLARIFY AND ADDRESS EXISTING DISCREPANCIES, IN THIS PAPER WE PRESENT A COMPREHENSIVE SURVEY AND ANALYSIS OF THE STATE OF THE ART ON TARS. THE ANALYSIS SHOW THAT MEANINGFUL DIVERGENCES APPEAR IN THE EVALUATION PROTOCOLS USED?METRICS AND METHODOLOGIES. WE IDENTIFY A NUMBER OF KEY CONDITIONS ON OFFLINE EVALUATION OF TARS, AND BASED ON THESE CONDITIONS, WE PROVIDE A COMPREHENSIVE CLASSIFICATION OF EVALUATION PROTOCOLS FOR TARS. MOREOVER, WE PROPOSE A METHODOLOGICAL DESCRIPTION FRAMEWORK AIMED TO MAKE THE EVALUATION PROCESS FAIR AND REPRODUCIBLE. WE ALSO PRESENT AN EMPIRICAL STUDY ON THE IMPACT OF DIFFERENT EVALUATION PROTOCOLS ON MEASURING RELATIVE PERFORMANCES OF WELL-KNOWN TARS. THE RESULTS OBTAINED SHOW THAT DIFFERENT USES OF THE ABOVE EVALUATION CONDITIONS YIELD TO REMARKABLY DISTINCT PERFORMANCE AND RELATIVE RANKING VALUES OF THE RECOMMENDATION APPROACHES. THEY REVEAL THE NEED OF CLEARLY STATING THE EVALUATION CONDITIONS USED TO ENSURE COMPARABILITY AND REPRODUCIBILITY OF REPORTED RESULTS. FROM OUR ANALYSIS AND EXPERIMENTS, WE FINALLY CONCLUDE WITH METHODOLOGICAL ISSUES A ROBUST EVALUATION OF TARS SHOULD TAKE INTO CONSIDERATION. FURTHERMORE WE PROVIDE A NUMBER OF GENERAL GUIDELINES TO SELECT PROPER CONDITIONS FOR EVALUATING PARTICULAR TARS.