THE SLACK-BASED ALGORITHMS ARE POPULAR BIN-FOCUS HEURISTICS FOR THE BIN PACKING PROBLEM (BPP). THE SELECTION OF SLACKS IN EXISTING METHODS ONLY CONSIDER PREDETERMINED POLICIES, IGNORING THE DYNAMIC EXPLORATION OF THE GLOBAL DATA STRUCTURE, WHICH LEADS TO NONFULLY UTILIZATION OF THE INFORMATION IN THE DATA SPACE. IN THIS PAPER, WE PROPOSE A NOVEL SLACK-BASED FLEXIBLE BIN PACKING FRAMEWORK CALLED REINFORCED BIN PACKING FRAMEWORK (RBF) FOR THE ONE-DIMENSIONAL BPP. RBF CONSIDERS THE RL-SYSTEM, THE INSTANCE-EIGENVALUE MAPPING PROCESS, AND THE REINFORCED-MBS STRATEGY SIMULTANEOUSLY. IN OUR WORK, THE SLACK IS GENERATED WITH A REINFORCEMENT LEARNING STRATEGY, IN WHICH THE PERFORMANCE-DRIVEN REWARDS ARE USED TO CAPTURE THE INTUITION OF LEARNING THE CURRENT STATE OF THE CONTAINER SPACE, THE ACTION IS THE CHOICE OF THE PACKING CONTAINER, AND THE STATE IS THE REMAINING CAPACITY AFTER PACKING. DURING THE CONSTRUCTION OF THE SLACK, AN INSTANCE-EIGENVALUE MAPPING PROCESS IS DESIGNED AND UTILIZED TO GENERATE THE REPRESENTATIVE AND CLASSIFIED VALIDATE SET. FURTHERMORE, THE PROVISION OF THE SLACK COEFFICIENT IS INTEGRATED INTO MBS-BASED PACKING PROCESS. EXPERIMENTAL RESULTS SHOW THAT, IN COMPARISON WITH FIT ALGORITHMS, MBS AND MBS?, RBF ACHIEVES STATE-OF-THE-ART PERFORMANCE ON BINDATA AND SCH_WAE DATASETS. IN PARTICULAR, IT OUTPERFORMS ITS BASELINE MBS AND MBS?, AVERAGING THE NUMBER INCREASE OF OPTIMAL SOLUTIONS OF 189.05% AND 27.41%, RESPECTIVELY.

WITH THE ADVENT OF COMPUTING SYSTEMS WITH ON-DIE INTEGRATED GRAPHICS PROCESSING UNIT (IGPU), NEW GENERAL-PURPOSE GPU PROGRAMMING CHALLENGES HAVE EMERGED FROM THESE HETEROGENEOUS PROCESSORS. WE PROPOSE A LOCK-FREE SKIPLIST FOR INTELS INTEGRATED GRAPHICS PROCESSOR THAT IS OPTIMIZED TO ACHIEVE THE BEST PERFORMANCE USING THE C FOR MEDIA FRAMEWORK. TO THE BEST OF OUR KNOWLEDGE, THIS IS THE FIRST IMPLEMENTATION OF A LOCK-FREE DATA STRUCTURE FOR IGPU. EXPERIMENTAL RESULTS SHOW THAT OUR PROPOSAL IS MORE COMPUTE-EFFICIENT THAN AN EXISTING DISCRETE GPU IMPLEMENTATION AND OUTPERFORMS STATE-OF-THE-ART LOCK-FREE AND LOCK-BASED SKIPLISTS FOR MULTI-CORE CPU, ACHIEVING UP TO 3.5X SPEEDUP. ADDITIONALLY, ENERGY SAVINGS OF UP TO 300% ARE OBTAINED WHEN RUNNING DIFFERENT SKIPLIST WORKLOADS ON IGPU INSTEAD OF CPU CORES, HENCE FURTHER IMPROVING ENERGY EFFICIENCY.

ONE OF THE MOST IMPORTANT STEPS IN OBTAINING A RELATIONAL MODEL FROM LEGACY SYSTEMS IS THE EXTRACTION OF FUNCTIONAL DEPENDENCIES (FDS) THROUGH DATA MINING TECHNIQUES. SEVERAL METHODS HAVE BEEN PROPOSED FOR THIS PURPOSE AND MOST USE DIRECT SEARCH METHODS THAT TRAVERSE THE SEARCH SPACE IN EXPONENTIAL TIME IN THE NUMBER OF ATTRIBUTES OF THE RELATION. AS IT IS NOT UNCOMMON TO FIND IN PRACTICE RELATIONS WITH TENS OF ATTRIBUTES, A NEED EXISTS TO FURTHER DEVELOP MORE EFFICIENT TECHNIQUES TO FIND FDS. THE METHOD STUDIED HERE FINDS THE MINIMAL SET OF MINIMAL FDS USING ALGORITHMS THAT SOLVE THE HYPERGRAPH DUALITY PROBLEM APPLIED ON THE COMPLEMENT OF THE REFUTATION HYPERGRAPH OF THE RELATION WITHOUT GOING THROUGH THE EXPONENTIAL SEARCH SPACE. AFTER SHOWING THAT THE EXTRACTION OF FDS CAN BE REDUCED TO THE HYPERGRAPH DUALITY PROBLEM, EXPERIMENTAL RESULTS ARE GIVEN AS VERIFICATION AND CHARACTERIZATION OF THE CORRECTNESS AND TIME COMPLEXITY OF THE PROPOSED TOOL.

HYBRID MEMORY SYSTEMS COMPOSED OF DRAM AND NON-VOLATILE MEMORY (NVM) PROMISE THE CAPACITY BENEFITS OF NVM AND THE LOW-LATENCY BENEFITS OF DRAM. MOST EXISTING HASH-BASED INDEXES ARE DESIGNED FOR NVM ONLY AND DO NOT EXPLOIT THE BENEFITS OF DRAM. IN THIS PAPER, WE PROPOSED A NOVEL HYBRID DRAM-NVM PERSISTENT AND CONCURRENT HASHING INDEX, NAMED MULTI-HASHING INDEX (MUHASH). MUHASH USES A MULTI-HASH FUNCTION SCHEME TO SOLVE THE CASCADING WRITE PROBLEM OF OPEN-ADDRESSED HASH-BASED INDEXES IN NVM. IT EMPLOYS A CUCKOO FILTER, AN APPROXIMATE MEMBERSHIP QUERY DATA STRUCTURE, TO PRUNE UNNECESSARY NVM ACCESSES FOR IMPROVING READ PERFORMANCE. TO MAXIMIZE THROUGHPUT IN MULTI-THREAD ENVIRONMENTS, MUHASH ALSO INCLUDES A FINE-GRAINED CONCURRENCY CONTROL MECHANISM. WE IMPLEMENTED MUHASH FOR INTEL OPTANE DC PERSIST MEMORY, AND SINGLE-CORE EXPERIMENTS SHOWS THAT MUHASH ACHIEVES UP TO 90% HIGHER READ THROUGHPUT COMPARED TO STATE-OF-THE-ART HASH-BASED INDEXES. ON MULTICORE EXPERIMENTS, MUHASH ACHIEVES NEAR-LINEAR SCALABILITY FOR ALL OPERATIONS.

SPACE EXPLORATION IS A HOT TOPIC IN THE APPLICATION FIELD OF MOBILE ROBOTS. PROPOSED SOLUTIONS HAVE INCLUDED THE FRONTIER EXPLORATION ALGORITHM, HEURISTIC ALGORITHMS, AND DEEP REINFORCEMENT LEARNING. HOWEVER, THESE METHODS CANNOT SOLVE SPACE EXPLORATION IN TIME IN A DYNAMIC ENVIRONMENT. THIS PAPER MODELS THE SPACE EXPLORATION PROBLEM OF MOBILE ROBOTS BASED ON THE DECISION-MAKING PROCESS OF THE COGNITIVE ARCHITECTURE OF SOAR, AND THREE SPACE EXPLORATION HEURISTIC ALGORITHMS (HAS) ARE FURTHER PROPOSED BASED ON THE MODEL TO IMPROVE THE EXPLORATION SPEED OF THE ROBOT. EXPERIMENTS ARE CARRIED OUT BASED ON THE EASTER ENVIRONMENT, AND THE RESULTS SHOW THAT HAS HAVE IMPROVED THE EXPLORATION SPEED OF THE EASTER ROBOT AT LEAST 2.04 TIMES OF THE ORIGINAL ALGORITHM IN EASTER, VERIFYING THE EFFECTIVENESS OF THE PROPOSED ROBOT SPACE EXPLORATION STRATEGY AND THE CORRESPONDING HAS.

EFFICIENT RESOURCE MANAGEMENT IN A CLOUD DATA CENTER RELIES ON MINIMIZING ENERGY CONSUMPTION AND UTILIZING PHYSICAL RESOURCE EFFICIENTLY WHILE MAINTAINING THE SERVICE-LEVEL AGREEMENT (SLA) AT ITS HIGHEST LEVEL. TO ACHIEVE THIS GOAL, DYNAMICALLY CONSOLIDATING VIRTUAL MACHINES (VMS) IS CONSIDERED A PROMISING METHOD, BECAUSE IT ELIMINATES THE HOTSPOTS RESULTING FROM OVERLOADED HOSTS AND SWITCHES THE UNDERLOADED HOSTS TO SLEEP MODE THROUGH THE LIVE MIGRATION OF VMS. HOWEVER, DURING THE CONSOLIDATION, EACH VM MIGRATION CONSUMES ADDITIONAL RESOURCE, LEADING TO PERFORMANCE DEGRADATION AND SLA VIOLATION. TO ADDRESS THIS ISSUE, THIS STUDY PROPOSES A NOVEL ADAPTIVE PERFORMANCE-TO-POWER-RATIO (PPR)-AWARE DYNAMIC VM CONSOLIDATION FRAMEWORK BASED ON BOTH THE PREDICTED RESOURCE UTILIZATION AND PPR OF THE HETEROGENEOUS HOSTS TO RESOLVE THE TRADE-OFF OF PERFORMANCE AND ENERGY. THE PROPOSED FRAMEWORK CONSISTS OF FOUR STAGES: (1) HOST OVERLOAD DETECTION BASED ON RESIDUAL AVAILABLE COMPUTING CAPACITY; (2) SELECTION OF THE APPROPRIATE VMS FOR MIGRATION FROM THE OVERLOADED HOSTS BASED ON MINIMUM DATA TRANSFER; (3) HOST UNDERLOAD DETECTION BASED ON MULTI-CRITERIA Z-SCORE APPROACH; (4) ALLOCATING THE VMS SELECTED FOR MIGRATION FROM THE OVERLOADED AND UNDERLOADED HOSTS BASED ON THE MODIFIED POWER-AWARE BEST-FIT DECREASING ALGORITHM. TO VALIDATE THE RELIABILITY AND SCALABILITY OF THE PROPOSED METHOD, WE PERFORMED EXPERIMENTAL EVALUATION IN BOTH REAL AND SIMULATED ENVIRONMENTS. THE EXPERIMENTAL RESULTS DEMONSTRATE THAT THE PROPOSED APPROACH CAN REDUCE THE ENERGY CONSUMPTION EFFECTIVELY AND ENSURE MAXIMAL CONFORMITY TO THE QUALITY OF SERVICE (QOS) REQUIREMENTS ACROSS HETEROGENEOUS INFRASTRUCTURES, IN COMPARISON WITH THE EXISTING COMPETITIVE APPROACHES.

IN MOBILE EDGE COMPUTING SYSTEMS, THE EDGE SERVER PLACEMENT PROBLEM IS MAINLY TACKLED AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM AND SOLVED WITH MIXED INTEGER PROGRAMMING, HEURISTIC OR META-HEURISTIC ALGORITHMS, ETC. THESE METHODS, HOWEVER, HAVE PROFOUND DEFECT IMPLICATIONS SUCH AS POOR SCALABILITY, LOCAL OPTIMAL SOLUTIONS, AND PARAMETER TUNING DIFFICULTIES. TO OVERCOME THESE DEFECTS, WE PROPOSE A NOVEL EDGE SERVER PLACEMENT ALGORITHM BASED ON DEEP Q-NETWORK AND REINFORCEMENT LEARNING, DUBBED DQN-ESPA, WHICH CAN ACHIEVE OPTIMAL PLACEMENTS WITHOUT RELYING ON PREVIOUS PLACEMENT EXPERIENCE. IN DQN-ESPA, THE EDGE SERVER PLACEMENT PROBLEM IS MODELED AS A MARKOV DECISION PROCESS, WHICH IS FORMALIZED WITH THE STATE SPACE, ACTION SPACE AND REWARD FUNCTION, AND IT IS SUBSEQUENTLY SOLVED USING A REINFORCEMENT LEARNING ALGORITHM. EXPERIMENTAL RESULTS USING REAL DATASETS FROM SHANGHAI TELECOM SHOW THAT DQN-ESPA OUTPERFORMS STATE-OF-THE-ART ALGORITHMS SUCH AS SIMULATED ANNEALING PLACEMENT ALGORITHM (SAPA), TOP-K PLACEMENT ALGORITHM (TKPA), K-MEANS PLACEMENT ALGORITHM (KMPA), AND RANDOM PLACEMENT ALGORITHM (RPA). IN PARTICULAR, WITH A COMPREHENSIVE CONSIDERATION OF ACCESS DELAY AND WORKLOAD BALANCE, DQN-ESPA ACHIEVES UP TO 13.40% AND 15.54% BETTER PLACEMENT PERFORMANCE FOR 100 AND 300 EDGE SERVERS RESPECTIVELY.

THE SHORTEST COMMON SUPERSEQUENCE (SCS) PROBLEM IS A CLASSICAL NP-HARD PROBLEM, WHICH IS NORMALLY SOLVED BY HEURISTIC ALGORITHMS. ONE IMPORTANT HEURISTIC THAT IS INSPIRED BY THE PROCESS OF CHEMICAL REACTIONS IN NATURE IS THE CHEMICAL REACTION OPTIMIZATION (CRO) AND ITS ALGORITHM KNOWN AS CRO_SCS. IN THIS PAPER WE PROPOSE A NOVEL CRO ALGORITHM, DUBBED IMCRO, TO SOLVE THE SCS PROBLEM EFFICIENTLY. TWO NEW OPERATORS ARE INTRODUCED IN TWO OF THE FOUR REACTIONS OF THE CRO: A NEW CIRCULAR SHIFT OPERATOR IS ADDED TO THE DECOMPOSITION REACTION, AND A NEW TWO-STEP CROSSOVER OPERATOR IS INCLUDED IN THE INTER-MOLECULAR INEFFECTIVE COLLISION REACTION. EXPERIMENTAL RESULTS SHOW THAT IMCRO ACHIEVES BETTER PERFORMANCE ON RANDOM AND REAL SEQUENCES THAN WELL-KNOWN HEURISTIC ALGORITHMS SUCH AS THE ANT COLONY OPTIMIZATION, DEPOSITION AND REDUCTION, ENHANCED BEAM SEARCH, AND CRO_SCS. ADDITIONALLY, IT OUTPERFORMS ITS BASELINE CRO_SCS FOR DNA INSTANCES, AVERAGING A SCS LENGTH REDUCTION OF 1.02, WITH A MAXIMUM LENGTH REDUCTION OF UP TO 2.1.

AS A NON-DETERMINISTIC POLYNOMIAL HARD (NP-HARD) PROBLEM, THE SHORTEST COMMON SUPERSEQUENCE (SCS) PROBLEM IS NORMALLY SOLVED BY HEURISTIC OR METAHEURISTIC ALGORITHMS. ONE TYPE OF METAHEURISTIC ALGORITHMS THAT HAS RELATIVELY GOOD PERFORMANCE FOR SOLVING SCS PROBLEMS IS THE CHEMICAL REACTION OPTIMIZATION (CRO) ALGORITHM. SEVERAL CRO-BASED PROPOSALS EXIST; HOWEVER, THEY FACE SUCH PROBLEMS AS UNSTABLE MOLECULAR POPULATION QUALITY, UNEVEN DISTRIBUTION, AND LOCAL OPTIMUM (PREMATURE) SOLUTIONS. TO OVERCOME THESE PROBLEMS, WE PROPOSE A NEW APPROACH FOR THE SEARCH MECHANISM OF CRO-BASED ALGORITHMS. IT COMBINES THE OPPOSITION-BASED LEARNING (OBL) MECHANISM WITH THE PREVIOUSLY STUDIED IMPROVED CHEMICAL REACTION OPTIMIZATION (IMCRO) ALGORITHM. THIS UPGRADED VERSION IS DUBBED OBLIMCRO. IN ITS INITIALIZATION PHASE, THE OPPOSITE POPULATION IS CONSTRUCTED FROM A RANDOM POPULATION BASED ON OBL; THEN, THE INITIAL POPULATION IS GENERATED BY SELECTING MOLECULES WITH THE LOWEST POTENTIAL ENERGY FROM THE RANDOM AND OPPOSITE POPULATIONS. IN THE ITERATIVE PHASE, REACTION OPERATORS CREATE NEW MOLECULES, WHERE THE FINAL POPULATION UPDATE IS PERFORMED. EXPERIMENTS SHOW THAT THE AVERAGE RUNNING TIME OF OBLIMCRO IS MORE THAN 50% LESS THAN THE AVERAGE RUNNING TIME OF CRO_SCS AND ITS BASELINE ALGORITHM, IMCRO, FOR THE DESOXYRIBONUCLEIC ACID (DNA) AND PROTEIN DATASETS.

OPTICAL MUSIC RECOGNITION (OMR) IS AN IMPORTANT WAY TO DIGITIZE SCORE IMAGES AND HAS BROAD APPLICATION PROSPECTS IN FIELDS SUCH AS THE STORAGE OF MUSIC DOCUMENTS, MUSIC EDUCATION AND DIGITAL CREATION. AS A NEW PARADIGM FOR OBJECT DETECTION, DETR (DETECTION TRANSFORMER) HAS THE ABILITY TO ASSOCIATE CONTEXTUAL INFORMATION, WHICH CAN BE EXPLOITED TO RESOLVE THE OMR TASK. HOWEVER, THE ORIGINAL DETR DOES NOT FIT OMR WELL DUE TO ITS HIGH COMPUTATIONAL COMPLEXITY AND NUMEROUS PARAMETERS. TO ADDRESS THE DETR DEFECTS AND IMPROVE THE RECOGNITION ACCURACY OF OMR, WE PROPOSE A NOVEL MULTI-SCALE DETR (M-DETR) WITH A MULTI-SCALE FEATURE FUSION MECHANISM AND IMPROVED ATTENTION MECHANISMS. FIRST, A NEW MULTI-SCALE FEATURE FUSION MECHANISM IS DESIGNED TO LET THE BACKBONE NETWORK OF M-DETR GET RICH MULTI-SCALE INFORMATION. THEN, A KEY-REGION ATTENTION MECHANISM IS INCORPORATED BASED ON THE CHARACTER THAT THE KEY INFORMATION IS CONCENTRATED ON A SCORE IMAGE. FINALLY, THE PRE-CONTEXT ATTENTION MECHANISM IS INTRODUCED TO MAKE BETTER USE OF THE CONTEXTUAL ASSOCIATION BETWEEN RECOGNITION NOTES IN MUSIC SCORES. EXPERIMENT RESULTS SHOW THAT M-DETR ACHIEVES RECOGNITION ACCURACY OF 90.6% FOR 7 TYPICAL SMALL-SIZED NOTES, WHICH IS BETTER THAN FASTER R-CNN AND YOLO V5, AND THE IMPROVEMENT RATE IS 10.02% COMPARED TO THE ORIGINAL DETR ALGORITHM. THE RESULTS INDICATE THAT M-DETR IS AN EFFECTIVE WAY FOR THE OMR TASK, WHICH ALSO PROVIDES A NEW SOLUTION FOR THE DETECTION OF SMALL-SIZED OBJECTS WITH CONTEXTUAL ASSOCIATION.

WE CONSIDER THE PROBLEM OF MINING FOR FUNCTIONAL DEPENDENCIES IN RELATIONAL DATABASES. INTERMEDIATE DATA STRUCTURES, ALTHOUGH SIMPLE, EXPLODE IN SIZE AND A SOLUTION IS PROPOSED USING RADIX TREES TO REDUCE MEMORY UTILIZATION. PARALLELISM IS FURTHER APPLIED IN A MULTI-CORE COMPUTER TO FURTHER SPEEDUP THE PROCESS. BECAUSE BIT-PERMUTATIONS ARE THE BASIS OF THE CONSTRUCTION OF A BINARY INTERMEDIATE MATRIX, RADIX TREES REDUCE THE MEMORY USAGE 10 TIMES. MULTI-THREADING THE CONSTRUCTION AND PROCESSING OF THE INTERMEDIATE DATA LEADS TO A CONCURRENT COMPUTING AVERAGE-OVER-TIME OF 63 % ON AN EQUIVALENT SPEEDUP OF 6.3 ON A SYSTEM WITH 12 CORES, 256 GB OF MEMORY AND 1 TB SSD.

A THREAD SYNCHRONIZATION MECHANISM CALLED SPATIAL LOCKS FOR PARALLEL GEOMETRIC ALGORITHMS IS PRESENTED. WE DEMONSTRATE THAT SPATIAL LOCKS CAN ENSURE THREAD SYNCHRONIZATION ON GEOMETRIC ALGORITHMS THAT PERFORM CONCURRENT OPERATIONS OVER GEOMETRIC SURFACES AND SHAPES IN TWO-DIMENSIONAL OR THREE-DIMENSIONAL SPACE, CONSIDERING ALSO THAT THESE OPERATIONS FOLLOW A CERTAIN ORDER OF PROCESSING. A PARALLEL ALGORITHM FOR MESH SIMPLIFICATION WAS IMPLEMENTED USING SPATIAL LOCKS TO SHOW ITS USEFULNESS WHEN PARALLELIZING GEOMETRIC ALGORITHMS WITH EASE ON MULTI-CORE MACHINES. EXPERIMENTAL RESULTS ILLUSTRATE THE ADVANTAGE OF USING THIS SYNCHRONIZATION MECHANISM, WHERE SIGNIFICANT COMPUTATIONAL IMPROVEMENT CAN BE ACHIEVED.