Einstein Würfelt Nicht! (abbr. EWN) is a relatively recent board game that holds considerable significance in the field of computer games. It has been included in the official game collection of the ICGA Computer Olympiad for several years. Numerous computer programs have been developed to play this game, and solving it remains a prominent objective for researchers. In this research, we developed a lossy compression method to construct a database for EWN that closely approximates the exact win rate while requiring about 1,821 times smaller space. Using this solution, we created a near-perfect computer program that is practically close to exactly solving EWN. It consistently secures first place in multiple computer tournaments, including TCGA and ICGA.

Alpha-beta-based search is used in the game of Chinese dark chess, which is stochastic and has perfect information. To efficiently determine a good move, an evaluation function needs to be carefully designed. This paper aims to enhance the evaluation function for material combinations with seven or more pieces by extracting information from 6-piece endgame databases. Firstly, we utilize the existing endgames to evaluate a win/loss ratio of each material combination, establishing a benchmark for endgame scores. We then design the values of each material combination, called the MCV, based on the ratios. Secondly, we extend the MCVs to encompass a larger number of pieces and integrate it into the hybrid evaluation function used by the game-playing program. The experimental results demonstrate that the original evaluation function has been improved, leading to an overall enhancement in playing strength.

Random forest has been widely used to classifying objects recently because of its efficiency and accuracy. On the other hand, nonvolatile memory has been regarded as a promising candidate to be a part of a hybrid memory architecture. For achieving the higher accuracy, random forest tends to construct lots of decision trees, and then conducts some post-pruning methods to fell low contribution trees for increasing the model accuracy and space utilization. However, the cost of writing operations is always very high on non-volatile memory. Therefore, writing the to-be-pruned trees into non-volatile memory will significantly waste both energy and time. This work proposed a framework to ease such hurt of training a random forest model. The main spirit of this work is to evaluate the importance of trees before constructing it, and then adopts different writing modes to write the trees to the non-volatile memory space. The experimental results show the proposed framework can significantly mitigate the waste of energy with high accuracy.

The semi-streaming model is a variant of the streaming model frequently used for the computation of graph problems. It allows the edges of an n-node input graph to be read sequentially in p passes using Õ(n) space. If the list of edges includes deletions, then the model is called the turnstile model; otherwise it is called the insertion-only model. In both models, some graph problems, such as spanning trees, k-connectivity, densest subgraph, degeneracy, cut-sparsifier, and (Δ+1)-coloring, can be exactly solved or (1+ε)-approximated in a single pass; while other graph problems, such as triangle detection and unweighted all-pairs shortest paths, are known to require Ω̃(n) passes to compute. For many fundamental graph problems, the tractability in these models is open. In this paper, we study the tractability of computing some standard spanning trees, including BFS, DFS, and maximum-leaf spanning trees. Our results, in both the insertion-only and the turnstile models, are as follows. - Maximum-Leaf Spanning Trees: This problem is known to be APX-complete with inapproximability constant ρ ∈ [245/244, 2). By constructing an ε-MLST sparsifier, we show that for every constant ε > 0, MLST can be approximated in a single pass to within a factor of 1+ε w.h.p. (albeit in super-polynomial time for ε ≤ ρ-1 assuming P ≠ NP) and can be approximated in polynomial time in a single pass to within a factor of ρ_n+ε w.h.p., where ρ_n is the supremum constant that MLST cannot be approximated to within using polynomial time and Õ(n) space. In the insertion-only model, these algorithms can be deterministic. - BFS Trees: It is known that BFS trees require ω(1) passes to compute, but the naïve approach needs O(n) passes. We devise a new randomized algorithm that reduces the pass complexity to O(√n), and it offers a smooth tradeoff between pass complexity and space usage. This gives a polynomial separation between single-source and all-pairs shortest paths for unweighted graphs. - DFS Trees: It is unknown whether DFS trees require more than one pass. The current best algorithm by Khan and Mehta [STACS 2019] takes Õ(h) passes, where h is the height of computed DFS trees. Note that h can be as large as Ω(m/n) for n-node m-edge graphs. Our contribution is twofold. First, we provide a simple alternative proof of this result, via a new connection to sparse certificates for k-node-connectivity. Second, we present a randomized algorithm that reduces the pass complexity to O(√n), and it also offers a smooth tradeoff between pass complexity and space usage.

Endgame databases can be difficult to use in tree search when database sizes remain large even after compression. Given the same endgame, we discover that the compression ratios vary significantly when using different encoding schemes. The intuition is that when a set of positions mapped into a continuous chunk of segments have similar values, block-based compression libraries such as zlib can yield a better compression ratio than cases where segments contain diversified values. However, finding the optimal encoding scheme by exhaustive enumeration is time-infeasible for endgame databases with a large number of pieces. We propose a novel approach using deep learning to obtain an encoding scheme so that the compression ratio is suitable for practical purposes. Our approach can be applied to chess-like games.

As the volume of data stored by Big data and Cloud services continues to grow, both academia and industry are seeking for high-performance storage systems. Recently, with the recent advances in write-optimized indexes (WOI), WOI-based file systems can now outperform conventional file systems with orders of magnitude on random writes, metadata updates, and small file creation. Based on the B-tree structure, WOI-based file systems can not only process data faster than the conventional B-tree but also improve the range query performance. However, the write amplification of these WOI-based file systems becomes a serious performance overhead when adopting flash storage as underlying storage devices due to the recursive entry update behavior. To mitigate the write amplification problem of WOIbased file systems, we propose a flash-friendly WOI design to reduce the number of write requests on flash storage. To evaluate the performance of the proposed design, we adapt B+-tree as a case study and the experimental results are promising.

In our previous work, a simulation framework was proposed to focus on imitating the behavior of wireless charging vehicles (WCVs) and wireless sensor networks (WSNs) because current mainstream simulators have very limited support on simulation of WCVs. The WCV is an integration of a mobile vehicle and a wireless power transfer broadcaster, which is used to recharge sensors wirelessly to prolong the lifetime of sensor networks. In the study of WCVs, simulators are extensively used to study the routing algorithms and behaviors of WCVs because it is very costly to build a WSN testbed and many specifications are not standardized. However, mainstream WSN simulators require researchers to develop and integrate their WCV modules. Therefore, the previously proposed framework aims to provide a simple framework for simulating of wireless power transfer and mobile vehicles. In this study, to further strength the usability of the proposed simulation framework, a graphic user interface is introduced to allow users to assign sensors' location and specify simulation parameters.

To accommodate the sheer volume of data in the era of Big Data and Cloud Computing, both new storage medium technologies and high-performance file systems are proposed. For storage medium, Shingled Magnetic Recording (SMR) increases the areal density by overlapping adjacent tracks so as to provide larger storage capacity. On the other hand, write-optimized indexes (WOI) file systems are also studied and can now outperform conventional file systems with orders of magnitude. However, the main drawback of SMR is the random-write restriction because random-write operations will cause the extra overhead of rewriting data stored in overlapped tracks. The rewriting overhead is amplified by the recursive entry update behavior of WOI-based file systems. Therefore, the rewriting issue becomes a serious performance overhead when adopting SMR drives as underlying storage devices for WOI-based file systems. To mitigate the write amplification problem when deploying WOI-based file systems on SMR disks, this paper proposes the update-overhead-aware caching policy to reduce the update overhead with the help of flash-based storage devices. To evaluate the performance of the proposed design, the B+-tree as a case study. The experimental results are promising.

Agent-based stochastic simulation is an established approach to study infectious diseases. Its advantage is the flexibility to incorporate important concepts. The effect of various mitigation strategies has been demonstrated using simulation models. Most of the previous studies compared a few options with a few selected scenarios. We propose to use genetic algorithms to search for the best vaccination strategy for a given scenario with the simulation program as fitness scorer. Vaccination efficacy varies significantly. Therefore, the real challenge is to find a good strategy without the knowledge of it. The simulation software is efficient, yet still takes three minutes to complete a simulation run with Taiwan population. We use surrogate to speed up the search about 1000 times. The surrogate has the average of the absolute value of error around 0.284 percent and the rank correlation coefficient is greater than 0.98 for all the scenarios except one. The optimal solution with surrogate has fitness value very close to use simulations. The difference is generally less than one percent. We envision that an autonomous software searches through the huge scenario space with the help of surrogate function and adaptively executes simulation program to revise the surrogate function to produce higher fidelity surrogate and better search results.

Summary The concept of green storage in cluster computing has recently attracted enormous interest among researchers. Consequently, several energy‐efficient solutions, such as multi‐speed disks and disk spin down methods, have been proposed to conserve power in storage systems and improve disk access. Some researchers have assessed their proposed solutions via simulations, while others have used real‐world experiments. Both methods have advantages and disadvantages. Simulations can more swiftly assess the benefits of energy‐efficient solutions, but various measurement errors can arise from procedural shortcomings. For instance, many power simulation tools fail to consider how heat increases the power overhead of disk operations. Some researchers claim that their modeling methods reduce the measurement error to 5% in the single disk model. However, the demand for large‐scale storage systems is growing rapidly. Traditional power measurement using a single disk model is unsuited to such systems because of their complex storage architecture and the unpredictability of numerous disks. Consequently, a number of studies have conducted real machine experiments to assess the performance of their solutions in terms of power conservation, but such experiments are time consuming. To address this problem, this study proposes an efficient simulation tool called Benchmark Analysis Software for Energy‐efficient Solution (BASE), which can accurately estimate disks' power consumption in large‐scale storage systems. We evaluate the performance of BASE on real‐world traces of Academia Sinica (Taiwan) and Florida International University. BASE incorporates an analytical method for assessing the reliability of energy‐efficient solutions. The analytical results demonstrate that the measurement error of BASE is 2.5% lower than that achieved in real‐world experiments involving energy‐estimation experiments. Moreover, the results of simulations to assess solution reliability are identical to those obtained through real‐world experiments. Copyright © 2015 Copyright © 2015 John Wiley & Sons, Ltd.

Recently, many applications and users rely on cloud storage services, such as Google drive, Dropbox, iCloud and Sky drive, to store private files and system data, and cloud storage services must thus be reliable and secure. To increase reliability, previous studies have proposed a variety of erasure coding algorithms for data fault tolerance for use in storage systems. Although these data fault tolerance mechanisms increase data reliability, implementation also increases storage system costs and energy consumption due to data redundancy. However, to date energy-efficient schemes have only been developed based on a RAID architecture, and none have been implemented using an erasure coding algorithm. To address this issue, this study proposes an energy-aware I/O framework with a quality-of-service (QoS) aware data reconstruction scheduler for erasure coding algorithms, called the EEC-scheme. This approach reduces storage system energy consumption and decreases response times for user requests when the system restores failed disks. A series of experiments show that the proposed scheme can significantly reduce power consumption in storage systems.

The household structure is an important aspect of population based simulation. How to generate a mock population with specific household structure characteristics is thus an important question. The network structure is one of the dominant factors for contact-based disease transmission. And household structure is the most important source of close contact among small groups. We identify the percentage of elderly-children households as an important character and study the process to generate mock population with specified percentage of elderly-children households. The generated mock populations are fed into the agent-based simulation module to study the impact of household structure on vaccination policy.

The lifetime of wireless sensor networks are constrained by its limited battery capacity. Therefore, the lifetime is widely regard as a bottleneck of technique of wireless sensor network. Recently, the emerging breakthrough in wireless power transfer technique is expected to eliminate the power constraint bottleneck. In this paper, we propose an intelligent wireless charging vehicle (IWCV) strategy to resolve above problem in a dynamic and scalable approach. The IWCV strategy includes an intelligent routing strategy to traverse the sensor network topology and charging their battery to prolong their lifetime. What makes IWCV different to previous studies is that IWCV can still work even if the topology changes by re-computing the traversing route and stop time for each node in a relative short amount of time, compared with the time needed to find the shortest Hamiltoaian cycle. With the scalable and dynamic feature of IWCV, one can change their sensor network topology without down time to reconfigure the wireless charging vehicle while still maintain low energy consumption during traveling and charging in dynamic network topologies.

Summary A distributed environment requires a monitoring system to oversee the operation of various distributed nodes. A monitoring service is crucial because it ensures a high‐quality computing environment and a reliable service. The interface and framework determine the effectiveness of a monitoring system. This paper uses the concept of user‐adaptive visualization to design its interface and proposes a flexible modular framework. Designers can use the proposed modular framework to flexibly extend existing modules, design visual interfaces to satisfy user requirements, and improve system failover schemes. The implementation of such a monitoring system for monitoring data preservation nodes is also provided. The system including fault‐tolerance and notification functions supports full monitoring services for Storage Resource Broker (SRB) or integrated Rule‐Oriented Data System (iRODS) based systems. The experimental results show that the proposed framework is suitable for data preservation services and is robust and responsive when faced with system failures. Copyright © 2014 John Wiley & Sons, Ltd.

Chinese Dark Chess, a nondeterministic two-player game, has not been studied thoroughly. State-of-the-art programs focus on using search algorithms to explore the probability behavior of flipping unrevealed pieces in the opening and the midgame phases. There has been comparatively little research on opening books and endgame databases, especially endgames with nondeterministic flips. In this paper, we propose an equivalence relation that classifies the complex piece relations between the material combinations of each player, and derive a partition for all such material combinations. The technique can be applied to endgame database compression to reduce the number of endgames that need to be constructed. As a result, the computation time and the size of endgame databases can be reduced substantially. Furthermore, understanding the piece relations facilitates the development of a well-designed evaluation function and enhances the search efficiency. In Chinese Dark Chess, the number of nontrivial material combinations comprised of only revealed pieces is 8 497 176, and the number that contain at least one unrevealed piece is 239 980 775 397. Under the proposed method, the compression rates of the above material combinations reach 28.93% and 42.52%, respectively; if the method is applied to endgames comprised of three to eight pieces, the compression rates reach 5.82% and 5.98%, respectively.

Previous studies proposed energy-efficient solutions, such as multispeed disks and disk spin-down methods, to conserve power in their respective storage systems. However, in most cases, the authors did not analyze the reliability of their solutions. According to research conducted by Google and the IDEMA standard, frequently setting the disk status to standby mode will increase the disk’s Annual Failure Rate and reduce its lifespan. To resolve the issue, we propose an evaluation function called E 3 SaRC (Economic Evaluation of Energy Saving with Reliability Constraint), which considers the cost of hardware failure when applying energy-saving schemes. We also present an adaptive write cache mechanism called CacheRAID. The mechanism tries to mitigate the random access problems that implicitly exist in RAID techniques and thereby reduce the energy consumption of RAID disks. CacheRAID also addresses the issue of system reliability by applying a control mechanism to the spin-down algorithm. Our experimental results show that the CacheRAID storage system can reduce the power consumption of the conventional software RAID 5 system by 65% to 80%. Moreover, according to the E 3 SaRC measurement, the overall saved cost of CacheRAID is the largest among the systems that we compared.

Improving the disk I/O performance is always a critical research issue in cloud computing platforms, especially for cloud computing platforms with distributed data-intensive workloads. To resolve the problem of poor disk I/O, in this paper, we propose a novel scheduling framework in the Xen-based hypervisor. In our framework, we employ a locality-aware scheduler with a deadline constraint and provide a basic QoS controller to guarantee the throughput of the cloud platform. The experimental results show that our solution increases the IOPS of Xen-based cloud platform by 9%-13% compared to the solution of early-deadline-first (EDF), deadline scheduling algorithm or Flubber. Moreover, according to the schedulability test, our solution can improve the miss deadline ratio significantly, compared to the EDF scheduling algorithm and Flubber.

The concept of green storage in cluster computing has generated a great deal of interest among researchers in recent years. As a result, several energy-efficient solutions, such as multi-speed disks and disk spin down methods, have been proposed to conserve power in storage systems and improve disk access. Some researchers evaluate their solutions via simulations, while others utilize real-world experiments. Both methods have advantages and disadvantages. To address the problem, we propose an efficient simulation tool called BASE, which can accurately estimate the power consumption of disks in large-scale storage systems. We evaluate the performance of BASE on real-world traces from Academia Sinica (Taiwan) and Florida International University. BASE incorporates an analytical method for evaluating the reliability of energy-efficient solutions. Our analysis results show that the measurement error of BASE is 2.5% lower than that achieved in real-world experiments on energy estimation. Moreover, the results of simulations performed to evaluate a solution's reliability are the same as those derived by real-world experiments.

Virtualization is a critical technology issue in cloud computing. Virtualization technology faces two major challenges, namely, poor I/O throughput and long latency for data access. To address the issues, we present a distributed deadline modification SCAN mechanism called DDM-SCAN. We integrate the DDM-SCAN mechanism to Xen-based cloud platform. The result of our simulations show DDM-SCAN improve the I/O response time of Xen-based cloud platform 20% comparing to Flubber [1] mechanism and it also guarantee same or better quality of service (QoS) as compared with Flubber mechanism in Xen-based hypervisor. In advance, this paper provides more easy method to implement DDM-SCAN on Xen-based hypervisor rather than Flubber mechanism.

A large variety of data mining and machine learning techniques are applied to a wide range of applications today. There- fore, there is a real need to develop technologies that allows data analysis while preserving the confidentiality of the data. Secure multi-party computation (SMC) protocols allows participants to cooperate on various computations while retaining the privacy of their own input data, which is an ideal solution to this issue. Although there is a number of frameworks developed in SMC to meet this challenge, but they are either tailored to perform only on specific tasks or provide very limited precision. In this paper, we have developed protocols for floating point arithmetic based on secure scalar product protocols, which is re- quired in many real world applications. Our protocols follow most of the IEEE-754 standard, supporting the four fundamental arithmetic operations, namely addition, subtraction, multiplication, and division. We will demonstrate the practicality of these protocols through performing various statistical calculations that is widely used in most data analysis tasks. Our experiments show the performance of our framework is both practical and promising.

Collaborative recommender systems use various types of information to help customers find products of personalized interest. To increase the usefulness of collaborative recommender systems in certain circumstances, it could be desirable to merge recommender system databases between companies, thus expanding the data pool. This can lead to privacy disclosure hazards during the merging process. This paper addresses how to avoid privacy disclosure in collaborative recommender systems by comparing with major cryptology approaches and constructing a more efficient privacy-preserving collaborative recommender system based on the scalar product protocol.

There is a fair amount of research about privacy, but few empirical studies about its cost have been conducted. In the area of secure multiparty computation, the scalar product has long been reckoned as one of the most promising alternatives to classic logic gates. The reason for this is that the scalar product is not only complete, which is as good as logic gates, but also much more efficient than logic gates. As a result, we set out to study the computation and communication resources needed for some of the most well-known and frequently referenced secure scalar product protocols, including the composite residuosity, the invertible matrix, the polynomial sharing, and the commodity-based approaches. In addition to the implementation details of these approaches, we analyze and compare their execution time, computation time, and memory and random number consumption. Moreover, Fairplay, the benchmark approach that implements Yao's circuit evaluation protocol, is also included in our experiments in order to demonstrate the potential for the scalar products to replace logic gates.

Social network analysis is an important methodology in sociological research. Though social network data is very useful to researchers and policy makers, releasing such data to the public may cause an invasion of privacy. We generalize the techniques for protecting personal privacy in tabulated data, and propose some metrics of anonymity for assessing the risk of breaching confidentiality by disclosing social network data. We assume a situation of data publication, where data is released to the general public. We adopt description logic as the underlying knowledge representation formalism, and consider the metrics of anonymity in open world and closed world contexts respectively.

A graph is componentwise biconnected if every connected component either is an isolated vertex or is biconnected. We present a linear-time algorithm for the problem of adding the smallest number of edges to make a bipartite graph componentwise biconnected while preserving its bipartiteness. This algorithm has immediate applications for protecting sensitive information in statistical tables.

A query on the distribution of a sensitive field within a selected population in a database can be submitted to the data center, and the answer to this query can leak private information, even though no identification information is provided. Inspired by decision theory, we present a quantitative model of the privacy protection problem in such a database query environment. In our model, the user information states are defined as classes of probability distributions on the set of possible confidential values. These states can be modified and refined by knowledge acquisition actions. The data confidentiality is guaranteed by ensuring that misusing private information is more costly than any possible gain. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper, we sketch common properties of a class of so-called subgraph optimization problems that can be systematically solved on distance-hereditary graphs. Based on the found properties, we then develop a general problem-solving paradigm that solves these problems efficiently in parallel. As a by-product, we also obtain new linear-time algorithms by a sequential simulation of our parallel algorithms. Let T d |V|,|E|) and P d (|V|,|E|) denote the time and processor complexities, respectively, required to construct a decomposition tree of a distance-hereditary graph G=(V,E) on a PRAM model M d . Based on the proposed paradigm, we show that the maximum independent set problem, the maximum clique problem, the vertex connectivity problem, the domination problem, and the independent domination problem can be sequentially solved in O(|V|+|E|) time, and solved in parallel in O(T d (|V|,|E|)+log |V|) time using O(P d (|V|,|E|)+|V|log |V|)processors on M d . By constructing a decomposition tree under a CREW PRAM, we also show that T d (|V|,|E|)=O(log 2 |V|) and P d (|V|,|E|)=O(|V|+|E|).

This paper studies the scheduling of tasks on a pool of identical workstations in a network where message passing is used for data transfer and communication between processors and where the precedence relations among tasks form a send-receive graph. Bur parallel computation model differs from previous models by including all of the following practical considerations: 1) the sending and receiving of multiple messages from one processor to another is performed sequentially, 2) communication overhead is proportional to the message size, and 3) the starting and ending task must be performed on the same machine. These factors are crucial when performing parallel task execution using a pool of workstations whose communication primitives are provided by off-the-shelf packages, such as PVM, and whose message sizes are nontrivial. Although our model is new, using reduction from other well-known scheduling results shows that finding a scheduling with the optimal makespan is NP-hard. Our focus, therefore, is on developing and analyzing approximation algorithms for this problem. When the number of workstations in the network is abundant, a linear approximation algorithm is given with a proven performance bound of two times the optimal. When the number of available workstations is a fixed constant k that is greater than 2, we show an O(nlogn)-time approximation algorithm which always performs better than (3+k-2/k) times the optimal. Simulation results show that, on the average, both of our approximation algorithms perform much better than the worst case analysis and both generate schedulings whose makespans are very close to optimal.

We present efficient parallel algorithms for finding a minimum weighted connected dominating set, a minimum weighted Steiner tree for a distance-hereditary graph which take O(log n) time using O(n+m) processors on a CRCW PRAM, where n and m are the number of vertices and edges of a given graph, respectively. We also find a maximum weighted clique of a distance-hereditary graph in O(log/sup 2/ n) time using O(n+m) processors on a CREW PRAM.

Studies statistical database problems for 2D tables whose regular cells, row sums, column sums and table sums may be suppressed. Using graph-theoretical techniques, we give optimal or efficient algorithms for the query system problem, the adversary problem and the minimum complementary suppression problem. These three problems are considered for a variety of data security requirements such as those of protecting linear invariants, analytic invariants, k rows (or columns) as a whole, and a table as a whole.

The problem of finding a minimum number of edges whose addition biconnects an undirected graph is considered. This problem has been studied by several other researchers, two of whom presented a linear-time algorithm for this problem in an earlier volume of this journal. However, that algorithm contains an error that is exposed in this paper. A corrected linear-time algorithm for this problem, as well as a new efficient parallel algorithm, are presented. The parallel algorithm runs in $O(\log ^2 n)$ time with a linear number of processors on an EREW PRAM, where n is the number of vertices in the input graph.

The author considers the problem of finding a smallest set of edges whose addition four-connects a triconnected graph. This is a fundamental graph-theoretic problem that has applications in designing reliable networks. He presents an O(n alpha (m,n)+m) time sequential algorithm for four-connecting an undirected graph G that is triconnected by adding the smallest number of edges, where n and m are the number of vertices and edges in G, respectively, and alpha (m, n) is the inverse Ackermann function. He presents a new lower bound for the number of edges needed to four-connect a triconnected graph. The form of this lower bound is different from the form of the lower bound known for biconnectivity augmentation and triconnectivity augmentation. The new lower bound applies for arbitrary k, and gives a tighter lower bound than the one known earlier for the number of edges needed to k-connect a (k-1)-connect graph. For k=4, he shows that this lower bound is tight by giving an efficient algorithm for finding a set edges with the required size whose addition four-connects a triconnected graph.>

The problem of finding the smallest set of edges whose addition triconnects an undirected graph is considered. This is a fundamental graph-theoretic problem that has applications in designing reliable networks and fault-tolerant computing. A linear time sequential algorithm is given for the problem. This is a substantial improvement over the best previous algorithm for this problem, which runs in O(n(n+m)/sup 2/) time on a graph with n vertices and m edges.>