Abstract We study a family of variants of Jensen’s subcomplete forcing axiom , upper S upper C upper F upper A comma $\mathsf {SCFA,}$ S C F A , and subproper forcing axiom , upper S u b upper P upper F upper A $\mathsf {SubPFA}$ S u b P F A . Using these, we develop a general technique for proving nonimplications of upper S upper C upper F upper A $\mathsf {SCFA}$ S C F A , upper S u b upper P upper F upper A $\mathsf {SubPFA}$ S u b P F A and their relatives and give several applications. For instance, we show that upper S upper C upper F upper A $\mathsf {SCFA}$ S C F A does not imply upper M upper A Superscript plus Baseline left parenthesis sigma $\mathsf {MA}^+(\sigma $ M A + ( σ -closed) and upper S u b upper P upper F upper A $\mathsf {SubPFA}$

Apriori-based rule generators, which are powered by the DIS-Apriori algorithm and the NIS-Apriori algorithm, are applied to analyze the data sets available in the IEEE BigData 2019 Cup: Suspicious Network Event Recognition. Then, each missing value in the test data set is decided by using the obtained rules. The advantage of our rule-based model is that the obtained rules are very easy to understand in comparison with other ”black-box” machine learning models. Furthermore, two algorithms preserve the logical property ”completeness,” so they generate rules without excess and deficiency. In evaluation, the AUC measure seems unfavorable to our model, so we employed 3-fold cross-validation for the training data set, and we obtained a 94% mean score. This result ensures the validity of our model. We report several meaningful results in this experiment, as well as the estimation of missing values.

We focus on a questionnaire consisting of three-choice question or multiple-choice question, and propose a privacy-preserving questionnaire by non-deterministic information. Each respondent usually answers one choice from the multiple choices, and each choice is stored as a tuple in a table data. The organizer of this questionnaire analyzes the table data set, and obtains rules and the tendency. If this table data set contains personal information, the organizer needs to employ the analytical procedures with the privacy-preserving functionality. In this paper, we propose a new framework that each respondent intentionally answers non-deterministic information instead of deterministic information. For example, he answers `either A, B, or C' instead of the actual choice A, and he intentionally dilutes his choice. This may be the similar concept on the k-anonymity. Non-deterministic information will be desirable for preserving each respondent's information. We follow the framework of Rough Non-deterministic Information Analysis (RNIA), and apply RNIA to the privacy-preserving questionnaire by non-deterministic information. In the current data mining algorithms, the tuples with non-deterministic information may be removed based on the data cleaning process. However, RNIA can handle such tuples as well as the tuples with deterministic information. By using RNIA, we can consider new types of privacy-preserving questionnaire.

We have been developing the getRNIA software tool for data mining under uncertain information. The getRNIA software tool is powered by the NIS-Apriori algorithm, which is a variation of the well-known Apriori algorithm. This paper considers the parallelization of the NIS-Apriori algorithm, and implements a part of this algorithm based on the Apache-Spark environment. We especially apply the implemented software to two data sets, the Mammographic data set and the Mushroom data set in order to show the property of the parallelization. Even though this parallelization was not so effective for the Mammographic data set, it was much more effective for the Mushroom data set.

This paper discusses issues related to incomplete information databases and considers a logical framework for rule generation. In our approach, a rule is an implication satisfying specified constraints. The term incomplete information databases covers many types of inexact data, such as non-deterministic information, data with missing values, incomplete information or interval valued data. In the paper, we start by defining certain and possible rules based on non-deterministic information. We use their mathematical properties to solve computational problems related to rule generation. Then, we reconsider the NIS-Apriori algorithm which generates a given implication if and only if it is either a certain rule or a possible rule satisfying the constraints. In this sense, NIS-Apriori is logically sound and complete. In this paper, we pay a special attention to soundness and completeness of the considered algorithmic framework, which is not necessarily obvious when switching from exact to inexact data sets. Moreover, we analyze different types of non-deterministic information corresponding to different types of the underlying attributes, i.e., value sets for qualitative attributes and intervals for quantitative attributes, and we discuss various approaches to construction of descriptors related to particular attributes within the rules' premises. An improved implementation of NIS-Apriori and some demonstrations of an experimental application of our approach to data sets taken from the UCI machine learning repository are also presented. Last but not least, we show simplified proofs of some of our theoretical results.

In Perception-Based Computing (PBC), we face several problems, and the management of incomplete information and inexact data is an important issue to address. We have proposed a framework Rough Non-deterministic Information Analysis (RNIA) for handling tables with non-deterministic information as a kind of incomplete information. Under this framework, we coped with several rough sets-based concepts, and extended the Apriori algorithm to tables with non-deterministic information. We named this algorithm NIS -Apriori. This paper reports the overview of RNIA, NIS -Apriori and our new software getRNIA. This getRNIA gives us to generate rules through the web browser easily.

We have been proposing a framework rough Nondeterministic information analysis, which considers granular computing concepts in tables with incomplete and nondeterministic information, as well as rule generation. We have recently defined an expression named division chart with respect to an implication and a subset of objects. Each division chart takes the role of the minimum granule for rule generation, and it takes the role of contingency table in statistics. In this paper, we at first define a division chart in deterministic information systems (DISs) and clarify the relation between a division chart and a corresponding implication. We also consider a merging algorithm for two division charts and extend the relation in DISs to nondeterministic information systems. The relation gives us the foundations of rule generation in tables with nondeterministic information.

This paper presents an approach to pedestrian recognition using in-vehicle Lidar. In automobile applications for reducing pedestrian-involved accident, ADAS sensors are required to provide high detection performance with environmental robustness, applicable to variety of pedestrians under the variety of driving condition. DENSO has developed the high resolution in-vehicle Lidar installing inside the cabin for higher environmental robustness, and pedestrian recognition algorithm with high tracking ability, adaptable to traffic-congestion condition seen in urban environments. Several experiments show these developments have high potential for achieving effective pedestrian safety system.

Rough sets are applied to data tables containing missing values. A new method, called a method of possible equivalence classes, is proposed. Discernibility as well as indiscernibility of missing values is considered in order to improve previous results. A family of possible equivalence classes is obtained, in which each possible equivalence class has the possibility that it is an actual one. By using the family of possible equivalence classes, we derive lower and upper approximations. The lower and the upper approximations coincide with ones obtained from methods of possible worlds.

Abstract We study the relationship between the semistationary reflection principle and stationary reflection principles. We show that for all regular cardinals λ ≥ ω2 the semistationary reflection principle in the space [λ]ω implies that every stationary subset of ≔ {α ∈ λ ∣ cf(α) = ω} reflects. We also show that for all cardinals λ ≥ ω3 the semistationary reflection principle in [λ]ω does not imply the stationary reflection principle in [λ]ω.

Rough sets are applied to data tables containing possibilistic information. A family of weighted equivalence classes is obtained, in which each equivalence class is accompanied by a possibilistic degree to which it is an actual one. By using the family of weighted equivalence classes we can derive a lower approximation and an upper approximation. The lower approximation and the upper approximation coincide with those obtained from methods of possible worlds. Therefore, the method of weighted equivalence classes is justified.

Our context-aware information delivery system enables information to be provided directly to the standby screen of a user’s mobile phone. The information appears on the standby screen only while the user context matches the information context due to a function that continuously monitors the behavioral response to user context, time, location, and reference history. We conducted a four-month trial of local information provision with over 800 mobile phone users participating. Approximately 30% of them.. "positively accepted" this information provision and most users actually utilized the received information. This shows that our approach is effective for mobile ad delivery.

This paper discusses an autonomous towed vehicle for underwater inspection in a port area, in which a sea current is so fast and complex. The autonomous towed vehicle has three different navigation modes; towed mode, autonomous mode and kite mode, to assure safe and reliable inspection in such a port area. An autonomous underwater vehicle (AUV) is employed as a towed vehicle to increase autonomy. In towed systems, it is important to monitor a towed force for preventing cable breaks and making use of it more efficiently since a towing cable plays an important role in transmitting electric signals and towing forces. In this paper, we propose a sequence for towed force estimation consisting of different navigation modes and control methods for it. In addition, the navigation control system of the autonomous towed vehicle is briefly described. Simulations to verify the towed force estimation and control methods are carried out and the results are discussed.

We are now touching a problem how we add soft computing aspects to logic programming and we have been discussing null attribute values on logic programs. Here, we introduce two functors setu and sets into logic programs for describing indefinite attribute values explicitly. Every logic program with setu or sets has variability and tolerance in itself, namely this program expresses a set of possible definite logic programs. We call this program a variational logic program. In this paper, we show the variational logic programs and a theorem prover for them.

Bus-based shared-memory multi-processors (SM-MP) have successfully been used commercially, since implementation requires no drastic changes to the programming paradigm. In this paper we propose the memory structure called SSM-MP (Scalable shared-memory multi-processors), aimed to shorten the cache refill latency and to relax the bus bottle neck problem. In this machine, main memory consists of local memories dedicated to each of the processors and something called MTag. MTag is a small piece of hardware that filters out bus traffic headed to the system bus and maintains cache coherency. A popular UNIX (SVR4 ES/MP) was ported. Original OS code works well due to its natural locality. Furthermore, by allocating tasks to the local memory, we were able to reduce the system bus traffic to nearly a quarter. SSM-MP is an effective approach in building a multi-processor system with a medium number (4-32) of processors.

A description is given of parallel control techniques and performance evaluations by simulation for the knowledge-base machine (KBM) using the multiport page-memory (MPPM) and the unification engine (UE). Relational knowledge base retrieval requires repeated application of the unification-join (U-join) operations on term relations. It also requires each U-join to be dynamically scheduled, observing the result of the previously executed U-join. Control techniques to execute the query process of the relational knowledge base effectively in the KBM using the MPPM and the UE have been investigated. For parallel execution of coarse-grain operations, each operation is decomposed to concurrently executable fine-grain operations by partitioning input data sets. A number of features that depend on processor-allocation strategies have been identified. In particular, it was found that query processing resulting in repetitions of the retrieval operation, by a certain control strategy, increases the amount of the total data transfer to the highly parallel UEs and lowers processing efficiency.>

A relational database machine Delta [2] has been developed at the Institute for New Generation Computer Technology (IOOT).The main component of Delta is an RDBE (relational database engine)[3] which performs relational database operations, such as projection and join.The RDBE consists of a sorting process hardware module called the sorter, a merging process hardware module called the merger, other special hardware modules called the IN module, a data input adapter, a data output adapter, and a CPU controlling these hardware modules.A special sorting cell was developed and implemented on LSI.The sorting process is based on a two-way merge-sort algorithm [l].~ne merging process is based on a hardware pipeline algorithm.In this paper, we describe the design considerations of the RDBE and these algorithms.Then, we describe the detailed design, implementation, and a performance evaluation of the RDBE.