Logical Distance Research Articles

Linear-time logics -- a coalgebraic perspective

We describe a general approach to deriving linear-time logics for a wide variety of state-based, quantitative systems, by modelling the latter as coalgebras whose type incorporates both branching and linear behaviour. Concretely, we define logics whose syntax is determined by the type of linear behaviour, and whose domain of truth values is determined by the type of branching behaviour, and we provide two semantics for them: a step-wise semantics akin to that of standard coalgebraic logics, and a path-based semantics akin to that of standard linear-time logics. The former semantics is useful for model checking, whereas the latter is the more natural semantics, as it measures the extent with which qualitative properties hold along computation paths from a given state. Our main result is the equivalence of the two semantics. We also provide a semantic characterisation of a notion of logical distance induced by these logics. Instances of our logics support reasoning about the possibility, likelihood or minimal cost of exhibiting a given linear-time property.

Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion.

Understanding social interactions in livestock groups could improve management practices, but this can be difficult and time-consuming using traditional methods of live observations and video recordings. Sensor technologies and machine learning techniques could provide insight not previously possible. In this study, based on the animals’ location information acquired by a new cooperative wireless localisation system, unsupervised machine learning approaches were performed to identify the social structure of a small group of cattle yearlings () and the social behaviour of an individual. The paper first defined the affinity between an animal pair based on the ranks of their distance. Unsupervised clustering algorithms were then performed, including K-means clustering and agglomerative hierarchical clustering. In particular, K-means clustering was applied based on logical and physical distance. By comparing the clustering result based on logical distance and physical distance, the leader animals and the influence of an individual in a herd of cattle were identified, which provides valuable information for studying the behaviour of animal herds. Improvements in device robustness and replication of this work would confirm the practical application of this technology and analysis methodologies.

Pragmatic Distance in IoT Devices

Stand-alone devices perform their functions without human intervention. These types of devices would benefit from capabilities that allow collaboration among the devices in order to maximize service availability, as well as dynamic adaptation of the devices to user requirements in order to minimize resource usage. For example, when detecting a malfunction in one of the devices, the system should find devices with similar functionality to recover the overall service by functionality substitution. Additionally, for example, in the context of a user with hearing loss, the system should utilize devices with visual feedback functionality and disable audio feedback functionality to conserve resources. In this paper, we describe the implementation of a method to detect similar functionalities between heterogeneous devices, denoted Lightweight Machine to Machine Resource Pragmatic Distance (LwRPD). We define that devices are pragmatically close if they can be substituted with each other in order to perform a specific function in a particular context. We implement the method for enabling the aforementioned capabilities in specific use cases, using resources from real-life devices, to demonstrate the benefits. We also compare LwRPD with a multi-agent pragmatic similarity metric, Fuzzy logic and Levenshtein distance techniques, demonstrating LwRPD superiority over those techniques.

Multipoint channel charting-based radio resource management for V2V communications

We consider a multipoint channel charting (MPCC) algorithm for radio resource management (RRM) in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems. A massive MIMO (mMIMO) infrastructure network performs logical localization of vehicles to a MPCC, based on V2I communication signals. Combining logical distances given by channel charting with V2V measurements, the network trains a function to predict the quality of a direct V2V communication link from observed V2I communication signals. In MPCC, the network uses machine learning techniques to learn a logical radio map from V2I channel state information (CSI) samples transmitted from unknown locations. The network extracts CSI features, constructs a dissimilarity matrix between CSI samples, and performs dimensional reduction of the CSI feature space. Here, we use Laplacian Eigenmaps (LE) for dimensional reduction. The resulting MPCC is a two-dimensional map where the spatial distance between a pair of vehicles is closely approximated by the distance in the MPCC. In addition to V2I CSI, the network acquires V2V channel quality information for vehicles in the training set and develops a link quality predictor. MPCC provides a mapping for any vehicle location in the training set. To use MPCC for cognitive RRM of V2I and V2V communications, network management has to find logical MPCC locations for vehicles not in the training set, based on newly acquired V2I CSI measurements. For this, we develop an extension of LE-based MPCC to out-of-sample CSI samples. We evaluate the performance of link quality prediction for V2V communications in a mMIMO millimeter-wave scenario, in terms of the relative error of the predicted outage probability.

Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality.

Sexually dimorphic phrase organization in the song of the indris (Indri indri).

Animal acoustic communication often takes the form of complex sequences, composed of multiple distinct acoustic units, which can vary in their degree of stereotypy. Studies of sequence variation may contribute to our understanding of the structural flexibility of primates' songs, which can provide essential ecological and behavioral information about variability at the individual, population, and specific level and provide insights into the mechanisms and drivers responsible for the evolutionary change of communicative traits. Several methods have been used for investigating different levels of structural information and sequence similarity in acoustic displays. We studied intra and interindividual variation in the song structuring of a singing primate, the indri (Indri indri), which inhabits the montane rain forests of Madagascar. Indri groups emit duets and choruses in which they combine long notes, short single units, and phrases consisting of a variable number of units (from two to six) with slightly descending frequency. Males' and females' contributions to the song differ in the temporal and frequency structure of song units and repertoire size. We calculated the similarity of phrase organization across different individual contributions using the Levenshtein distance, a logic distance that expressed the minimum cost to convert a sequence into another and can measure differences between two sequences of data. We then analyzed the degree of similarity within and between individuals and found that: (a) the phrase structure of songs varied between reproductive males and females: female structuring of the song showed a higher number of phrases if compared to males; (b) male contributions to the song were overall more similar to those of other males than were female contributions to the song of other females; (c) male contributions were more stereotyped than female contributions, which showed greater individual flexibility. The picture emerging from phrase combinatorics in the indris is in agreement with previous findings of rhythmic features and song repertoire size of the indris, which also suggested that female songs are potentially less stereotyped than those of males.

A Fuzzy Decision Support Model for Cropland Recommendation of Food Cropping in Indonesia

Indonesia has an incredibly wide area for agriculture. The agriculture area in Indonesia has specific characteristic for each area (e.g., water capacity, land porosity, land height, etc.,). Furthermore, food crop is the most plant planted in Indonesia; rice, corn, red bean, green bean are some important food crops. The study aims to create a Decision Support Model based on fuzzy logic (FDSM). The model is able to recommend the most suitable food crop to be rationally planted in a specified area. The recommendation is based on distance value between plant and area characteristic value. Two main methods are operated in this study, they are fuzzy logic and Euclidean distance measurement. The method fuzzy logic purposely avoids the ambiguity of parameter value and considers the human linguistic based parameter value. Afterward, the Euclidean distance adopted to calculate the fitness value as a fundamental value for decision recommendation. Here, seven geographic parameters (i.e., water availability, temperature, humidity, land height, land slope, rainfall and land porosity) matched to food crop’s biotic parameters in finding the fittest value. Finally, the model shows a simulation of recommendation for 514 administrative areas of 34 provinces’ district/municipality (in Indonesia) based on five types of food crops (i.e., rice, maize, soybeans, green beans and peanuts).

A novel computation for predicting time series using fuzzy logical distance connectivity function and visibility graph theory

A novel computation for predicting time series using fuzzy logical distance connectivity function and visibility graph theory

Multifactorial Fuzzy Logic Distance Correction for UWB Positioning System

Multifactorial Fuzzy Logic Distance Correction for UWB Positioning System

Role of Transformational Leadership and Philosophical Mindset in Teachers

The subject of this article achieved by combining "transformational Leadership" and "Philosophical mindset", two important and affective issues in educational management and curriculum areas and its application in education teachers' performance. Transformational Leadership (management) theory offered by Burns (1978) in order to explain the behavior of group of Leaders and managers that connecting powerful motivational mutual relations with followers. Philosophical mindset is the way of thinking of correct function which has three dimensions: Comprehensiveness, penetration and flexibility, that offered by Philip, J. Smith (2016).
 Methods: In this paper we examine the philosophical spirit in the transformational leadership and philosophical mindset with functional wisdom features, attention to self actualization, future favorable theoretical and practical prospects according to the present (here and now), reasonable daubt, curiosity, wide visibility, tolerance, seeking progress and unity of the personality.
 Findings: Application of transformational Leadership and philosophical mindset in education teachers leads to improve correct, logical and creative scientifically thinking in them and eventually transfer these concepts to students.
 Results: This type of application relate the immediate issues to distant targets, intelligent rational behavior, avoid accelerated decisions, attention to the levels of psychological needs, logical and positive communication with students, attention to solving the problems, and logical distance from Clear definition of doing duties and having desirable work ethic.

Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems

Learning and exploring the connectivity of unknown networks represent an important problem in practical applications of communication networks and social-media networks. Modeling large-scale networks as connected graphs is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This paper investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish the VCS to define logical distance among nodes based on principal component analysis and to determine connectivity relationship and effective routing methods. More importantly, we present a robust analytical algorithm to derive the VCS against practical issues of missing and corrupted measurements. We also develop a connectivity inference method which classifies nodes into layers based on the hop distances and derives partial information on network connectivity.

Integrated Topology Management in Flying Ad Hoc Networks: Topology Construction and Adjustment

Flying ad hoc networks (FANETs) that consist of multiple unmanned aerial vehicles (UAVs) are promising technologies for future networked systems due to the versatility of UAVs. One of the most distinguishing features of FANET is frequent and rapid topological fluctuations due to the high-mobility of UAVs. Hence, the topology management adapting to the movements of UAVs is one of the most critical issues in FANET. In this paper, we study a FANET topology management problem that optimizes the locations and movements of UAVs to maximize the network performance, adapting to the topological changes while UAVs carry out their missions. When formulating the problem, we take into account the routing protocol as an arbitrary function since the network performance is inseparably linked with the routing protocol in use. We first develop two algorithms. One is the topology construction algorithm, which constructs a FANET topology from the scratch without any given initial topology, based on particle swarm optimization. The other is the topology adjustment algorithm, which incrementally adjusts the FANET topology adapting to the movements of UAVs with low-computational costs, based on gradient descent. Then, by defining a logical distance (the so-called topology edit distance) that measures the degree of changes in FANET topology, we develop an integrated topology management algorithm that contains the topology construction and adjustment algorithms. The simulation results show that our algorithm achieves a good network performance with low computational overhead, which is one of the most essential virtues in FANETs with rapidly varying topology.

HyCube: A distributed hash table based on a variable metric

Distributed Hash Tables (DHT) proved to be scalable decentralized systems providing efficient resource location. This paper concentrates on efficiency and resilience to node failures of DHT systems and presents a novel model of a distributed hash table based on a hierarchical hypercube geometry, called HyCube. The DHT geometry, the choice of the metric defining logical distances between nodes, and the routing algorithm have fundamental influence on routing efficiency and resilience. The use of the one-dimensional model (placing the nodes logically on a ring) allows the nodes to maintain sets of references called sequential neighbors - certain numbers of neighbors that are the closest existing nodes in both directions on the ring. Such a model yields a very high level of resilience to node failures. The new approach, presented in the paper, employs a variable multi-dimensional metric adopting the Steinhaus transform. Routing, lookup and search algorithms are discussed, as well as routing table nodes selection and self-organization techniques. It is shown that the new approach allows reaching a higher level of resilience to node failures, as well as a shorter average routing path length than with the use of the sequential neighbors sets.

Opportunistic Routing Using Prioritized Forwarders with Retransmission Control

Abstract In ad hoc networks, broadcast-based forwarding protocols called opportunistic routing have been proposed. In general backoff-based opportunistic routing protocols, each receiver autonomously makes a forwarding decision using a random backoff time based on logical distance. However, each potential forwarder must wait for the expiration of the backoff timer before the packet forwarding. Moreover, they cannot gain forwarding path diversity in sparse environments. In this paper, we propose a novel forwarder selection method for opportunistic routing. In the proposed method, a terminal called, a prioritized forwarder, and which is selected from among neighbours and can forward packets without using the backoff time. In addition, we integrate a hop-by-hop retransmission control in the proposed method, which improves the packet transmission success rate in sparse environments. We evaluate the proposed method in comparison with the conventional protocols in computer simulations.

Clustering analysis for muon tomography data elaboration in the Muon Portal project

Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data.

대규모 IoT 컴퓨팅 환경에서 동적 클러스터링 기반 효율적 관리 기법

IoT devices that collect information for end users and provide various services like giving traffic or weather information to them that are located everywhere aim to raise quality of life. Currently, the number of devices has dramatically increased so that there are many companies and laboratories for developing various IoT devices in the world. However, research about IoT computing such as connecting IoT devices or management is at an early stage. A server node that manages lots of IoT device in IoT computing environment is certainly needed. But, it is difficult to manage lots of devices efficiently. However, anyone cannot surly know about how many servers are needed or where they are located in the environment. In this paper, we suggest a method that is a way to dynamic clustering IoT computing environment by logical distance among devices. With our proposed method, we can ensure to manage the quality in large-scale IoT environment efficiently.

EAPOR: A Distributed, Energy-Aware Topology Control Algorithm Based Path–Obstacle–Remove Model for WSN

The obstacles existing in the propagation path cause shadow fading. As a consequence, signal's energy is additionally consumed to overcome the influence incurred by the shadow fading. This situation leads to the unpredictable communication environment in practical application. However, most topology control algorithms ignore the additional energy consumption in the process of appraising links' communication quality. The topologies based on the ideal signal attenuation model are too ideal to meet the requirements of practical application. In order to obtain a more practical description of the real environment, we structure a new model named path---obstacle---remove model. This model aims at erasing the influence of shadow fading. Thus, it transforms the additional attenuation energy into logic distance between nodes. Besides, considering that the excessive energy consumption of lower-energy nodes restricts the network lifetime, a distributed, energy-aware topology control algorithm based on path---obstacle---remove model (EAPOR) is proposed in this paper. The theoretical analysis demonstrates that the topology constructed by EAPOR is connected and bi-directional. Besides, EAPOR can easily construct the topology with a low message complexity of O(n). The simulation result shows that EAPOR has good performance on robustness and sparseness. Moreover, EAPOR reduces the end-to-end delay and prolongs the network lifetime significantly.

Opinion Impact Models and Opinion Consensus Methods in Ad Hoc Tactical Social Networks

Ad hoc social networks are special social networks, such as ad hoc tactical social networks, ad hoc firefighter social networks, and ad hoc vehicular social networks. The social networks possess both the properties of ad hoc network and social network. One of the challenge problems in ad hoc social networks is opinion impact and consensus, and the opinion impact plays a key role for information fusion and decision support in ad hoc social networks. In this paper, consider the impact of physical and logical distance on the opinions of individuals or nodes in heterogeneous social networks; we present a general opinion impact model, discuss the local and global opinion impact models in detail, and point out the relationship between the local opinion impact model and the global opinion impact model. For understanding the opinion impact models easily, we use the general opinion impact model to ad hoc tactical social networks and discuss the opinion impact and opinion consensus for ad hoc tactical social networks in the end.

Test Point Insertion with Control Points Driven by Existing Functional Flip-Flops

This paper presents a novel test point insertion method for pseudorandom built-in self-test (BIST) to reduce the area overhead. The proposed method replaces dedicated flip-flops for driving control points by existing functional flip-flops. For each control point, candidate functional flip-flops are identified by using logic cone analysis that investigates the path inversion parity, logical distance, and reconvergence from each control point. Four types of new control point structures are introduced based on the logic cone analysis results to avoid degrading the testability. Experimental results indicate that the proposed method significantly reduces test point area overhead by replacing the dedicated flip-flops and achieves essentially the same fault coverage as conventional test point implementations using dedicated flip-flops driving the control points.

Min-Distance Hop Count based Multi-Hop Clustering In Non-uniform Wireless Sensor Networks

In wireless sensor networks, an energy efficient data gathering scheme is one of core technologies to process a query. The clusterbased data gathering methods minimize the energy consumption of sensor nodes by maximizing the efficiency of data aggregation. However, since the existing clustering methods consider only uniform network environments, they are not suitable for the real world applications that sensor nodes can be distributed unevenly. To solve such a problem, we propose a balanced multi-hop clustering scheme in non-uniform wireless sensor networks. The proposed scheme constructs a cluster based on the logical distance to the cluster head using a min-distance hop count. To show the superiority of our proposed scheme, we compare it with the existing clustering schemes in sensor networks. Our experimental results show that our proposed scheme prolongs about 48% lifetime over the existing methods on average.