Miracle-dz/UniFAR-FTUs huggingface dataset:
A Formal Methodology for Deriving Purely Functional Programs From Z Specifications via the Intermediate Specification Language FunZ.

In recent years, both formal methods and software reuse have been increasingly advocated as a means of alleviating the ills of the software crisis. During this same time period, purely functional programming languages, which have a long history in the realm of rapid prototyping, have emerged as a viable medium for real-world applications. Since these trends are likely to continue, this work describes a methodology that facilitates the derivation of purely functional programs from existing Z specifications. A unique aspect of the methodology is its incorporation of an intermediate specification language (FunZ) during the design phase of software development. Most of the previous techniques for translating Z specifications to functional programs were designed primarily to expedite rapid prototyping. In contrast, the FunZ methodology, which is an adapted form of the IBM Hursley method, is a comprehensive approach, spanning the software life cycle from specification through design to final implementation. Due to its greater scope, the FunZ methodology offers several advantages over existing approaches. First, the specification language integrates features from Z with those of the functional programming paradigm to provide a bridge between Z specifications and functional implementations. Since FunZ is expressly designed to target functional languages, the implementor's job is simplified. In fact, a FunZ document looks like extended Haskell code, so an obvious effect in applying FunZ is that the distance from design to code is reduced. Second, the methodology provides a framework for recording design decisions, which is useful for future maintenance. Within this framework, users may select a development path ranging from an intuitive style to a fully formal approach that includes the proofs of functional refinement. Furthermore, FunZ allows software developers to prove properties about a system design within the realm of Z or Haskell. This means that proofs can be performed throughout software development and the designer is free to select the most appropriate notation. In summary, the intermediate specification language FunZ and its related methodology provide software developers with a complete, formal approach for translating Z specifications to Haskell implementations. Previously, such extensive methods were only available for traditional, imperative languages.
A Unified Management Framework for EIoT Systems Based on Metadata and Event Detection

Environmental Internet of Things (EIoT) has been widely applied in environmental monitoring. However, unified management, which is important for improving the operability and interoperability of EIoT systems, is difficult to perform due to the ubiquitous heterogeneity of EIoT systems. This paper puts forward an analysis of EIoT system management patterns and then proposes a storage layer monitoring (SLM) method to understand the state of EIoT systems, which is the basis for systems’ management. A unified management framework for EIoT systems based on SLM is proposed, which includes event detection and fault diagnosis assistance. The framework uses a database to store metadata pertaining to each EIoT system, which are mostly registered as ontologies. Based on pre-registered metadata, users can create listeners for events, which is applicable for detecting events occurring in specific EIoT storage layers through the SLM method. The fault diagnosis assistance function can provide ontology-based evidence and other useful information about the participating objects of the event in the system, which is helpful for identifying the cause of each event. This framework could satisfy the requirements for multiple EIoT systems’ management.
Framework of Cascade Quality Prediction Method Using Latent Variables for Multi-Stage Manufacturing

Prediction model in data mining context is the formulation that explains the relationship between predictor and response variables. In similar, quality prediction model in manufacturing context is the formulation that explains the relationship between manufacturing operation and quality variables. Quality prediction model, as the key to realize the real-time quality manufacturing process, has been developed using various data mining techniques. Unfortunately, most of quality prediction models are developed in single-stage manufacturing system (SMS) where the relationship between manufacturing operation and quality variables is straightforward. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing system due to the complex variable relationships. This conceptual study is intended to propose a framework to develop quality prediction model which is able to deal with the complex variable relationships in multi-stage manufacturing system (MMS). This framework, named Cascade Quality Prediction Method (CQPM), is developed based on the characteristics of multi-stage manufacturing system that have been explained in several studies. CQPM is expected to produce more accurate quality prediction model since it is able to explain all type of existed variable relationship in MMS. However further investigation regarding the performance of CQPM and the selection of the data mining technique to be employed is required
Empirical study of malware diversity in major Android markets

ABSTRACT The popularity of Android has motivated a significant increase in the amount of malware specially designed to target this operating system. During the last years, the threat has become more serious and every day cybercriminals create and share new specimens through almost all existing markets. This situation has promoted a notable research interest in the development of automated malware detection and classification systems. In this paper, we perform a large-scale empirical study to examine the diversity of Android malware in major markets. Through the analysis of more than 5 million of apps, we use the labels assigned by 57 different anti-malware vendors and diversity measures to get insights about the distribution and evolution of Android malware. Furthermore, we propose a dissimilarity measure for comparing these labels, which can be applied as part of an agglomerative hierarchical clustering algorithm. This clustering method groups the labels according to the scanning reports of different anti-malware vendors. The results obtained make evident an increase in the diversification of malware in both official and alternative markets. Moreover, we show how the criteria of various anti-malware, in conjunction with clustering techniques, is a suitable approach for grouping and analysing malware samples that perform a similar behaviour.
Investigating effects of interactive signage–based stimulation for promoting behavior change

In recent years, many types of research and developments on behavior change have been conducted. The purpose of behavior change is to improve people's lifestyle pattern or to maintain the improvement for a long time with the aim to achieve a goal such as promoting health condition improvement. To achieve the foundation of a new lifestyle, it is necessary to recognize the daily life patterns of users and give triggers for behavior change to users in their daily life. To realize this, in our research, we develop an interactive signage which is able to identify and actively talk to the passing user and try to induce behavior change by sending visual and auditory stimulation. Then, we record users' reactions and upload them to the server. In this paper, we report the investigation result on users' reactions and feelings to the developed interactive signage. As a survey experiment, we set up four interactive signs on a floor of our university and asked 15 participants to carry a name tag with a Bluetooth Low Energy beacon during their daily life. Five kinds of tasks based on dialogue scenarios are posted to the approaching participants. Participants can respond to these tasks through a touchscreen. The period of the experiment was three weeks. To get the data in an ideal environment, during the first week, we asked all the participants to respond to the utterance from the interactive signage whenever they hear the voice message. During the next two weeks, participants were not asked to respond to the task definitely to get the data in the real environment. The result of the experiment showed that our proposed interactive signage could induce behavior change effectively. Based on the result of experiment, we updated our interactive signage system by adding response time (the time passed from showing contents until user respond), record function, and voice feedback function. Furthermore, to collect the data of response time that is considered as a part of users' reactions, we conducted an additional experiment with the same participants in previous experiment (except for one missing participant) for one week after updating the system. In the additional experiment, the participants were not asked to respond to the utterance definitely. As a result, it is shown that the behavior change by the proposed signage is still effectively induced. We also analyzed the relationship between the day passed and the response rate of each task type. The result shows that the number of ignorance of personal task and check task does not rise even as the time passes.
Towards multi-agent based solution for ontology alignment

Ontology alignment is an ontology mapping technique, which is used for sharing and reusing the content of disparate source of ontologies. Aligning dynamic and distributed ontologies turn out to be a research challenge for many decades. This paper discusses the complexity of the ontology alignment process and presents some major tools that have been developed to support knowledge engineers. Then, we discuss how the agent technology can be used to align dynamic ontologies in distributed environments. Our solution is inspired by how group of people communicate, negotiate, and coordinate to reach common agreements. Index Terms Autonomous Agents, Multi Agent Systems, Ontology Alignment
ARTIFACT-CENTRIC SEMANTIC SOCIAL-COLLABORATIVE NETWORK IN AN ONLINE HEALTHCARE CONTEXT

The emergence of Web 2.0 technology and associated social networking system, brings many possibilities and features for online collaboration. Several reference models, frameworks, tools and infrastructures have been proposed to support seamless interaction and communication between human entities in an online social environment. A few studies indicated that social networks are not only constructed on social connections of people, but also mediated by shared objects, known as object-centred sociality. However, most of the developed social software system was centred around the concept of maintaining human-centric social relationships only. This may be because of the common issues of exploiting social network approach for online collaboration related to maintaining the social behaviour, interaction and communication between human and artifacts themselves. These resources or artifacts (i.e., physical objects, software entities, documents, etc.) are active elements in a way that they may coordinate, cooperate, and even trigger collaborative work in a social environment, which is difficult understand and implement. Thus, it is essential to concentrate on exploring the artifact-centric social relations in a new generation of social-collaboration network. This paper explores the concept and characteristics of the social software system and emphasise the importance and role of objects and artifact-centric sociality in a social-collaboration network. We also outline the benefits of semantic representation of social-collaborative network structure by extending existing social ontologies such as FOAF, SIOC, and DC that define additional concepts, properties and complex social relationship of humans, social objects and collaboration artifacts. Finally, we demonstrate the effectiveness of our proposed approach by applying it to a large-scale social-collaborative healthcare service called GRaCE-AGE within the United Kingdom.
Time-Multiplexed FPGA Overlay Architectures

This article presents a comprehensive survey of time-multiplexed (TM) FPGA overlays from the research literature. These overlays are categorized based on their implementation into two groups: processor-based overlays, as their implementation follows that of conventional silicon-based microprocessors, and; CGRA-like overlays, with either an array of interconnected processor-based functional units or medium-grained arithmetic functional units. Time-multiplexing the overlay allows it to change its behavior with a cycle-by-cycle execution of the application kernel, thus allowing better sharing of the limited FPGA hardware resource. However, most TM overlays suffer from large resource overheads, due to either the underlying processor-like architecture (for processor-based overlays) or due to the routing array and instruction storage requirements (for CGRA-like overlays). Reducing the area overhead for CGRA-like overlays, specifically that required for the routing network, and better utilizing the hard macros in the target FPGA are active areas of research.
Blind Pilot Decontamination With Hierarchical Design

In massive multiple input multiple output systems, the nonlinear channel estimation method has been initially proved to completely eliminate pilot contamination, which is based on spectrum separation theory to remove interference through projecting it onto the subspace of the desired signal. However, in view of the existing system parameters design for ensuring exact spectrum separation being not accurate, a hierarchical system parameter design method is proposed in this letter. It clarifies the different functions of the different system parameters, so that the asymptotic spectrum of the covariance matrix of the received signal could be more accurately separated. Furthermore, according to the proposed numerical algorithm, in the presence of pilot interference, a better tradeoff between channel estimation quality and pilot overhead is given. It uses pilots as few as possible to obtain the most of the channel estimation quality gain, which is brought by projecting onto the subspace of the separable asymptotic spectrum described above. All of these make our approach more practical.
Density Peaks Clustering Based on Local Minimal Spanning Tree

The fake center is a common problem of density-based clustering algorithms, especially for datasets with clusters of different shapes and densities. Clustering by fast search and find of density peaks (DPC) and its improved versions often ignore the effect of fake centers on clustering quality. They usually have a poor performance even the actual number of centers are used. To solve this problem, we propose a density peaks clustering based on local minimal spanning tree (DPC-LMST), which generates initial clusters for each potential centers first and then introduce a sub-cluster merging factor (SCMF) to aggregate similar sub-clusters. Meanwhile, we introduce a new strategy of representative points to reduce the size of data and redefine local density <inline-formula> <tex-math notation="LaTeX">$\rho _{i}$ </tex-math></inline-formula> and distance <inline-formula> <tex-math notation="LaTeX">$\delta _{i}$ </tex-math></inline-formula> of each representative point. Furthermore, the hint of <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> is redesigned to highlight true centers for datasets with clusters of different densities. The proposed algorithm is benchmarked on both synthetic and real-world datasets, and we compare the results with K-means, DPC, and the three state-of-the-art improved DPC algorithms.
Analyzing NDN NACK on Interest Flooding Attack via SIS Epidemic Model

Security properties are within the fundamental concerns in the original protocol stack of named data networking (NDN) paradigm. However, NDN still cannot evade some serious network attacks, such as the notorious interest flooding attack (IFA). To mitigate the IFA, interest Negative ACKnowledgments (NACK) may be one of the best countering candidates in NDN, yet does not attract enough concerns. In this paper, we first present a more powerful type of IFA using interest packets with real names rather than spoofed ones to confuse detections. Then, we evaluate the effectiveness of interest NACK on mitigating IFA via a modified susceptible-infected-susceptible epidemic model. In this quantitative analysis tool, we introduce a variable curing parameter to accurately characterize the impact of interest NACK on chosen NDN routers. Moreover, we derive the upper bound for the infection fraction of NDN routers with and without interest NACK, and deduce the relationship between the fraction of total infected nodes and those with NACK implementation. In addition, the accuracy of the proposed model is verified by extensive simulations. To the best of our knowledge, this is the first attempt to theoretically analyze the NDN NACK mechanism on mitigating IFA via the epidemic theory.
Pitch-class prevalence in plainchant, scale-degree consonance, and the origin of the rising leading tone

The most common tones in Medieval plainchant were G and D; the least common, B. I propose that individual scale degrees differed in perceived consonance depending on the number of audible partials (harmonics) corresponding to scale degrees. The first ten harmonics of G correspond to scale degrees (G, D, B, F, A), but not those of B (B, F#, D#, A, C#). More consonant tones were sung more often and became more stable (reference pitches). Tones C and F (fa) were sung more often than B and E (mi). The melodic semitone became psychologically associated with a difference in stability.
Handover Mitigation in Dense HetNets via Bandit Arm Elimination

Dense heterogeneous network (HetNet) has become one of the key architectural features in the 5G communication network. In this paper, we aim at solving the frequent handover (FHO) problem in dense HetNets. We first present an unified framework suitable for various optimization objectives in wireless communication networks. A novel bandit algorithm is then proposed to obtain better results compared with the existing works. The corresponding bounds for the regret and the number of handovers are rigorously proved accordingly. The results are further generalized by taking into account the imperfect feedbacks. Extensive performance comparisons with different bandit algorithms are provided in order to highlight the advantages of the proposed algorithm and its potential for pursuing a general solution.
Torque control based direct teaching for industrial robot considering temperature-load effects on joint friction

Purpose The purpose of this paper is to design a practical direct teaching method for the industrial robot with large friction resistance and gravity torque but without expensive force/torque sensor, where the gravity torque is just a function of joints position, whereas the friction is closely associated with joint velocity, temperature and load. Design/methodology/approach In the teaching method, the output torque of joint motor is controlled through current to compensate gravity torque completely and friction resistance incompletely. Three variables closely associated with friction are investigated separately by experiment and theoretical analysis, and then a comprehensive friction model which is used to calculate the required compensated friction torque is proposed. Finally, a SIASUN 7 degrees of freedom robot was used to verify the model and the method. Findings Experimental results demonstrated that the teaching method enables an operator to teach the robot in joint space by applying small force and torque on either end-effector or its body. The friction investigation suggests that the velocity and temperature have a strong nonlinear influence on viscous friction, whereas load torque significantly influences the Coulomb friction linearly and causes a slight Stribeck effect. Originality/value The main contribution includes the following: a practical joint space direct teaching method for a common industrial robot is developed, and a friction model capturing velocity, temperature and load for robot joints equipped with commercialized motors and harmonic drives is proposed.
A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization

The development of algorithms to solve Many-objective optimization problems (MaOPs) has attracted significant research interest in recent years. Solving various types of Pareto front (PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm (RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector adaptation. Information concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.
A Study of Unsupervised Outlier Detection for One-Class Classification

One-class classification is a problem that arises in situations where we have data available that describes objects belonging to a particular class but very little or no data describing objects that do not belong to this class, where we must then be able to classify new data objects according to whether or not they belong to this class. Outlier detection is a similar problem where we are presented with an unlabelled collection of data and must determine whether the data objects are outliers or inliers according to some definition of an outlier. In this thesis we explore the relationship between one-class classification and outlier detection by comparing methods used for each problem in a common framework, investigate some unique issues in applying one-class classification in a realistic setting, as well as consider methods to combine one-class classifiers. In comparing one-class classification and outlier detection, we note that they are similar problems in that both are looking to classify data objects as either inlier or outlier. We extend previous comparison studies by studying a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics using the commonly used area under the receiver operating characteristic curve (AUC) measure, as well as the adjusted precision-at-n measure used in unsupervised outlier detection. An additional contribution is the adaptation of the unsupervised outlier detection method, GLOSH, to the one-class classification setting. ii The lack of outlier data objects available for training means that we cannot use the standard procedure of using a validation set in order to estimate the generalization performance of a model for one-class classification, and so selecting good parameters for a method can be difficult. We investigate this problem by comparing the performance of methods at different parameter values to determine how stable their performance is with respect to their parameters, and whether certain parameter settings are likely to do well across multiple datasets. In combining one-class classifiers, we apply rank-based combination strategies to the outlier rankings produced by multiple one-class classifiers and compare different strategies. We find that simple combinations of ranks can produce robust classifiers which outperform individual classifiers.