BEvote: Bitcoin-Enabled E-Voting Scheme with Anonymity and Robustness

When building the large-scale distributed decision control system based on mobile terminal devices (MTDs), electronic voting (E-voting) is a necessary technique to settle the dispute among parties. Due to the inherent insecurity of Internet, it is difficult for E-voting to attain complete fairness and robustness. In this study, we argue that Bitcoin blockchain offers better options for a more practical E-voting. We first present a coin mixing-based E-voting system model, which can cut off the relationship between the voter’s real identity and its Bitcoin address to achieve strong anonymity. Moreover, we devise a secret sharing-based E-voting protocol, which can prevent voting number from being leaked ahead and further realize strong robustness. We establish the probable security theory to prove its security. In addition, we use the experimental evaluation to demonstrate its efficiency.

Sensor-Based Environmental Perception Technology for Intelligent Vehicles

Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

THE EFFORTS TO DEVELOP SELF-CONTROL SKILLS TO BECOME AN EFFECTIVE PERSON

Each person strives to mold himself into a mature or effective person. One of the characteristics of a mature or effective person is being able to control every emotion or impulse from within. Self-control is the ability to direct one's own behavior and suppress the various urges that exist. The impulses that appear can be controlled by means of behavioral control, cognitive control, decision control, informational control, and retrospective control. And self-control should follow ethical or moral principles, the principle of awareness, the principle of reflection, the principle of patience, and the principle of distraction towards objects or something else. The objective of this research is to examine the efforts made by individuals to develop the ability to control themselves or control emotions in their lives. To achieve this goal, the researcher conducted a literature study taken from books and journals. The results of the study show three efforts made to develop self-control abilities, namely growing self-awareness, managing one emotion, and trying to become a mature person.

KONTRIBUSI GURU PENDIDIKAN AGAMA ISLAM (PAI) DALAM MENGEMBANGKAN SELF CONTROL SISWA DI SMA DUA MEI CIPUTAT

ABSTRACTThis study aims to describe the contribution of Islamic Religious Education teachers in developing self-control in students at SMA Dua Mei Ciputat. This type of research is a type of qualitative or non-statistical research. Qualitative research is an intensive search using scientific procedures to produce narrative conclusions both written and oral based on analysis of certain data. Sources of data in research are the subjects from which data can be obtained. The research subject was the Islamic Religious Education (PAI) teacher at SMA Dua Mei. According to Averill in M. Ghufron, self control is called personal control, namely behavior control, cognitive control and decision control. The conclusion of this study is that the teachers of Islamic Religious Education (PAI) at SMA Dua Mei Ciputat are sufficient to contribute in developing self-control for students at SMA Dua Mei Ciputat. It can be seen that the PAI teacher has tried to develop every aspect of self-control, namely the cognitive control aspect by providing more information about knowledge about Islam by integrating PAI learning material with the surrounding conditions, then the control aspect. The behavior (behavior control) of Islamic Education teachers provides habituation both to students inside and outside the classroom, such as discipline, accustoming students to praying in congregation, as well as aspects of decision control, where students are given the opportunity to have an opinion during discussions conducted with the guidance of the PAI teacher. PAI teachers also provide guidance and direction to students who violate school regulations. From the various contributions of Islamic Religious Education (PAI) teachers, it is certainly very influential on the development of student self-control, this can be seen from the habituation applied by Islamic Religious Education (PAI) teachers can provide positive changes to students, such as students having awareness in worshiping and participating in religious activities without coercion from the PAI teacher, students have self-confidence, and make students more interested in deepening the knowledge of Islam.Keywords: Teacher Contribution, Islamic Education and Self-Control

Exploring the key challenges facing company secretaries in a two-tier board context

Purpose This study aims to examine the core challenges facing company secretaries in a two-tier board context. This study focuses on the key factors contributing to these challenges and how company secretaries can effectively address them. Design/methodology/approach An analysis of the narratives provided by 291 Dutch company secretaries in response to a series of open-ended questionnaire questions led to insights into the key challenges company secretaries face in their day-to-day work. Findings Company secretaries perceive a myriad of factors contributing to pressures on their time, the need to work for multiple organizational bodies and the processing of information. They believe process interventions and social interventions are needed to alleviate these issues. Research limitations/implications The research highlights the need to deeply study boards from a holistic and systems point of view that recognizes the various actors, such as the company secretary, and their relationships in a boardroom context. Furthermore, the research shows how the two-tier board model may complicate these relational dynamics owing to the formal separation of decision management from decision control. Practical implications This study identifies various pragmatic ways to address the core challenges facing company secretaries so as to improve their contributions to decision-making at the apex of organizations. Originality/value This study sheds light on an important organizational actor (i.e. the company secretary) that hitherto has received scant attention in the governance literature.

Preferences, Machine Learning, and Decision Support With Cyber-Physical Systems

Modeling of complex processes with human participations causes difficulties due to the lack of precise measurement coming from the qualitative nature of the human notions. This provokes the need of utilization of empirical knowledge expressed cardinally. An approach for solution of these problems is utility theory. As cyber-physical systems are integrations of computation, networking, and physical processes in interaction with the user is needed feedback loops, the aim of the chapter is to demonstrate the possibility to describe quantitatively complex processes with human participation. This approach permits analytical representations of the users' preferences as objective utility functions and modeling of the complex system “human-process.” The mathematical technique allows CPS users dialog and is demonstrated by two case studies, portfolio allocation, and modeling of a competitive trade by a finite game and utility preference representation of the trader. The presented formulations could serve as foundation of development of decision support tools and decision control.

Target Learning in Event-Based Prospective Memory

Event-based prospective memory (PM) tasks require individuals to remember to perform a previously planned action when they encounter a specific event. Often, the natural environments in which PM tasks occur are embedded are constantly changing, requiring humans to adapt by learning. We examine one such adaptation by integrating PM target learning with the Prospective Memory Decision Control (PMDC) cognitive model. We apply this augmented model to an experiment that manipulated exposure to PM targets, comparing a single-target PM condition where the target was well learned from the outset, to a multiple-target PM condition with less initial PM target exposure, allowing us to examine the effect of continued target learning opportunities. Single-target PM accuracy was near ceiling whereas multiple-target PM accuracy was initially poorer but improved throughout the course of the experiment. PM response times were longer for the multiple- compared to single-target PM task but this difference also decreased over time. The model indicated that PM trial evidence accumulation rates, and the inhibition of competing responses, were initially higher for single compared to multiple PM targets, but that this difference decreased over time due to the learning of multiple-targets over the target repetitions. These outcomes provide insight into how the processes underlying event-based PM can dynamically evolve over time, and a modelling framework to further investigate the effect of learning on event-based PM decision processes.

Model-Based Optimization of a Fed-Batch Bioreactor for mAb Production Using a Hybridoma Cell Culture

Production of monoclonal antibodies (mAbs) is a well-known method used to synthesize a large number of identical antibodies, which are molecules of huge importance in medicine. Due to such reasons, intense efforts have been invested to maximize the mAbs production in bioreactors with hybridoma cell cultures. However, the optimal control of such sensitive bioreactors is an engineering problem difficult to solve due to the large number of state-variables with highly nonlinear dynamics, which often translates into a non-convex optimization problem that involves a significant number of decision (control) variables. Based on an adequate kinetic model adopted from the literature, this paper focuses on developing an in-silico (model-based, offline) numerical analysis of a fed-batch bioreactor (FBR) with an immobilized hybridoma culture to determine its optimal feeding policy by considering a small number of control variables, thus ensuring maximization of mAbs production. The obtained time stepwise optimal feeding policies of FBR were proven to obtain better performances than those of simple batch operation (BR) for all the verified alternatives in terms of raw material consumption and mAbs productivity. Several elements of novelty (i–iv) are pointed out in the “conclusions” section (e.g., considering the continuously added biomass as a control variable during FBR).