Future Internet — Open Access Journal

Online reputational systems are nowadays widely and effectively adopted by several online platforms to support and improve peoples’ interactions and communication. Despite the research approached and modeled social dynamics of reputational systems in different domains, adopting different frameworks, the role played by psycho-social factors, and personality traits, determining the individual susceptibility to online reputation is still elusive. To study such mediation effects, we implemented a modified online version of the Ultimatum Game, in which participants (215 adolescents) played before as proposers, and then as responders, always knowing the reputation of their interactors. Furthermore, after the reception phase, participants could evaluate the received offers, giving positive or negative feedback to their proposers. Despite the participants’ belief they were playing with their schoolmates, the interactors’ role was always fulfilled by bots characterized by standardized behaviors. Our results show how psychological traits influence the participants’ behavior in all the game phases, as well as in the rating dynamics. Reputation seems to have a direct effect only in the allocation behavior, while, in regards the other dynamics of the game (i.e., acceptance and rating), it comes into play in a complex interaction with the psychological dimensions. Full article

The introduction of the internet in embedded devices led to a new era of technology—the Internet of Things (IoT) era. The IoT technology-enabled device market is growing faster by the day, due to its complete acceptance in diverse areas such as domicile systems, the automobile industry, and beyond. The introduction of internet connectivity in objects that are frequently used in daily life raises the question of security—how secure is the information and the infrastructure handled by these devices when they are connected to the internet? Security enhancements through standard cryptographic techniques are not suitable due to the power and performance constraints of IoT devices. The introduction of virtualization technology into IoT devices is a recent development, meant for fulfilling security and performance needs. However, virtualization augments the vulnerability present in IoT devices, due to the addition of one more software layer—namely, the hypervisor, which enables the sharing of resources among different users. This article proposes the adaptation of ASMI (Architectural Support for Memory Isolation—a general architecture available in the literature for the improvement of the performance and security of virtualization technology) on the popular MIPS (Microprocessor without Interlocked Pipeline Stages) embedded virtualization platform, which could be adopted in embedded virtualization architectures for IoT devices. The article illustrates the performance enhancement achieved by the proposed architecture with the existing architectures. Full article

To address the continuous growth in high-speed ubiquitous access required by residential users and enterprises, Telecommunication operators must upgrade their networks to higher data rates. For optical fiber access networks that directly connect end users to metro/regional network, capacity upgrade must be done in a cost- and energy-efficient manner. 40 Gb/s is the possible lane rate for the next generation passive optical networks (NG-PONs). Ideally, existing 10 G PON components could be reused to support 40 Gb/s lane-rate NG-PON transceiver, which requires efficient modulation format and digital signal processing (DSP) to alleviate the bandwidth limitation and fiber dispersion. The major contribution of this work is to offer insight performance comparisons of 40 Gb/s lane rate electrical three level Duobinary, optical Duobinary, and four-level pulse amplitude modulation (PAM-4) for incorporating low complex DSPs, including linear and nonlinear Volterra equalization, as well as maximum likelihood sequence estimation. Detailed analysis and comparison of the complexity of various DSP algorithms are performed. Transceiver bandwidth optimization is also undertaken. The results show that the choices of proper modulation format and DSP configuration depend on the transmission distances of interest. Full article

A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms. Full article

The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods. Full article

In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video. It is well known that convolutional neural networks require a fixed size image input, which not only limits the network structure but also affects the recognition accuracy. Although this problem has been solved in the field of images, it has not yet been broken through in the field of video. To address the input problem of fixed size video frames in video recognition, we propose a three-dimensional (3D) densely connected convolutional network based on spatial pyramid pooling (3D-DenseNet-SPP). As the name implies, the network structure is mainly composed of three parts: 3DCNN, DenseNet, and SPPNet. Our models were evaluated on a KTH dataset and UCF101 dataset separately. The experimental results showed that our model has better performance in the field of video-based behavior recognition in comparison to the existing models. Full article

The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media. Full article

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts. Full article

With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms. Full article

Facial nerve paralysis (FNP) is the most common form of facial nerve damage, which leads to significant physical pain and abnormal function in patients. Traditional FNP detection methods are based on visual diagnosis, which relies solely on the physician’s assessment. The use of objective measurements can reduce the frequency of errors which are caused by subjective methods. Hence, a fast, accurate, and objective computer method for FNP classification is proposed that uses a single Convolutional neural network (CNN), trained end-to-end directly from images, with only pixels and disease labels as inputs. We trained the CNN using a dataset of 1049 clinical images and divided the dataset into 7 categories based on classification standards with the help of neurologists. We tested its performance against the neurologists’ ground truth, and our results matched the neurologists’ level with 97.5% accuracy. Full article

In recent years, the laboratory security of universities has become an important issue for students and devices. To solve this security issue, this paper proposes an intelligent monitoring system to realize environment detection in university laboratories. The main purpose of this system is to monitor the laboratory environment data in time and improve the laboratory inspection efficiency. The system consists of a single chip microcomputer, which is the core of this system, a sensor function module and GPRS wireless communication, realizing data monitoring and short message warning. Therefore, three features, front-end data acquisition, data wireless transmission and a security alarm, are achieved by the proposed system. The real experiments show that front-end data acquisition is effective, data transmission is reliable, and the alarm message is received in time. Furthermore, the system, with the modified function modules, can be used in other scenarios to detect environments, and thus has a significant applied value in other areas. Full article

Optical access networks, metro networks and mobile data networks are facing rapidly evolving demands, not only is it essential to satisfy the unyielding need for increased user bandwidths, but future networks must also support the growing wide variation in traffic dynamics and characteristics, due to various emerging technologies, such as cloud-based services, the Internet-of-Things (IoT) and 5G mobile systems, and due to growing trends, such as the proliferation of mobile devices and the rapidly increasing popularity of video-on-demand services. To be cost-effective and commercially sustainable, future optical networks must offer features, such as, dynamic reconfigurability, highly efficient use of network resources, elastic bandwidth provisioning with fine granularity, network sliceabilty and software defined networking (SDN). To meet these requirements Cloud Access Networks (CANs) are proposed which require a number of flexible, adaptive and reconfigurable networking elements. By exploiting digital signal processing (DSP) we have proposed a digital orthogonal filter-based multiplexing technique to implement CANs with multiplexed, independent optical channels at the wavelength, sub-wavelength, and orthogonal sub-band levels. This paper reviews the overall CAN concept, the operating principles of the various CAN network elements and presents an overview of the research work we have undertaken in order to validate the feasibility of the proposed technologies which includes real-time DSP-based demonstrations. Full article

This paper addresses the problem of museum visitors’ Quality of Experience (QoE) optimization by viewing and treating the museum environment as a cyber-physical social system. To achieve this goal, we harness visitors’ internal ability to intelligently sense their environment and make choices that improve their QoE in terms of which the museum touring option is the best for them and how much time to spend on their visit. We model the museum setting as a distributed non-cooperative game where visitors selfishly maximize their own QoE. In this setting, we formulate the problem of Recommendation Selection and Visiting Time Management (RSVTM) and propose a two-stage distributed algorithm based on game theory and reinforcement learning, which learns from visitor behavior to make on-the-fly recommendation selections that maximize visitor QoE. The proposed framework enables autonomic visitor-centric management in a personalized manner and enables visitors themselves to decide on the best visiting strategies. Experimental results evaluating the performance of the proposed RSVTM algorithm under realistic simulation conditions indicate the high operational effectiveness and superior performance when compared to other recommendation approaches. Our results constitute a practical alternative for museums and exhibition spaces meant to enhance visitor QoE in a flexible, efficient, and cost-effective manner. Full article

Composting is the delicate procedure of supervised decomposition of organic waste, which gradually transforms waste to nutrient-rich manure. It requires deep knowledge and constant attention by experts to achieve a quality outcome in a timely fashion. Nevertheless, due to the bizarre nature of the materials and the overall procedure, along with the space required and emitted odors, it is required that composting infrastructures and machinery are installed away from residential areas, rendering supervision a very tedious task. Automatic composting machinery is a promising new idea, but still cannot substitute the insightfulness of a human supervisor. In this paper, we introduce COMPosting as a Service (COMPaaS). COMPaaS is a novel cloud service in composition with specialized Internet of Things (IoT)-based composting machinery that allows for unsupervised composting. The focus of this work is on the tiered IT approach that is adopted following the edge-computing paradigm. More specifically, composting machinery, enriched with several sensors and actuators, performs a set of basic routine tasks locally and sends sensor values to a cloud service which performs real-time data analysis and instructs the composting machinery to perform the appropriate actions based on the outcome of the analysis. The overall composting procedure is performed in a completely unsupervised manner, and field evaluation has shown an up to 30% faster outcome in comparison to traditional supervised composting. Full article

In order to put Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) in a hybrid system, this paper presents the design and implementation of a RFID reader/router that can obtain information of both RFID tags and WSN sensor nodes and transmit the information through the WSN to the PC server. The RFID reader and WSN router are combined with both hardware and software. In hardware structure, CC2530 is used as micro controller and RF module for ZigBee wireless communication, and MF RC522 is used as reader RF chip. The software deals with both identity and sensing information and controls the routing. Experiment results show that the RFID reader/router achieves long distance identification, flexibility, scalability, and low cost. It also provides reliable and secured data transmission and broadens the communication range and application scope of RFID readers. Full article

The Internet of Things (IoT) is increasingly part of daily life. However, the development of IoT applications still faces many problems, such as heterogeneity, complex management, and other difficulties. In this paper, first, the open source technologies of IoT are surveyed. We compare these technologies from the point of view of different levels of technical requirements, such as device management, data management, communication, intelligent data processing, security and privacy protection; we also look at requirements of application development and deployment. Second, an IoT integrated development platform architecture for IoT applications based on open source ecosystem is proposed and evaluated in an industrial setting. We applied P2P technology to distributed resource management and blockchain-based smart contract mechanics for resource billing management. The results show that the IoT gateway based on an open source ecosystem had a stable and reliable system performance with a certain data size and concurrency scale. These conditions satisfy the application requirements of the IoT in most sensing environments. Full article

RFID (radio frequency identification) is widely used in the manufacturing processes of enterprises. At the same time, with the advent of the Industry 4.0 era, Manufacturing Execution System (MES) systems began to evolve into cloud MES systems. In this paper, a RFID-based manufacturing process for cloud MES is proposed and a framework manufacturing process fora cloud MES system centered on machine tools is constructed. The process division of the manufacturing process, RFID configuration and cloud processing are analyzed, and other key technologies involved in implementing the framework are briefly discussed. Finally, the effectiveness of a RFID-based manufacturing process of cloud MES is verified by two different types of case analysis namely photovoltaic slice production and garment outsourcing processing. Full article

An ICO (Initial Coin Offering) is an innovative way to fund projects based on blockchain. The funding is based on the selling of tokens by means of decentralized applications called smart contracts written in Solidity, a programming language specific for Ethereum blockchain. The ICOs work in a volatile context and it is crucial that the team is capable of handling constant changes. The Agile methods, proven practices enabling to develop software in presence of changing requirements, could be a means for managing uncertainty. The main goals of this work are to understand software engineering activities related to ICOs, recognize the ICOs developed using Agile methods, and make a comparison between ICOs and Agile ICOs. In addition, we perform a deeper analysis of Agile ICOs concerning project planning, software development, and code features. Our work shows that the roles of the people involved in an ICO can be compared to the typical roles of the SCRUM methodology. The majority of Agile ICOs use tool of testing before storing smart contract on blockchain. Finally, the application of volumetric and complexity software metrics shows that the files of Agile ICOs is on average shorter and less complex than in other smart contracts. Full article

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption. Full article