Miracle-dz/UniFAR-FTUs huggingface dataset:
A Collaborative Energy Optimization Routing Metric for IoT Network over Smart City

Internet of Things (IoT) has proven its ability to promote smartness into human's life by making physical objects more informative and adaptive to our needs. Consequently, a number of applications related to smart environments have been appeared, some examples are smart parking, smart building and smart agriculture. Furthermore, the integration of different smart environments leads to development of smart city as a hub for such ecosystems. Nonetheless, enabling end-to-end communications over ultra-large-scale network like cities poses a challenge on IoT devices due to their energy scarce resources. This paper proposes a novel routing metric designed especially for IoT networks over smart cities, this proposal enables each node to estimate the energy consumptions of each possible path and then selects that path which consumes the lowest energy. This estimation is carried-by a collaborative technique that does not impose any overhead on the network. Thereby massive saving of energy is achieved without affecting main performance metrics of the network. Simulation results along with multiple cases studies demonstrate the superlative of the proposed metric from different perspectives.
Fuzzy Logic for Evaluating Electrician Maintenance Teams at Transmission Lines Work Environment

This paper aims to show an algorithm for measuring performance of a variety of psychosocial risks factors involving the evaluation of activities and work environment of Electrician Maintenance Teams at Electricity Transmission Lines. The proposed Evaluation Complex System utilizes fuzzy logic to process the evaluation by the application of linguistic terms as variables and executing many fuzzy logic operations to classify, aggregate and treat data sources. The research was applied, as a pilot experience, in a company that operates at the Brazilian electric sector, to some teams of electricians. The results are shown to be important to redesign the human resources strategies and investments, with the objective of reducing risks to the workers and providing them with a better work environment. As a consequence, the company achieves better revenue levels by reducing accidents and disruptor events that cause penalties according to the transmission lines concession contract responsibilities rules.
Are Identifier Renaming Methods Secure?

The obfuscation methods are often used to hide sensitive information in software. Especially, an identifier renaming method (IRM) is well used because it is easy to implement. IRM is one of the obfuscation methods, and to change the names of function, and variables into meaningless one. However, if we can restore the identifiers to the original names, IRM is invalidated. One of the serious problems about IRM is that the tolerance against de-obfuscation is not discussed. This paper tries to de-obfuscate IRMed programs. Thereby, this paper evaluates the tolerance and effectiveness of IRM. From the experimental evaluation, the proposed method can restore the 49.71 % of method names to the original verbs. Furthermore, focusing on the meanings of verbs, the proposed method recommends the verbs of similar meanings to the original verbs in 57.01 % of methods.
Blockchain for Student Data Privacy and Consent

Current methods for obtaining data consent from legal guardians are limited to paper forms and human data entry. While this classic method of granting consent is familiar to the parties involved, it involves little accountability or ongoing access oversight. Parents are asked to sign multiple permission forms, but are not given an efficient manner by which they can differentiate between fields for consent nor keep track of usage. Blockchain technology represents a promising approach for developing ‘smart contracts', digital promises to be executed without reliance upon a legal intermediary. Utilizing Hyperledger Fabric and Hyperledger Composer, we propose a framework of nested authorization, allowing a public school data administrator the ability to grant authorization rights to a third party afterschool intervention program and their volunteers, who meet with parents and obtain consent for data access rights relying on smart contracts. These data consent signatures allow researchers the ability to design targeted inventions, meeting the needs of the community.
Optimal Demand Response and Real-Time Pricing by a Sequential Distributed Consensus-Based ADMM Approach

This paper proposes a novel optimization model and a novel approach to derive new demand response (DR) and real-time pricing schemes for smart grid in which renewable energy and power losses are taken into account. In our proposed optimization model, a time-varying load constraint is introduced to better capture the consumption variation of customers and hence gives our approach an adaptive feature as well as facilitates DR. Then our approach enables all generation and demand units to actively collaborate in a distributed manner to obtain the optimal electric price and their optimal power updates in real-time while achieving their best profits. To do so, the total welfare in the grid is maximized and the optimization problem is analytically solved using the alternating direction method of multipliers and consensus theory for multi-agent systems. Moreover, the power balance constraint is guaranteed in every iteration of the proposed algorithm. Next, the effects of renewable energy to conventional generation, consumer consumption, and electric price are theoretically revealed which show the essential role of renewable energy for peak load shifting. Finally, simulations on the IEEE 39-bus system are introduced to illustrate the effectiveness of the proposed approach.
On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.
Review Helpfulness Prediction with Embedding-Gated CNN

Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions. Thus, it is important for e-commerce companies to predict review helpfulness to present and recommend reviews in a more informative manner. In this work, we introduce a convolutional neural network model that is able to extract abstract features from multi-granularity representations. Inspired by the fact that different words contribute to the meaning of a sentence differently, we consider to learn word-level embedding-gates for all the representations. Furthermore, as it is common that some product domains/categories have rich user reviews, other domains not. To help domains with less sufficient data, we integrate our model into a cross-domain relationship learning framework for effectively transferring knowledge from other domains. Extensive experiments show that our model yields better performance than the existing methods.
Performance Analysis of a Generic Cooperative HARQ Algorithm Over Rayleigh Faded Channels

The relay-assisted Hybrid ARQ technique is regarded as a mean of further improving the coverage and reliability performance of future wireless networks. This paper studies the reliability, spectral efficiency and delay performance of a generic Repetition Redundancy cooperative HARQ (CHARQ) algorithm that uses both the direct source-destination (S-D) link and the source-relay-destination (S-R-D) path, and operates under delay constraint. The analysis is made using practical modulation and channel code over block-faded Rayleigh channels. Its performance is compared to the one provided by a generic HARQ algorithm using only the source-destination link. The results show that CHARQ provides smaller block-error rate (BLER) and ensures target BLER and smaller delay at lower signal-to-noise ratios (SNR) than HARQ, at the expense of smaller spectral efficiency.
Opinion-based Analysis of Structural Patterns in Online Viral Diffusion

As online information is often disseminated by various cascades, it is critical to understand the underlying mechanisms and influential factors that lead to the structural diversity in online diffusion. In this work, we examine the relations between the public opinion among information propagators and the structural patterns of online diffusion based on a Weibo dataset. To complement the existing structural measures of online diffusion cascades, we first propose six new structural patterns based on the analysis of multi-dimensional structural metrics. We then propose a set of new public opinion metrics by a hybrid sentiment analyzer to reveal the correlation between public opinion and diffusion structures. We further apply our understanding to predict the diffusion structure of diffusion events based on public opinion. The results of our experiments show the effectiveness of the proposed opinion-based analysis in the prediction of both structural virality and our proposed structural patterns.
3D Pickup Customization System

We propose 3D interactive pickup truck customization application. There are two main components in our system, parametric based cargo modeling and decal stickers and material decorations. Our workflow enables users to set the specifications of pickup cargos as needed. The system then generate the 3D model of the cargo from the specifications. Users then apply the materials and decal stickers for each section of the car. Finally, users can inspect the final decorated pickup truck in any desire viewpoint and taking images of the car. The system can also create the customization expense report for the users.
Stress Detection Through Speech Analysis

The work presented in this paper uses speech analysis to detect candidates stress during HR (human resources) screening interviews. Machine learning is used to detect stress in speech, using the mean energy, the mean intensity and Mel-Frequency Cepstral Coefficients (MFCCs) as classification features. The datasets used to train and test the classification models are the Berlin Emotional Database (EmoDB), the Keio University Japanese Emotional Speech Database (KeioESD) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The best results were obtained with Neural Networks with accuracy scores for stress detection of 97.98% (EmoDB), 95.83% (KeioESD) and 89.16% (RAVDESS).
Clothing Color Based De-Identification

Color of clothing can be easily used in the process of people identification as a non-biometric trait. This trait would be modified appropriately to preserve personal anonymity in social media or in video surveillance systems. This paper deals with a proposal of new method for clothing color de-identification in image records. The method consists of silhouette segmentation and clothing color segmentation algorithms. The de-identification is done by modifying components of HSV color space upon segmented clothing. The process of de-identification is reversible, i.e. original clothing color can be restored in case of transformation parameters that were used are known.
Demand-Side Resources for Electric Energy Management

Demand side management (DSM) is a scheduling of demand side resources (DSRs) activities such that the desired load shape change can be obtained for better electric power services. These sources are scheduled and optimized for obtaining various objectives as minimization of the cost or maximization of benefits for customers, utilities and environment with the best quality services of electric energy. The purpose of this paper is to provide basic information for the development of algorithms for optimal scheduling for the home energy management (HEM), building energy management (BEM) and distribution system energy management (DSEM) for the objectives.
Efficient Index-based Search Protocols for Encrypted Databases

It is astonishing to see more and more services built on user-oriented data, providing numerous tools to improve ones daily life. Nowadays, data collected from numerous sources is being used to monitor daily activities, i.e., monitoring patients. These innovations allow for more cost-efficient and scalable solutions. Nevertheless, these types of services can pose a threat to the privacy of individuals due to the possibility of leaking highly privacy-sensitive data. Therefore, it is essential to design such systems in a privacy-preserving manner. Inspired by a real-life project in the health-care domain, we propose to secure the data using encryption, while enabling the involved parties to run queries directly on this encrypted data. A vital component of such a system is searching for specific data entries within a large dataset. In this work, we present two cryptographic protocols that complete such a query by creating an encrypted vector in a simulation secure way. These vectors consist of a 1 for intended database entry, whereas other items would be represented as a 0. By creating index tables before the execution of the queries, it has become possible to execute a search query with high performance. As we show in our analyses, it takes less than one second to find the matching encrypted data-entry within a database with 100K records. Our proposal is generic, can be applied to several application domains, and practically compared to similar works.
Adoption of E-Government Applications for Public Health Risk Communication: Government Trust and Social Media Competence as Primary Drivers

Building upon a framework of the unified theory of acceptance and use of technology, this study explores the determinants of citizens’ intentions to use the government’s mobile application for public health risk communication. An online survey was conducted with a quota sample of 700 Korean citizens. The results from structural equation modeling suggest that social media competence and trust in government information are primary determinants of willingness to accept the new application and intention to use it. Trust in government information appeared to influence the acceptance of the application both directly and indirectly through performance expectancy and effort expectancy. More confidence in the use of social media led to higher levels of performance expectancy, effort expectancy, and facilitating conditions, all of which subsequently contributed to willingness to accept the application. The acceptance of the application further influenced intention to use the application and the likelihood of positive recommendations. The findings suggest that while developing applications that meet public expectations for informational benefits and time efficiency is important, it is also necessary for the government to build trust and improve citizens’ ability to use new tools in order for new information technology initiatives to fully benefit citizens.
DL MU-MIMO with TXOP Sharing and Suppressed Acknowledgments in IEEE 802.11ac WLANs

Transmission Opportunity (TXOP) sharing is the main Medium Access Control (MAC) layer contribution of the IEEE 802.11ac amendment. It is essentially an enhanced version of the standard TXOP mechanism initially introduced by the IEEE 802.11e amendment, specifically engineered to improve performance of the Downlink Multi-User Multiple-Input-Multiple-Output (DL MU-MIMO) technology. DL MU-MIMO transmissions with TXOP sharing are performed only by the Access Point (AP) allowing multiple frames from multiple traffic classes to be transmitted simultaneously towards multiple users in the downlink. However, in the reverse path, acknowledgments must be transmitted sequentially. The default acknowledgment scheme selected by the IEEE 802.11ac is the Block Acknowledgments (BlockACK) which is a poll-based approach. In this paper, we investigate the application of an acknowledgment-free scheme, known as No Acknowledgment (NoACK), during DL MU-MIMO transmissions in IEEE 802.11ac Wireless Local Area Networks (WLANs). Its impact on the overall achieved throughput is evaluated through an analytical study.
Distributed Convergence to Saddle-Points Over General Directed Multi-Agent Networks

In this paper, distributed saddle-points seeking problem is addressed in the scenario of general directed communication graphs, which does not require the weight-balanced condition. By utilizing the push-sum technique, a continuous-time multi-agent system is designed for computing a saddle point of a given convex-concave objective function in a distributed manner. It is proven that the proposed system is convergent to a saddle-point under a series of time-varying strongly connected communication graphs. Finally, numerical simulation results are provided to substantiate the effectiveness of the proposed system.
Space-Time Shift Keying and Constant-Envelope OFDM: A New Solution for Future Mm-Wave MIMO Multicarrier Systems

Future 5G systems are envisioned to support wide range of applications, from narrowband to broadband, by occupying non contiguous spectrum bands from lower sub-6GHz bands to higher millimeter wave (mmWave) bands. In this regard, significant challenges in design and implementation of 5G radio have to be addressed both in uplink and downlink. One of the challenges is related to the consideration of enabling physical layer (PHY) in the deployment of robust transmission systems able at striking an efficient tradeoff between power and spectral resources through the use of advanced MIMO-multicarrier techniques. In this paper, we address such challenge by proposing a novel technique based on space-time shift keying (STSK)-aided constant envelope orthogonal frequency division multiplexing (CE-OFDM) over frequency-selective mmWave channels. The main idea is to blend the benefits of STSK and CE-OFDM in mmWave transmissions by keeping complexity of the system as lowest as possible. We show that the STSK-CE-OFDM outperforms the state-of-the-art STSK-OFDM comprehensively in the presence of nonideal power amplifier. In particular, STSK-CE-OFDM can achieve a very large performance gain over the STSK-OFDM counterpart in the presence of nonlinear amplification. For this reason, STSK-CE-OFDM can strike a favorable and flexible tradeoff between power and spectral efficiency in the framework of 5G mmWave wireless communications. The price to be paid is an overall bandwidth increase with respect to conventional STSK-OFDM, due to the need of modulation indexes higher than 1.0 radians.
Multi-Component FBG-Based Force Sensing Systems by Comparison With Other Sensing Technologies: A Review

A multi-component force sensing system, capable of monitoring multiple components of force terms along x-, y-, and z-axis (F<sub><italic>x</italic></sub>, F<sub><italic>y</italic></sub>, and F<sub><italic>z</italic></sub>) and the moments terms about x-, y-, and z-axis (M<sub><italic>x</italic></sub>, M<sub><italic>y</italic></sub>, and M<sub><italic>z</italic></sub>) simultaneously, has been utilized in a huge variety of automation systems since 1970s. Fiber Bragg grating (FBG)-based sensing systems offer a significant viable with numerous competitive advantages over traditional force sensing systems such as immunity to electromagnetic interference, high sensitivity, larger sensing range, light weight, small size, intrinsically safe in the explosive environments, and multiplexing capabilities. Recently, a number of applications, such as robotic manipulation and robot-assisted surgery, have benefited from the developments in FBG-based force sensing systems. This paper presents a comprehensive review of different force transduction principles, state-of-the-art designs, and development methods, as well as their significances and limitations. Meanwhile, some of the significant developments in an FBG-based force sensing technology during the last few decades are surveyed, and current challenges in implementing the FBG-based force sensing technology is highlighted.
Single-Chip Design for Intelligent Surveillance System

During recent years, a digital surveillance system has gained more popularity. Object recognition plays a critical role in an intelligent surveillance system. It mainly consists of several vision-based techniques such as segmentation and tracking. However, these techniques are still challenging. In this paper, an intelligent surveillance system has been proposed on the basis of single-chip design. An algorithm development and its hardware implementation are well addressed. Our algorithm accomplishes tracking of moving people through successive frames without using boundary box of object or color cues. Although there is occlusion due to gathering of people and other foreground objects, still the proposed algorithm can deal with such situations. The developed hardware architecture involves several tasks including foreground detection, sliced connected component labeling, object grouping, and object tracking. We use parallel processing on object labeling architecture and give the upper and lower image label values simultaneously. Object grouping and object tracking are also manipulated to improve the performance. The whole system is designed using single-chip solution. It is implemented by TSMC 90-nm library with 18.71-K logic gates, 92.288-kB on-chip memory, and 11.4037-mW low power consumption.
Bi-Level Two-Stage Robust Optimal Scheduling for AC/DC Hybrid Multi-Microgrids

In view of the plurality of ac and dc microgrids connected to the power grid, this paper proposes a bi-level two-stage robust optimal scheduling model for ac/dc hybrid multi-microgrids (HMMs). In this model, the ac/dc HMM system is divided into two interest bodies, called the utility level and supply level, and two-stage robust (also called adaptive robust) optimal scheduling is carried out considering the uncertainties in each level. For the mutual interaction between the two bodies, power constraints and deviation penalties of the interaction lines are introduced in the model to realize bi-level coordinated scheduling and obtain a unified robust dispatch scheme of ac/dc HMMs. The column-and-constraint generation algorithm is used to convert the Min-Max-Min problem of each level into a two-stage mixed-integer linear programming problem, which can be solved quickly and effectively. Case studies verify the rationality and validity of the proposed model and the solving procedure.