Reviewerly/exHarmony huggingface dataset:
Aggregation-based location privacy: An information theoretic approach

We explore the problem of quantifying and protecting aggregation-based location privacy and study the privacy-utility tradeoff, which are essential to protect user's location privacy when releasing aggregate statistics. Existing works on Aggregation-based Location Privacy Protection Mechanisms (ALPPMs) are mainly based on differential privacy, and metrics for evaluating information leakage introduced by releasing aggregates are normally built on adversary's estimation error. However, there lacks privacy metrics for measuring the fundamental leakage on individual user's data that is independent of specific data instances or attack algorithms. In this paper, we aim to solve this problem using an information-theoretic approach. We first propose a privacy metric based on the mutual information between the individual user's location profile and the released location aggregates, and formulate the optimal location aggregate release problem that minimizes the mutual information given a utility constraint for each user. Since solving this optimization problem causes exponential complexity, we turn to prove and evaluate an upper bound, i.e., the mutual information between the original and the perturbed location aggregates, and propose a Blahut-Arimoto based algorithm to solve the optimization problem minimizing the mutual information to derive an ALPPM. We validate the actual leakage of our ALPPM and compare it to a differentially private mechanism through experiments over both synthetic and real-world location datasets. Results show the advantage of the proposed ALPPM in terms of privacy-utility tradeoff, which is enhanced when users' location prior distributions are more skewed.
Sequence Learning for Images Recognition in Videos with Differential Neural Networks

Sequence learning from real-time videos is one of the hard challenges to current machine learning technologies and classic neural networks. Since existing supervised learning technologies are heavily dependent on intensive data and prior training, new methodologies for learning temporal sequences by unsupervised learning theories and technologies are yet to be developed. This paper presents the design and implementation of a novel Differential Neural Network (∇NN) for unsupervised sequence learning. The methodology is developed with a set of fundamental theories and enabling technologies for solving the problems of visual object recognition, motion detection, and visual semantic analysis in video sequence. A set of experiments on ∇NN for sequence learning is demonstrated. This work has not only led to a theoretical breakthrough to novel machine sequence learning, but also applicable to a wide range of challenging problems in computational intelligence and the AI industry.
Strong security starts with software development

While there is – rightly – a big focus on securing software that is already deployed, the reality is that many future vulnerabilities stem from the creation of that software. Insecure applications give hackers a back door. For instance, buffer overflows and code injection attacks can lead to compromised confidentiality of data, loss of service, damage to the systems of thousands of users, even – in the case of products containing embedded software, such as medical equipment or vehicles – risk to life. While we focus on securing software that is already deployed, the reality is that many future vulnerabilities stem from the creation of that software. Securing development is a tough challenge due to the increasing complexity of software, the volume of code, multiple contributors, distributed teams and the pressure to deliver to tight deadlines. Plus, developers traditionally have not been focused on security. That is changing with the emergence of DevSecOps, which focuses on implementing software security practices and tools at every stage of the lifecycle, explains Rod Cope of Perforce Software.
Power feedback strategy based on efficiency trajectory analysis for HCPV sun tracking

This paper presents a control strategy for sun trackers which adapts continuously to different sources of error, avoiding the necessity of any kind of calibration by analyzing the produced electric power to sense the position of the Sun. The proposed strategy is able to meet the strict specifications for HCPV sun trackers despite of mechanical uncertainties (misalignments in the structure itself, misalignment of the solar modules with respect to the wing, etc.) and installation uncertainties (misalignments of the platform with respect to geographical north). Experimental results with an industrial-grade solar tracker showing the validity of the proposed control strategy under sunny and moderate cloudy conditions, as well as with different installation precisions by un-calibrating the system on purpose are exposed.
Tutorial on Task-Based Search and Assistance

While great strides are made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users. Specifically, we lack intelligent systems that can detect not only the request an individual is making (what), but also understand and utilize the intention (why) and strategies (how) while providing information. Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better. However, we are still struggling to support them in task completion, e.g., in search and assistance, it has been challenging to move beyond single-query or single-turn interactions. The proliferation of intelligent agents has opened up new modalities for interacting with information, but these agents will need to be able to work more intelligently in understanding the context and helping the users at task level. This tutorial will introduce the attendees to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching). Specifically, it will cover several recent theories, models, and methods that show how to represent tasks and use behavioral data to extract task information. It will then show how this knowledge or model could contribute to addressing emerging retrieval and recommendation problems.
Application Report Process Of Islamic School Based On Pesantren Boarding Using Waterfall Model

Abstract This research aims to develop information system applications for processing report evaluation of Islamic junior high schools based on boarding pesantren. Islamic junior high school based of boarding pesantren has different evaluation methods from junior high schools in general. Because Islamic junior high school based on boarding pesantren not only assess students academic abilities but also assess students religious abilities. This research uses waterfall method to build and develop software applications report process. There are 5 stages to be implemented such as communication, planning, modelling, construction, and development. In addition, it also uses Data Flow Diagrams to model the data flow that occurs in the system. The results is software applications that can combining report card from school with boarding pesantren.
A software‐defined radio testbed for deep learning‐based automatic modulation classification

Summary Automatic modulation classification (AMC) is the demodulation process on the receiver side, which is a crucial protocol for current and next‐generation intelligent communication systems. This method becomes complicated, in the presence of channel noise, to identify the modulation of the transmitted signal, that is, the transmitter and receiver with its ambiguous parameters like timing information, signal strength, phase offset, and carrier frequency. Two fundamental approaches are used for the AMC, namely, the signal statistical feature‐based approach and the maximum likelihood approach. Current Feature‐Based AMC approaches typically built for a limited set of modulation; a comprehensive AMC approach utilizing convolutional neural networks (CNN) is suggested in this article to overcome this obstacle. Altogether, 11 different types of modulations considered. In this method, without an extraction function, the transmitted signal can be identified directly. Also, the features of the received signal are known directly by using this method. The classification accuracy using CNN seems to be remarkable, especially for low SNRs. In this article, a realistic AMC framework that can be quickly applied to provide reliable efficiency in numerous commercial real‐time scenarios has developed and tested. Therefore, to prove the functional viability of our proposed model, it was applied to the software‐defined radio test‐bed.
An analysis of malaria in the Brazilian Legal Amazon using divergent association rules

In data analysis, the mining of frequent patterns plays an important role in the discovery of associations and correlations between data. During this process, it is common to produce thousands of association rules (ARs), making the study of each one arduous. This problem weakens the process of finding useful information. There is a scientific effort to develop approaches capable of filtering interesting patterns, balancing the number of ARs produced with the goal of not being trivial and known by specialists. However, even when such approaches are adopted, the number of produced ARs can still be high. This work contributes by presenting Divergent Association Rules Approach (DARA), a novel approach for obtaining ARs that presents themselves in divergence with the data distribution. DARA is applied right after traditional approaches to filtering interesting patterns. To validate our approach, we studied the dataset related to the occurrence of malaria in the Brazilian Legal Amazon. The discovered patterns highlight that ARs brought relevant insights from the data. This article contributes both in the medical and computer science fields since this novel computational approach enabled new findings regarding malaria in Brazil.
The Fuzzy Logic Programming language FASILL: Design and implementation

The FASILL programming language (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") combines a weak unification algorithm, based on similarity relations, along with a rich repertoire of fuzzy connectives and aggregators, whose truth functions can be defined on a complete lattice. In this work, we want to provide a unified view of the fundamental concepts and ideas that inspired its construction, jointly with the implementation techniques that made it possible. We detail the implementation of the operational semantics and we describe the overall structure of the FASILL system. The FASILL language is well-suited for a wide range of applications and, specifically, it can help the development of knowledge based systems where to deal with uncertainty is important. After ten years designing and implementing fuzzy logic systems, this work culminates and agglutinates the experience acquired in our research group on the development of this kind of modern programming languages.
Chasing play potentials in food culture

In this one-day workshop, we will explore how food related culture and traditions can guide the design of playful technologies and experiences. Using food as an accessible starting point, we aim to bring together a diverse set of participants in order to share and make creative use of playful traditions and food stuffs through hands-on prototyping, play and discussion. At the end of the day we expect to further advance our methodological inquiry with insights on how children's natural affinity to play can be leveraged in co-design explorations aimed at chasing play potentials in foods and food related practices as well as expand the repository of play-food potentials we have been curating for the past months. Overall the workshop will contribute to enriching the set of tools available for designers interested in play and technologies for everyday use, in and beyond the food domain.
Using susceptibility claims to motivate behaviour change in IT security

Organisations face growing IT security risks with substantial consequences for missteps in business continuity, data loss, reputational harm, and future competitive advantage. To improve precaution-taking among organisation members, leaders frequently turn to susceptibility claims embedded in security education, training, and awareness (SETA) initiatives to motivate change. However, prior studies have produced mixed empirical results concerning the role of susceptibility in motivating precaution-taking. To deepen theorising about using susceptibility claims to change behaviour, we argue that threat characteristics (overt versus furtive attacks) shape individuals’ attitudes of the threat, and these attitudes subsequently anchor how individuals respond to new claims about the threats. We introduce social judgement theory (SJT) to argue that when individuals participate in SETA initiatives, susceptibility claims that are too distant from individuals’ existing attitudes will be ignored, while claims that are more proximal are more likely to be accepted and result in behaviour change. Using a longitudinal field experiment, we found that susceptibility claims motivated precaution taking against phishing (overt attack) but did not against password cracking (furtive attack). These results support SJT predictions and imply latitudes of acceptability and rejection into which susceptibility claims are placed. Implications for researchers, organisation leaders, and SETA developers are discussed.
Rationale and Practical Assessment of a Fully Distributed Blockchain-based Marketplace of Fog/Edge Computing Resources

On modern electronic marketplaces, the buyers and sellers (i.e., the users) trade products over software platforms provided by trusted third-party entities. In exchange for better user experience, the users allow these intermediaries to set their own arbitrary rules, which may influence reciprocal interactions and also favor some products over others. Recently, researchers have shown an increased interest in blockchain technology as a possible solution for creating decentralized marketplaces, thus avoiding the need for intermediaries. However, due to the young age of this technology, the majority of architectures proposed in the literature still rely on other types of centralized services, mainly to guarantee better user experiences. Although these schemes offer several benefits over traditional centralized architectures, they introduce other issues, such as single points of failure for the whole system. This paper presents a software architecture and the implementation of a fully decentralized marketplace solely relying on a public blockchain network. To test its effectiveness, we implemented a full-fledged marketplace of Fog/Edge computing resources, evaluating several design decisions and trade-offs in terms of monetary cost versus performance. Our experiments show that the current capabilities of blockchain technology may support such fully decentralized marketplaces, providing seamless user experiences.
Extracting User-Centric Knowledge on Two Different Spaces: Concepts and Records

The growing demand for eliciting useful knowledge from data calls for techniques that can discover insights (in the form of patterns) that users need. Methodologies for describing intrinsic and relevant properties of data through the extraction of useful patterns, however, work on fixed input data, and the data representation, therefore, constrains the discovered insights. In this regard, this paper aims at providing foundations to make the descriptive knowledge that is extracted by pattern mining more user-centric by relying on flexible data structures defined on two different perspectives: concepts and data records. In this sense, items in data can be grouped into abstract terms through subjective hierarchies of concepts, whereas data records can also be organized based on the users' subjective perspective. A series of easy-to-follow toy examples are considered for each of the two perspectives to demonstrate the usefulness and necessity of the proposed foundations in pattern mining. Finally, aiming at experimentally testing whether classical pattern mining algorithms can be adapted to such flexible data structures, the experimental analysis comprises different methodologies, including exhaustive search, random search, and evolutionary approaches. All these approaches are based on well-known and widely recognized techniques to demonstrate the usefulness of the provided foundations for future research works and more efficient and specifically designed algorithms. Obtained insights demonstrate the importance of working with subjectivity: an item is a type of soda but belongs to a pack, including two or more soda types.
Secure Non-Public Health Enterprise Networks

Increasing demand for secure remote operation in industry and technology advancements to support delivering efficient services and tele-mentoring have opened a new market in healthcare sector and emergency services based on 5G and Tactile internet capabilities.In a connected world, hospitals would benefit from providing the on-time availability either for continuous health monitoring or critical services to the citizens in need.In this paper, we propose a secure non-public health enterprise network concept to enable an end-to-end secure and location-agnostic communication between a patient and a healthcare service provider, and other contacts with patient's consent either in case of an emergency or to be stored in the medical records.We present how applying non-public enterprise networks can address market demand in health care sector for improved end-to-end security and privacy when dealing with personal and critical information.We present the three-tier architecture model describing continuous authentication mechanisms based on biometric collection as well as the dynamic network solutions in the healthcare domain.The biometric collection can be done using ambient/IoT sensors as well as wearable/implantable devices to monitor the patient unobtrusively.Furthermore, end-to-end security solutions should adapt dynamically based on the user profile and situation awareness to address the required level of security at the network side.We discuss the related research challenges for developing the presented non-public health enterprise platform and provide suggestions for future work based on the healthcare sector requirements and opportunities.
Co-designing a conversational interactive exhibit for children

In this paper, we describe and reflect on the process of co-designing an Artificial Intelligence (AI) exhibition aimed to teach children basic AI concepts in the Catavento Science Museum in São Paulo, Brazil. We focus on two of the co-design process with the museum staff: one which sought to design the flow of the experience and get the sense of the target audience; and another which intended to provide content for the AI exhibition. We describe the activities and show how they assisted in the design process and opened possibilities for the design team to develop an exciting experience, tailored to children, promoting informal learning of Artificial Intelligence concepts.
Blockchain-Based Solution to Prevent Plastic Pipes Fraud

Use of counterfeit plastic pipes has caused considerable financial damage to the states and companies. This article proposes a blockchain-based supply chain management system for its market. It can make pipes' use and life cycles transparent, thus guarantee the invariability of pipes mass produced and used. The article provides a high-level technical description, containing ERP-blockchain connection and application for project-specific labels printing, mobile applications for manufactures and pipes installers.
Researchers' toolbox for the future

Within Child Computer Interaction (CCI), there is an emphasis on empowering children through co-design and co-creation of technology that shapes their learning, inside and outside the classroom, their wellbeing and lifestyles, and everyday experiences. However, the focus has primarily been on now or the near future, with limited work on designing children's future technological life worlds.
Organizational Rules and Cognitive Uncertainty Among Public Professionals: A Daily Diary Study

Although public management and human resource management research has extensively investigated the motivational effects of organizational rules, the original utility of organizational rules—uncertainty reduction—has remained overlooked. This study takes a cognitive perspective by examining how organizational rules relate to uncertainty experiences of public professionals. In this study, we provide a dynamic perspective on the relationship between organizational rules and uncertainty through a 2-week daily online diary study among 65 public professionals in the Netherlands. The results indicate that the amount and consistency of rules are related to professionals’ daily uncertainty experiences. Moreover, within-person experiences of rules and uncertainty are highly variable over time. We argue that a cognitive perspective of uncertainty reduction can broaden our understanding of the consequences of organizational rules in managing people, and that the dynamic nature of organizational rule experiences cannot be a mere footnote in future public administration and human resource management research.
A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

Social learning particle swarm optimization (SL-PSO) allows individuals to learn from others to improve the scalability with easy parameter settings. However, it still suffers from the poor convergence for those multi-modal problems due to the loss of swarm diversity. To improve both the diversity and the convergence, this paper proposes a novel algorithm to apply the mechanism of molecular interactions to SL-PSO, in which the molecular attraction aims to improve the convergence, and the molecular repulsion intends to enhance the diversity. In the experiments, we compare our algorithm with the SL-PSO algorithm and other representative PSO and evolutionary algorithms on 49 benchmark functions. The results show the performance of the proposed algorithm is better than that of the SL-PSO algorithm and other representative PSO and evolutionary algorithms on average. This work builds the solid foundation for the integration of the molecular interaction mechanism with PSO and other optimization algorithms.
Sign language handshape recognition using Myo Armband

In this work, we propose a new approach for recognizing sign language hand shapes using electromyogram (EMG) signals. The processed signal is obtained from the Myo armband witch uses eight sensors. The new approach is based on MFCC and DTW methods and the K-NN classifier. We also present the experiments carried out for the validation of the proposed approach as well as the obtained results.