Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Multi-Party Access Control

Multi-party access control has been proposed to enable collaborative decision making for the protection of co-owned resources. In particular, multi-party access control aims to reconcile conflicts arising from the evaluation of policies authored by different stakeholders for jointly-managed resources, thus determining whether access to those resources should be granted or not. While providing effective solutions for the protection of co-owned resources, existing approaches do not address the protection of policies themselves, whose disclosure can leak sensitive information about, e.g., the relationships of co-owners with other parties. In this paper, we propose a privacy-preserving multi-party access control mechanism, which preserves the confidentiality of user policies. In particular, we propose secure computation protocols for the evaluation of multi-party policies, based on two privacy-preserving techniques, namely homomorphic encryption and secure function evaluation. An experimental evaluation of our approach shows its practical feasibility in terms of both computation and communication costs.
Handling missing values for mining gradual patterns from NoSQL graph databases

Graph databases (NoSQL oriented graph databases) provide the ability to manage highly connected data and complex database queries along with the native graph-storage and processing. A property graph in a NoSQL graph engine is a labeled directed graph composed of nodes connected through edges with a set of attributes or properties in the form of (key:value) pairs. It facilitates to represent the data and knowledge that are in form of graphs. Practical applications of graph database systems have been seen in social networks, recommendation systems, fraud detection, and data journalism, as in the case for panama papers. Often, we face the issue of missing data in such kind of systems. In particular, these semi-structured NoSQL databases lead to a situation where some attributes (properties) are filled-in while other ones are not available, either because they exist but are missing (for instance the age of a person that is unknown) or because they are not applicable for a particular case (for instance the year of military service for a girl in countries where it is mandatory only for boys). Therefore, some keys can be provided for some nodes and not for other ones. In such a scenario, when we want to extract knowledge from these new generation database systems, we face the problem of missing data that arises need for analyzing them. Some approaches have been proposed to replace missing values so as to be able to apply data mining techniques. However, we argue that it is not relevant to consider such approaches because they may introduce biases or errors. In our work, we focus on the extraction of gradual patterns from property graphs that provide end-users with tools for mining correlations in the data when there exist missing values. Our approach requires first to define gradual patterns in the context of NoSQL property graph and then to extend existing algorithms so as to treat the missing values, because anti-monotonicity of the support cannot be considered anymore in a simple manner. Thus, we introduce a novel approach for mining gradual patterns in the presence of missing values and we test it on real and synthetic data.
Reading Digital Numbers of Water Meter with Deep Learning Based Object Detector

Automatically reading water meter is a classical OCR problem, typical method includes four major components: region of interests (ROIs) detection, skew correction of bounding boxes, single digital character segmentation, and digital classification. Disadvantage of the traditional method is that the pipeline is too complex and coupled to the accuracy of the final recognition result. Deep learning based object detection has achieved promising results on many computer vision tasks. As one of the representatives of the deep learning object detection framework, YOLOv3 perform detection task quickly and accurately. Inspired by this, we formulate the water meter reading problem as a detection problem, which is a true end-to-end solution. In order to attack the half-character problem of water meter, we proposed a heuristic rule to guarantee that there is only one bounding box in the vertical direction within a grid. Experimental results on our own built XMU-W-M dataset showed that the 0-error recognition rate reaches 96.67% and the 1-error recognition rate is up to 99.81%, which outperforms the traditional water meter recognition system in both time and precision. Both the code and dataset are available: https://github.com/sloan96/water-meter-recognition .
Solving the Individual Control Strategy Tasks Using the Optimal Complexity Models Built on the Class of Similar Objects

The conventional approach for calculating individual optimal strategies assumes that the best control actions are determined for the same object that has been studied by monitoring or conducting active trials. However, the class of objects for which is impossible to organize repeated tests is widespread. An example is patients with a particular disease, for each of which it is impossible to organize separate trials to study possible strategies for its cure. This paper proposes an approach to formulate the individual strategies optimization task that uses observational data obtained during the monitoring or active experiment on a sample of similar objects. It is proposed to obtain the state models of the optimal complexity object that are nonlinear in the parameters and initial conditions of the object and linear in control actions, to construct an effective calculation technology. As a modeling tool, algorithms of Group Method of Data Handling (GMDH) are used. The optimization task of individual strategies is formed after substituting the individual values of object parameters in the model of functional and models of constraints. The final calculation procedure takes the form of a linear programming problem. Limitations of the approach and an example of calculating the individual strategy are considered.
Interactive Machine Learning for the Internet of Things

The advances in Internet of Things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for Activity Recognition by using Machine Learning. However, as the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures, the feature space might also change over time. Moreover, the labelled data necessary for the training is often costly to acquire. Active Learning is a type of Interactive Machine Learning where the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data points to label. It is generally assumed that a query always gets a correct response, but in many real-world scenarios this is not a realistic assumption. In this work we investigate different Proactive Learning strategies, which explore the human factors of the oracle and aspects that might influence a user to provide or withhold labels. We implemented four proactive strategies and hybrid versions of them. They were evaluated on two datasets to examine how a more proactive, or reluctant, user affects performance. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.
Application of Artificial Intelligence Technology in Short-range Logistics Drones

With the increasing demand for logistics in China, the shortcomings of the current short-range logistics field are inefficient, and the occupation of labor is becoming more and more obvious. The proportion of the personnel engaged in production and the transportation personnel who ensure the flow of materials continues to increase, and the production efficiency of the total social labor force continues to decline. In this paper, combined with artificial intelligence related technology, a special drone design scheme is proposed instead of short-distance logistics distributors, aiming at adjusting short-range logistics links, improving logistics efficiency, and optimizing related logistics processes to reduce the need of number of workers to ensure the normal transportation of logistics.
Do You Really Follow Them? Automatic Detection of Credulous Twitter Users

Online Social Media represent a pervasive source of information able to reach a huge audience. Sadly, recent studies show how online social bots (automated, often malicious accounts, populating social networks and mimicking genuine users) are able to amplify the dissemination of (fake) information by orders of magnitude. Using Twitter as a benchmark, in this work we focus on what we define credulous users, i.e., human-operated accounts with a high percentage of bots among their followings. Being more exposed to the harmful activities of social bots, credulous users may run the risk of being more influenced than other users; even worse, although unknowingly, they could become spreaders of misleading information (e.g., by retweeting bots). We design and develop a supervised classifier to automatically recognize credulous users. The best tested configuration achieves an accuracy of 93.27% and AUC-ROC of 0.93, thus leading to positive and encouraging results.
Unconstrained texture classification using efficient jet texton learning

This paper proposes a simple and effective texture recognition method that uses a new class of jet texton learning. In this approach, first a Jet space representation of the image is derived from a set of derivative of Gaussian (DtGs) filter responses upto 2nd order (R6), so called local jet vector (Ljv), which satisfies the scale space properties, where the combinations of local jets preserve the intrinsic local structure of the image in a hierarchical way and are invariant to image translation, rotation and scaling. Next, the jet textons dictionary is learned using K-means clustering algorithm from DtGs responses, followed by a contrast Weber law normalization pre-processing step. Finally, the feature distribution of jet texton is considered as a model which is utilized to classify texture using a non-parametric nearest regularized subspace (Nrs) classifier. Extensive experiments on three large and well-known benchmark database for texture classification like KTH-TIPS, Brodatz and CUReT show that the proposed method achieves state-of-the-art performance, especially when the number of available training samples is limited. The source code of complete system is made publicly available at https://github.com/swalpa/JetTexton.
Spatial Relational Reasoning in Networks for Improving Semantic Segmentation of Aerial Images

Most current semantic segmentation approaches rely on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields causes failures in modeling contextual spatial relations. Prior works have tried to address this issue by using graphical models or spatial propagation modules in networks. But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions. In this work, we introduce a simple yet effective network unit, the spatial relation module, to learn and reason about global relationships between any two spatial positions, and then produce relation-enhanced feature representations. The spatial relation module is general and extensible, and can be used in a plug-and-play fashion with the existing fully convolutional network (FCN) framework. We evaluate spatial relation module-equipped networks on semantic segmentation tasks using two aerial image datasets. The networks achieve very competitive results, bringing significant improvements over baselines.
The design space of media architecture displays

Interaction design is increasingly about embedding interactive technologies in our built environment; architecture is increasingly about the use of interactive technologies to reimagine and dynamically repurpose our built environment. This forum focuses on this intersection of interaction and architecture. --- Mikael Wiberg, Editor
Real-Time Multi-Pedestrian Detection in Surveillance Camera using FPGA

In surveillance cameras, pedestrians and objects are detected using Convolutional Neural Network (CNN) based Object Detection such as YOLO and SSD. Since the size of the CNN input image is fixed, small objects cannot be detected when the high-resolution image is resized. By splitting image and applying object detection to each split image, CNN can detect small objects and take advantage of the high performance of the FPGA. We demonstrate an object detector by splitting the given image as stream data to YOLOv2 on an FPGA which has a very high performance. It achieved both practical speed and accuracy. Due to the difference in scale between training data and test data, the detection of small objects fails when the granularity of split is small. Splitting the image matches the scale of the data. When detection throughput is high enough, it detects many objects with a practical speed. We demonstrate the comparison of FPGA and mobile GPU in the proposed image split method of object detection. We implement Object Detection Systems by Xilinx ZCU104 FPGA board and NVIDIA Mobile GPU boards with a USB camera. The image split method can be adapted as it is to all implementations of object detection. We compare the performance of the FPGA and GPU. As a result of the experiment, FPGA achieves 547.0 FPS per image and is 3.9 times faster than Mobile GPU. When the given image is split into 4 × 4 grids, the system realizes 34.2 FPS and it satisfies the real-time requirement on the standard camera(30FPS). We showed that ultra-fast object detection can be used to improve accuracy.
2nd Workshop on Knowledge-aware and Conversational Recommender Systems - KaRS

Over the last years, we have been witnessing the advent of more and more precise and powerful recommendation algorithms and techniques able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest of the recommendation engine. Although very effective in in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move into the direction of providing novel and diverse results as well as generating an explanation for the recommended items or support interactive and conversational recommendation processes.
AttentionRNN: A Structured Spatial Attention Mechanism

Visual attention mechanisms have proven to be integrally important constituent components of many modern deep neural architectures. They provide an efficient and effective way to utilize visual information selectively, which has shown to be especially valuable in multi-modal learning tasks. However, all prior attention frameworks lack the ability to explicitly model structural dependencies among attention variables, making it difficult to predict consistent attention masks. In this paper we develop a novel structured spatial attention mechanism which is end-to-end trainable and can be integrated with any feed-forward convolutional neural network. This proposed AttentionRNN layer explicitly enforces structure over the spatial attention variables by sequentially predicting attention values in the spatial mask in a bi-directional raster-scan and inverse raster-scan order. As a result, each attention value depends not only on local image or contextual information, but also on the previously predicted attention values. Our experiments show consistent quantitative and qualitative improvements on a variety of recognition tasks and datasets; including image categorization, question answering and image generation.
The Impact of IT Bootcamp on Student Learning - Experience from ICT Enabled Experiential-Based Course

We have been teaching an experiential-based course for fist year master students. In the last two years, we have added to the course external activities such as Hackathon and Bootcamp. These external activities helped students internalize how important are soft skills and involvement of external stakeholders to succeed in developing relevant startup projects. This year, we wanted to evaluate if students were getting what we declared. We conducted a survey on students' perception of different dimensions: soft skills challenges (teamwork, communication with stakeholders, presentation, negotiation, and innovation), technical challenges and project management (PM) challenges, before and at the end of the Bootcamp days. We found out that the mean values regarding soft skill challenges and project management vary, while values regarding technical challenge have not changed before and after Bootcamp. The overall outcomes of the study contribute to conceptualizing an early model integrating student startup formation with course learning outcomes.
Predicted direct solar radiation (ECMWF) for optimized operational strategies of linear focus parabolic-trough systems

Day-ahead forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS), the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF), are used to simulate a concentrating solar power (CSP) plant through the System Advisor Model (SAM) to assess the potential value of the IFS in the electricity market. Although DNI forecasting from the IFS still demands advances towards cloud and aerosol representation, present results show substantial improvements with the new operational radiative scheme ecRad (cycle 43R3). A relative difference of approximately 0.12% for the total annual energy availability is found between forecasts and local measurements, while approximately 10.6% is obtained for the previous version. Results of electric energy injection to the grid from a simulated linear focus parabolic-trough system shows correlations coefficients of approximately 0.87 between hourly values of electric energy based on forecasted and measured DNI, while 0.92 are obtained for the daily values. In the context of control strategy, four operational strategies are given for different weather scenarios to handle the energy management of a CSP plant, including the effect of thermal energy storage capacity. Charge and discharge operational strategies are applied accordingly to the predicted energy availability.
MODELLING THE EFFICIENCY OF THE CLOUD COMPUTING IMPLEMENTATION AT ENTERPRISES

The article describes the main characteristics, types and properties of cloud computing. The most widespread cloud technologies in Ukraine are analyzed. It is identified that the largest share among users of cloud technologies in Ukraine currently belong to large holdings, IT companies, commercial enterprises and banks, but other sectors of business are also involved in the development of these services. The aim of the article is to develop the methodology for evaluating the efficiency of cloud technologies implementation at enterprises and its experimental verification. The economic component of the cloud computing implementation at enterprises (expenditures and revenues of both cloud technology owners and users) is considered. The efficiency of using cloud computing at enterprises is proved. It is found that organizations usually do not use the power of their personal data centers to a full extent. This leads to idle equipment, extra cost on maintenance and servicing of hardware, amortization, staff salaries and etc. The feasibility of transition of enterprises to cloud computing in such situations has been proved, which considerably reduce the costs of the enterprise due to the absence of need for hardware and necessary staff to support the operation of information systems. Usability of the methodology of total cost of ownership in evaluating the effectiveness of using services for the enterprise has been proved. The proposed methodology compares the main costs of using personal data centers and the cost of using cloud computing. It is experimentally proven that in most cases, the cost of maintaining personal data center (PDC) is higher than the cost of cloud services. It is also proved that the efficiency of cloud technology operation depends on the internal structure and organization of computing processes inside the systems, as well as on external factors such as the size of enterprises-clients, industries, costs for the organization of data centers, etc. Cloud computing is an advanced technology which has future prospects and is cost-effective for both enterprise users and provider organizations.
NoteSum: An integrated note summarization system by using text mining algorithms

This study implemented an integrated system of Note Summarization (NoteSum) that merged with multi-users’ notes and searched for relevant information on the Internet and, slides, and textbooks to create a summary for students to learn effectively. The integrated system's framework consists of four different modules: Topic Identification Module, Supporting Material Finding Module, Content Mapping Module and Learning Material Integrating Module. Five experiments were conducted; these resulted in the following findings. First, translating notes with the assistance of topic terms could enhance translation quality. Second, when mapping contents, NoteSum performed better in a discussion-based course rather than in a technical course. Third, the Jensen-Shannon (JS) Divergence was used to assess the generated summary that performed better for the discussion-based course. Fourth, the three attributes—presence of topic terms, number of non-topic words, and ratio of the words with important parts of speech—had different effects on different subjects. Finally, we compared NoteSum with other existing summarization systems. The results indicated that the NoteSum-generated summary was closer to students’ original notes and thus resulted in better performance in readability, informativeness, and completeness. All the results confirm that our proposed NoteSum is an effective note summarization system for student learning.