Reviewerly/exHarmony huggingface dataset:
Digital Signature: Quantum Chaos Approach and Bell States

The quantum mechanics applied by quantum digital signatures (QDSs) is applied to guarantee the nonrepudiation, unforgeability, and transferability of a signature. Previously, the security of QDSs schemes only depended on the length of the signature. Also, they required unreliable security channels and have been written for one-bit messages. In this paper, quantum digital signature schemes based on bell states sequence and synchronization of coupled chaotic map is introduced. In our schemes, using the synchronization of coupled chaotic map when signing up, security increases against repudiation and forgery. Furthermore, to sign a regular message, repeating the signature steps is half as much as the previous ones.
Log Analysis of 360-degree Video Users via MQTT

Analyzing 360-degree video users is beneficial for 360-degree video application development. The analysis can be done with logged user data. In this paper, we argue that MQTT is a conventional technology for distributed logging of mobile 360-degree video users. MQTT not only saves resources also allows communication from the logging server to mobile clients in various networking conditions relatively easy. We constructed a proof of concept to show the feasibility of the approach. As log analysis examples, the proof of concept visualizes results of the most popular region of interest analysis and k-means clustering. The used research method is design science.
You Can't See Me: Anonymizing Graphs Using the Szemerédi Regularity Lemma

Complex networks gathered from our online interactions provide a rich source of information that can be used to try to model and predict our behaviour. While this has very tangible benefits we all grew accustomed to, there is a concrete privacy risk in sharing potentially sensitive data about ourselves and the people we interact with, especially when this data is publicly available online and unprotected from malicious attacks. k-anonymity is a technique aimed at reducing this risk by obfuscating the topological information of a graph that can be used to infer the nodes' identity. In this paper we propose a novel algorithm to enforce k-anonymity based on a well-known result in extremal graph theory, the Szemeredi regularity lemma. Given a graph, we start by computing a regular partition of its nodes. The Szemeredi regularity lemma ensures that such a partition exists and that the edges between the sets of nodes behave almost randomly. With this partition to hand, we anonymise the graph by randomising the edges within each set, obtaining a graph that is structurally similar to the original one yet the nodes within each set are structurally indistinguishable. We test the proposed approach on real-world networks extracted from Facebook. Our experimental results show that the proposed approach is able to anonymise a graph while retaining most of its structural information.
Designing a Video Course. The Case of the Online Course of Mathematical Olympiads

Current secondary and university students use their technological devices not only in playtime but also in study time. Our proposal makes use of this fact to create educational content that can be viewed on any smartphone or computer. These videos can be used to teach new concepts, mathematical procedures or problem-solving resolutions. Although there is a wide variety of educational videos and MOOCs, on many occasions these videos do not fit in terms of content or notation. With this in mind, in this article, we define the detailed process that we should follow if we are interested in creating our own educational videos. We devote special attention to giving advice on video creation from a methodological point of view. This process is enhanced with an example of learning unit collection that we created at the University of La Rioja. The project that we present is a collection of math Olympiad problem-solving strategies that can be used as complementary learning materials.
Environment Scene Classification Based on Images Using Bag-of-Words

We analyse the environment scene classification methods based on the Bag of Words (BoW) model. The BoW model encodes images by a bag of visual features, which is a sparse histogram over a dictionary of visual features extracted from an image. We analyse five feature detectors (Scale Invasive Feature Transform (SIFT), Speed-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Maximally Stable Extremal Regions (MSER), and grid-based) and three feature descriptors (SIFT, SURF and U-SURF). Our experiments show that feature detection with a grid and feature description using SIFT descriptor, and feature detection with SURF and feature description with U-SURF are most effective when classifying (using Support Vector Machine (SVM)) images into eight outdoor scene categories (coast, forest, highway, inside city, mountain, open country, street, and high buildings). Indoor scene classification into five categories (bedroom, industrial, kitchen, living room, and store) achieved worse results, while the most confused categories were industrial/store images. The classification of full image dataset (15 outdoor and indoor categories) achieved the overall accuracy of 67.49 ± 1.50%, while most errors came from misclassifications of indoor images. The results of the study can be applicable for assisting living applications and security systems.
Application of Fuzzy Kernel C-Means in face recognition to identify look-alike faces

Abstract Machine learning has been rapidly evolving and continuously developing. Many problems can be solved by machine learning. One of those is face recognition. There are many application of face recognition. One application that will be discussed in this research is face recognition to identify look-alike faces. Such application can be useful when dealing with huge data. Machine learning method that will be used is Fuzzy Kernel C-Means. This method is the modification of previous method Fuzzy C-Means. The kernel used is Radial Basis Function Kernel. Each image is characterised by its features. It is believed that reducing the number of features can also reduce the cost. Therefore, a feature selection method called Chi-Square was also used. Solving this problem in face recognition using Fuzzy Kernel C-Means resulted in quite high accuracies.
Sharing of Encrypted Files in Blockchain Made Simpler

Recently, blockchain technology has attracted much attention of the research community in several domains requiring transparency of data accountability, due to the removal of intermediate trust assumptions from third parties. One such application is enabling file sharing in blockchain enabled distributed cloud storage. Proxy re-encryption is a cryptographic primitive that allows such file sharing by re-encrypting ciphertexts towards legitimate users via semi-trusted proxies, without them learning any information about the underlying message. To facilitate secure data sharing in the distributed cloud, it is essential to construct efficient proxy re-encryption protocols. In this paper, we introduce the notion of proxy self re-encryption (SE-PRE) that is highly efficient, as compared to the existing PRE schemes in the literature. We show that our self encryption scheme is provably CCA secure based on the DLP assumption and our proxy re-encryption scheme with self encryption is CCA secure under the hardness of the Computational Diffie Hellman (CDH) and Discrete Logarithm (DLP) assumption. Our novel encryption scheme, called self encryption, has no exponentiation or costly pairing operation. Even the re-encryption in SE-PRE does not have such operations and this facilitates the service provider with efficiency gain.
A Subjective Study of Viewing Experience for Drone Videos Using Simulated Conte

This paper presents subjective evaluation results on the viewing experience of simulated aerial videos shot at different drone heights. A total of fifty video sequences were generated using a simulation engine, Unreal Engine 4, for two racing scenarios and five different shot types. Twenty human viewers were then employed to participate a subjective experiment, providing their preference opinions on viewing experience of these videos. Through the subjective test, optimal parameters of UAV height have been identified for the evaluated shot types and scenarios. These will provide recommendation of default shot parameters for drone operation in autonomous shooting and flight planning.
A Compact Wearable Eye Movement Measurement System for Support of Safe Driving

If the eye movement of a driver who makes a long drive during the day and at night can be measured with less stress to the driver in order to estimate the following items for a long time with high accuracy, it is expected that the information will make a great contribution to safe driving: 1) What the driver is looking at or paying attention to (i.e. gaze), 2) A feeling or sign of unwellness such as dizziness, car sickness, and discomfort (i.e. rotational eye movement). In this study, the authors propose an approach in which an area of high intensity gradients in the white part of the eye, i.e. an area that includes a blood vessel with a distinctive shape, is automatically selected to estimate the gaze and calculate the angle of eye rotation, so that the target ocular blood vessel can be tracked with high accuracy even if blood vessels in the white part of the eye differ considerably between individuals in terms of shape, length, and thickness. An evaluation experiment yielded excellent results under various lighting conditions. Especially, extremely positive results were obtained for the estimation accuracy of rotational eye movement, with an estimation error of -0.69° ± 1.02° (mean and standard deviation).
56‐3: A novel Emissive Projection display (EPD) on Fully‐Transparent Phosphor Screen

A new paradigm of digital projection is on the horizon, based on innovative emissive screen that are fully transparent. It can be readily applied and convert any surface to a high image quality emissive display, without affecting the surface appearance. For example, it can convert any glass window or windshield to completely see‐through displays, with unlimited field of view and viewing angles. It also enables a scalable and economic projection display on a pitch‐black emissive screen with black level and image contrast that rivals other emissive displays such as plasma display or OLED.
On Linear Programming for Constrained and Unconstrained Average-Cost Markov Decision Processes with Countable Action Spaces and Strictly Unbounded Costs

We consider the linear programming approach for constrained and unconstrained Markov decision processes (MDPs) under the long-run average-cost criterion, where the class of MDPs in our study have Borel state spaces and discrete countable action spaces. Under a strict unboundedness condition on the one-stage costs and a recently introduced majorization condition on the state transition stochastic kernel, we study infinite-dimensional linear programs for the average-cost MDPs and prove the absence of a duality gap and other optimality results. Our results do not require a lower-semicontinuous MDP model. Thus, they can be applied to countable action space MDPs where the dynamics and one-stage costs are discontinuous in the state variable. Our proofs make use of the continuity property of Borel measurable functions asserted by Lusin’s theorem.
Evaluation on perceived sizes using large-scale augmented floor visualization devices

Large scale output displays are an enabling technology in order to achieve immersive, isometrically registered, virtual environments. Amongst other benefits, pervasive displays allow for more accurate size judgments, better collaboration performance in workshops and less task completion time. Since research still does not offer an holistic view on size perception and reaction time using wall-sized displays, this paper follows a call for research. This evaluation investigates the size judgment accuracy, precision and reaction time using a large scale led floor compared to tablet computer with relative scales. Therefore, an output apparatus is presented using a 54 sqm LED floor surrounded by additional 36 square meters of LED walls. Using this apparatus, 22 participants were provided with the same virtual contents in 3 different scenarios. The baseline scenario (tablet computer) visualizes relative sized geometry, whereas two LED floor scenarios show true to scale (absolute sized) contents. Results indicate that using true to scale visualization of 2D contents on a LED floor reduces mean absolute percentage error (MAPE) of spatial estimations significantly and is the user's preferred visualization device. No systematic over- or underestimation of size judgments could be revealed for all three scenarios. In contrast to that, task completion time rises by using such an apparatus.
Video Object Detection by Aggregating Features across Adjacent Frames

Abstract Video object detection is of great significance for video analysis. In contrast to object detection in still image, video object detection is more challenging which suffers from motion blur, varying view-points/pose, and occlusion. Existing methods utilized temporal information during detection in videos and show improvement over static-image detector. In this paper, we propose a novel method for video object detection that can aggregate features across adjacent frames adaptively as well as capture more global cues so as to be more robust to drastic appearance changes. Initially, current frame feature and warped feature from adjacent frames can be obtained via feature extraction network and optical flow network. Next, a coherence contribution module is designed for adaptively feature aggregation of the two kinds of features obtained from the first step. Finally, the still-image detector which included an extra instance-level module that agregates features from adjacent frames for capturing more global feature is adopted to get the final result. The experimental results evaluated on our method shows leading performance on the ImageNet VID dataset.
Detecting Opinion Polarities Using Ensemble of Classification Algorithms

Text classification enables developers to track consumer's reaction to e-commerce products. Such information, often expressed in the form of raw emotions, can be used to measure consumers' emotions and their emotional preference for commodities, so as to help future consumers to make choices. However, capturing and interpreting human emotions expressed in product reviews is not a trivial task. Challenges stem from integrated approach and optimal feature combination methods of different classifiers. In this paper, we present a ensemble framework of text classification. It can found that the most adapitve feature sets for classifiers. An effective method of CRF model to process medium and long text is proposed, this method significantly improves the CRF model's ability to handle text, length of which is more than ten. As base learning algorithms, three classifiers are integrated to improve the efficiency of classification, which are Support Vector Machine (SVM), Conditional Random Filed(CRF), and Naive Bayes Multinomial(NBM). The experiments prove the effectiveness of our proposed method.
A Video Gesture Processing Method Based on Convolution and Long Short-Term Memory Network

As there are many deaf-mute groups in today's society, it is particularly important to solve the problem of communication barriers. In view of the limitations of wearing equipment and the low recognition rate of non-equipment at the present stage, using the network to extract hand information and using different features and classification methods to recognize dynamic hand information has become the mainstream. In this paper, Optical flow method is used to detect and process moving target objects on video. The method of combining Convolution Neural Network with Long Short-Term Memory (CNN + LSTM ) was improved and compared with Convolution Neural Network and Support Vector Machine (CNN + SVM ). Experiments show that the optical flow method is used to process video motion information, which not only contains the description of the motion information of moving objects, but also the information of the three-dimensional structure of the scene. After the processed video stream is trained by CNN + SVM , CNN +LSTM and the improved CNN + LSTM , CNN + SVM has a lower recognition rate than CNN + LSTM, The improved network uses PReLU as activation function to improve the gate structure of LSTM, and The recognition accuracy increased from 61.3% to 92.4%.
AQuA: Automatic Quality Analysis of Conversational Scripts in Real-time

Call center is a point of contact between customers and the organization. A service agent represents the organization in one way and act as the face of the organization. The quality of service provided to a customer by a service agent strongly determines the reputation of an organization. Hence, good quality service is of utmost importance for an organization's continual growth in the present dynamic market situation. In order to streamline the process of providing exceptional service, organizations have placed human experts to evaluate the quality of calls. However, evaluation through human experts is labour-intensive, limited to few random calls and expensive. In this paper, we propose a Quality AI Assistant which actively monitors all agents' interactions and provides feedback to improve call quality in real-time. We also propose a model to predict the customer satisfaction. We apply Deep Learning based approach to evaluate the quality of calls on different organizational compliance aspects and predict the customer satisfaction. We evaluated the performance of our system on an in-house contact centre dataset of finance domain. The experiment results demonstrate the effectiveness of the approach which is quite promising.
Energy Efficient Level by Level Scheduling for Multiple Workflows in Cloud

Cloud computing provides paradigms for hosting a large number of services by providing configurable computing resources on demand, causing multiple applications to be hosted simultaneously in the form of multiple workflows which requires large scale data centers. The reduction in energy consumption is one of the major concerns of large-scale data centers. In this article, the authors develop a multiple workflow scheduling heuristic with the aim to reduce energy consumption along with the execution time. In the proposed approach, first tasks are considered level by level following the precedence constraints as a result of the tasks multiple DAGs could run in parallel. The tasks at each level are considered in the order of their ranking and scheduled on the efficient processor based on their estimated finish time where idle slots of resources are used to reduce the overall makespan and energy consumption. Finally, DVFS is applied during idle slots and the communication phase to reduce energy consumption further by scaling the frequency to appropriate level.
Getting (more) real

The classification of eye movements is a very important part of eye tracking research and has been studied since its early days. Over recent years, we have experienced an increasing shift towards more immersive experimental scenarios with the use of eye-tracking enabled glasses and head-mounted displays. In these new scenarios, however, most of the existing eye movement classification algorithms cannot be applied robustly anymore because they were developed with monitor-based experiments using regular 2D images and videos in mind. In this paper, we describe two approaches that reduce artifacts of eye movement classification for 360° videos shown in head-mounted displays. For the first approach, we discuss how decision criteria have to change in the space of 360° videos, and use these criteria to modify five popular algorithms from the literature. The modified algorithms are publicly available at https://web.gin.g-node.org/ioannis.agtzidis/360_em_algorithms. For cases where an existing algorithm cannot be modified, e.g. because it is closed-source, we present a second approach that maps the data instead of the algorithm to the 360° space. An empirical evaluation of both approaches shows that they significantly reduce the artifacts of the initial algorithm, especially in the areas further from the horizontal midline.
Visually comparing eye movements over space and time

Analyzing and visualizing eye movement data can provide useful insights into the connectivities and linkings of points and areas of interest (POIs and AOIs). Those typically time-varying relations can give hints about applied visual scanning strategies by either individual or many eye tracked people. However, the challenging issue with this kind of data is its spatio-temporal nature requiring a good visual encoding in order to first, achieve a scalable overview-based diagram, and second, to derive static or dynamic patterns that might correspond to certain comparable visual scanning strategies. To reliably identify the dynamic strategies we describe a visualization technique that generates a more linear representation of the spatio-temporal scan paths. This is achieved by applying different visual encodings of the spatial dimensions that typically build a limitation for an eye movement data visualization causing visual clutter effects, overdraw, and occlusions while the temporal dimension is depicted as a linear time axis. The presented interactive visualization concept is composed of three linked views depicting spatial, metrics-related, as well as distance-based aspects over time.
A Fast, Memory-Efficient Alpha-Tree Algorithm Using Flooding and Tree Size Estimation

The $$\alpha $$ –tree represents an image as hierarchical set of $$\alpha $$ -connected components. Computation of $$\alpha $$ –trees suffers from high computational and memory requirements compared with similar component tree algorithms such as max–tree. Here we introduce a novel $$\alpha $$ –tree algorithm using (1) a flooding algorithm for computational efficiency and (2) tree size estimation (TSE) for memory efficiency. In TSE, an exponential decay model was fitted to normalized tree sizes as a function of the normalized root mean squared deviation (NRMSD) of edge-dissimilarity distributions, and the model was used to estimate the optimum memory allocation size for $$\alpha $$ –tree construction. An experiment on 1256 images shows that our algorithm runs 2.27 times faster than Ouzounis and Soille's thanks to the flooding algorithm, and TSE reduced the average memory allocation of the proposed algorithm by 40.4%, eliminating unused allocated memory by 86.0% with a negligible computational cost.