Reviewerly/exHarmony huggingface dataset:
Design implications from Cognitive Event Analysis

A key question is how technologies can influence social interaction in autistic children, and how opportunities for interaction can embedded into design. Here, we present observations from video recordings of autistic children playing with different technological interfaces and use cognitive event analysis (CEA) to identify instances of child-led interactions. Artefacts which fostered interaction include i) visual prompts for collaboration, ii) multi-media feedback, and iii) expansion of opportunities through tangible interactions.
Investigating the Social and Temporal Aspects of Children's Physical Activity Games

There are a variety of fitness technologies such as activity trackers, exergames and mobile applications available to promote physical activity. Based on our previous research, we found that children prefer having social interaction, a narrative and flexibility while interacting with such technologies. Other research has shown that persuasive displays encouraged adults towards physical activity. In this research, we present the initial results from a user study conducted on 16 children (ages 6-11) who used the Kidfit suite, a collection of mobile applications that combine and utilize the elements found from previous research to promote activity in children.
Estimated Vehicle Density Based on Video Processing Using the Gaussian Mixture Model Method

Abstract Congestion is a problem that cannot be decomposed properly, especially at big cities in Indonesia, one of them is in Surabaya. To solve this problem is needed to observe characteristics of the road such as the number of vehicles, vehicle speed and vehicle density. This research took the data in several areas in Surabaya by put the camera in the middle of a crossing bridge. This research aims to determine the density value by comparing traffic volume with vehicle speed. The value of speed and number of vehicles can be known using Gaussian Mixture Model method, where object detection is detected by background substraction process, which is video divided into several frames and in each frame images is divided into two part there are foreground and background. The object will be count 1 when blob analysis detect the foreground. In the theory to know the density value of a traffic flow, with compare the number of vehicles and the speed of vehicle. From the data has been taken, it is known that the number of vehicles is opposite with average speed the vehicle that passes. If average velocity of vehicle greater than the number of vehicle so the value density vehicle getting smaller . so that information can be found that the road is experiencing congestion conditions.
Constraint Exploration of Convolutional Network Architectures with Neuroevolution

The effort spent on adapting existing networks to new applications has motivated the automated architecture search. Network structures discovered with evolutionary or other search algorithms have surpassed hand-crafted image classifiers in terms of accuracy. However, these approaches do not constrain certain characteristics like network size, which leads to unnecessary computational effort. Thus, this work shows that generational evolutionary algorithms can be used for a constrained exploration of convolutional network architectures to create a selection of networks for a specific application or target architecture.
Hybrid cryptosystem based on diffraction transfer function and phase-truncated Fourier transform encryption

A hybrid cryptosystem based on phase-truncated Fourier transform (PTFT) encryption and diffraction transfer function is designed. The designed cryptosystem cannot be decrypted by phase-retrieval-based attacks to which conventional phase-truncated Fourier transform cryptosystems are proven vulnerable. Compared with conventional PTFT encryption, the encryption process of this proposed cryptosystem needs no extra devices. With the help of Rayleigh–Sommerfeld diffraction theory, the inverse diffraction optically is realized and the decryption process can be performed using a liquid crystal spatial light modulator. Unlike conventional PTFT cryptosystem, no useful information can be retrieved in case of lacking any of ciphertext, private keys and diffraction parameters. The optical setup and numerical simulation results are provided to verify the effectiveness, security and robustness of proposed hybrid cryptosystem.
An Integrated Biomedical Event Trigger Identification Approach With a Neural Network and Weighted Extreme Learning Machine

Biomedical event trigger identification is a sub-task in biomedical event extraction that aims to recognize the trigger label of biomedical events in context. It is a fundamental task in natural language processing. Previous approaches usually depended on feature engineering with unbalanced data. In this paper, we present a bidirectional long short-term memory convolution neural network weighted extreme learning machine (BC-WELM) to identify the biomedical event trigger. Using the different dimensions of embeddings as input, this model considers the contextual modeling by the Bi-LSTM and the local modeling by CNN and, then, classifies the trigger label to settle the unbalanced problem by the WELM. With this design, the BC-WELM model is helpful for biomedical event trigger identification. The experimental results on the MLEE dataset demonstrate that our approach is capable of outperforming the state-of-the-art baselines on an F1 score.
Innovative research on the construction of China’s youth physical health promotion service system based on sensor technology1

There is a certain correlation between human health and actual exercise. This has always been one of the focuses of many scholars. When analyzing the movement of human body, a lot of modern technology and equipment need to be used. In order to better realize the physical health of young people in our country, the health service system based on sensor technology has been put forward. By introducing accelerometer technology, real-time data capture and analysis of human motion posture and movement trajectory were carried out. At the same time, the reliability of the algorithm was improved. In order to verify the feasibility of the method, a practical case was verified. Experimental results showed that the method was more scientific and reasonable in motion capture analysis.
Augmented Reality for Product Validation: Supporting the Configuration of AR-Based Validation Environments

Organizations are increasingly recognizing the importance of early and continuous validation activities as a means of addressing customer requirements in product development in a better way. At the same time, early validation activities come with additional challenges resulting from a high degree of uncertainty and a lack of resources at this stage. The use of Augmented Reality technology offers a promising approach for addressing these challenges and allows different product variants to be experienced early in the development process, thus generating meaningful customer feedback. However, in many cases, the use of AR technology in product development practice is not target-oriented as it lacks methodological support. Building on this observation, this paper presents a method for supporting the configuration of AR-based validation environments as one of the central preparatory activities for product validation using AR technology. It also introduces a descriptive model for AR-based validation environments laying the theoretical foundations for the method.
The Effects of Adaptive Synchronization on Performance and Experience in Gameplay

Adaptive synchronization (Async) is a recent display technology that lowers frame time mean and variation by altering communication between display and GPU. While conventional graphics systems introduce delay to match irregular GPU frame rates to regular display refresh rates, Async systems instead match display refresh rates to to irregular frame rates. Both NVIDIA and AMD claim that Async displays improve experience for enthusiast gamers. In a first, controlled examination of these claims with non-professionals playing a popular first-person shooter, we find that Async improves gaming performance, can improve experience, and has intriguing relationships to game familiarity and years of gameplay.
Search and Retrieve with a Fully Autonomous Aerial Manipulator

The system presented is an autonomous multirotor-based aerial manipulator that is capable of searching an environment, visually identifying a payload, and performing a pick-and-place type maneuver. Full 3D trajectories for the search stage of the flight are predefined, with the grasping maneuver generated dynamically once the payload is identified. The manipulator and payload interactions with the multirotor base are actively compensated for by the controller to ensure stable flight during all maneuvers. Experimental results are analyzed to evaluate the performance of the system.
KidLED

Obesity in children is a growing cause of concern as it affects more than 18% of children in the United States. Sedentary behaviour is one of the contributors to childhood obesity. Technological interventions like activity trackers and fitness games have been introduced to motivate both children and adults to be physically active. Most of these technologies encourage activity by making them aware of their activity levels. While awareness of current activity levels may help motivating adults, it can be difficult for children to understand their activity levels in terms of number of steps (the most common method used by adults). Instead children should be made aware of their activity levels via simpler and easier to understand technology. Within this context, we present KidLED -- an LED activity display that represents user's activity via a simple color display rather than numbers -- designed to simplify activity tracking for children.
Mapping the Challenges of Outdoor Learning for Both Students and Teachers

In education, it is common to use mobile applications for learning at school and maybe even at museums or parks near the school. However, using technology for outdoor activities in an uncommon environment, for instance at the zoo, is challenging especially for teachers and also for students. It is important to equip them with means that support their learning in this complex situation. To map and understand the challenges of outdoor learning for both students and teachers, where the learning process is supported by a mobile application and the role of the learner has become more as creators of artefacts (game), two case studies were conducted at the zoo. Students and teachers evaluated their experience by answering questionnaires, additional interviews were conducted with the teachers, and observations about students’ activities were carried out at the zoo. The results of these two cases show that this type of learning scenario has a potential to be implemented with high school and university students despite of some typically occurring technological and administrative challenges. However, the learning design must be reconsidered in order to reduce students’ challenges and to provide meaningful learning experiences. Designing and developing a few additional functionalities for the piloted mobile application can eliminate the majority of the reported challenges of teachers and students.
The Case of the ‘Nasty Trolley’ or How Mobile Learning and Tablets are Influencing Emotions and Affects and Shaping the Constitution of the Identity of Teachers and Students

The purpose of this study was to discuss the influence of the use of tablets and mobile learning in post-secondary education and their impact on the emotions and affects felt by a teacher and students and on their identity constitution. Assemblage theory was the onto-epistemological perspective in which this investigation was grounded, and assemblage ethnography was the methodology used to collect data. An ethnographic account was provided based on what was observed during lessons, and was analysed using the concepts of emotions, affects and subjectivation. Findings indicated that no contradictory emotions and affects emerged as a result of the actions and interactions of components in the assemblage. There was a smooth relationship among human components, influencing the way they see each other and their roles. However, regarding human and non-human components, the affects and emotions that emerged impacted each other, causing tensions between these components.
Enhanced Background Oriented Schlieren (EBOS)

Abstract The Background Orientend Schlieren (BOS) is a line of sight optical technique that exploits light refraction arising in inhomogeneous density field to visualize or either measure density gradients. The working principle of BOS is quite simple: a background patter appears distorted when observed through an optical inhomogeneous media, by comparing the distorted and undistorted images the apparent local displacement of the background pattern can be measured. The images comparison is carried out using either cross-correlation or optical flow techniques. In the framework of geometric optics and for a 2D refractive index field the measured displacements is proportional to the refractive index gradient. When applicable, the Gladstone-Dale relationship provides the link between the refractive index and the fluid density. The uncertainty in the measured displacements affects the qualitative and quantitative results extracted from BOS images. Spatial and temporal filtering techniques can reduce the uncertainty at the expenses of the spatial and the temporal resolutions. This work describes a modified BOS technique, named Enhanced BOS (EBOS), which is able to reduce the displacement uncertainty without using any spatial or temporal filter. The EBOS technique uses N undistorted images of a grey-scale background pattern taken at slightly different potions, the N reference images are paired with a single distorted background image. Eventually displacement maps obtained from the N image couples are averaged together. The mean displacement map is an improved estimate of the actual displacement field. The EBOS technique can also be used with optical flow techniques anyhow we limited our investigation to image analysis based on cross-correlation technique. The paper reports the results obtained by applying EBOS and BOS techniques to the same test case. The comparison evidence the capability of EBOS to reduce displacement uncertainty respect to BOS by using a limited number of undistorted background images. EBOS and BOS techniques achieve almost the same uncertainty when the number of particles contained in an interrogation area is high, thus EBOS is most advantageous to use when small interrogation area are required.
Exploring Deep Models for Comprehension of Deictic Gesture-Word Combinations in Cognitive Robotics

In the early stages of infant development, gestures and speech are integrated during language acquisition. Such a natural combination is therefore a desirable, yet challenging, goal for fluid human-robot interaction. To achieve this, we propose a multimodal deep learning architecture, for comprehension of complementary gesture-word combinations, implemented on an iCub humanoid robot. This enables human-assisted language learning, with interactions like pointing at a cup and labelling it with a vocal utterance. We evaluate various depths of the Mask Regional Convolutional Neural Network (for object and wrist detection) and the Residual Network (for gesture classification). Validation is carried out with two deictic gestures across ten real-world objects on frames recorded directly from the iCub's cameras. Results further strengthen the potential of gesture-word combinations for robot language acquisition.
Adversarial Examples are a Manifestation of the Fitting-Generalization Trade-off

In recent scientific literature, some studies have been published where recognition rates obtained with Deep Learning (DL) surpass those obtained by humans on the same task. In contrast to this, other studies have shown that DL networks have a somewhat strange behavior which is very different from human responses when confronted with the same task. The case of the so-called “adversarial examples” is perhaps the best example in this regard. Despite the biological plausibility of neural networks, the fact that they can produce such implausible misclassifications still points to a fundamental difference between human and machine learning. This paper delves into the possible causes of this intriguing phenomenon. We first contend that, if adversarial examples are pointing to an implausibility it is because our perception of them relies on our capability to recognise the classes of the images. For this reason we focus on what we call cognitively adversarial examples, which are those obtained from samples that the classifier can in fact recognise correctly. Additionally, in this paper we argue that the phenomenon of adversarial examples is rooted in the inescapable trade-off that exists in machine learning (including DL) between fitting and generalization. This hypothesis is supported by experiments carried out in which the robustness to adversarial examples is measured with respect to the degree of fitting to the training samples.
End-to-End Voice Encryption Based on Multiple Circular Chaotic Permutation

Voice communication is an important need in daily activities whether delivered with or without technology. Telecommunication technology has accommodated this need by providing a wide range of infrastructure, including large varieties of devices used as intermediary and end devices. One of the cellular technologies that is very widely used by the public is GSM (Global System for Mobile), while in the military, trunked radio is still popular. However, the security systems of GSM and trunked radio have limitations. Therefore, this paper proposes a platform to secure voice data over wireless mobile communication by providing end-to-end encryption. This platform is robust to noise, real-time and remains secure. The proposed encryption utilizes multicircular permutations rotated by expanded keys as dynamic keys to scramble the data. We carry out simulations and testbed implementation to prove that application of the proposed method is feasible.
RETRACTED: An enhanced approach for object detection using wavelet based neural network

Abstract Object recognition indicates colossal increment in the field of picture investigation. The required arrangement of picture objects are recognized and recovered based on object recognition. The procedure associated with Object recognition incorporates division, arrangement, and gathering of articles that can be connected in different designing and logical teaches, for example, science, drug, advertising, brain science, man-made consciousness, PC vision, and remote detecting. A large portion of the ongoing plans in picture investigation confront certain bad marks particularly in protest acknowledgment exactness. Indeed, even in Object recognition, the off base order concerns flawed protest extraction. One of the fundamental detriments in existing methods to picture grouping is that immense measure of information needs in learning plans which is a tedious procedure because of the human time and endeavors. In this way, a novel proposition is expected to successfully separate the state of the protest with appropriate characterization procedure. The topological course of action of the WNN contains the concealed neutrons in the wavelet layer with various goals. Wavelet Neural Network idea is utilized for picture handling which is by all accounts exceptionally productive. WNN structure offers the parallel handling of pictures and preparing process makes the system appropriate for the different sort of picture handling. In this work, a novel characterization system called Affluence based Image Classification (AIC) is proposed utilizing a wavelet neural system.
Learning multi-view manifold for single image based modeling

Image based modeling has an inherent problem that the complete geometry and appearance of a 3D object cannot be directly acquired from limited 2D images, namely reconstruction of a 3D object when only sporadic views are available is challenging due to occlusions and ambiguities within limited views. In this paper, we present a generative network architecture to address the problem of single image based modeling by learning multi-view manifold of 3D objects, which we call Multi-view GAN. Penalties for shape identity consistency and view diversity are introduced to guide the learning process, and Multi-view GAN can provide a powerful representation which consists of 3D descriptors both for shape and view. This disentangled and oriented representation affords us to explore the manifold of views, thus one can detail a 3D object without “blind spot” even if only single view is available. We have evaluated our method on multi-view and 3D shape generation with a wide range of examples, and both qualitative and quantitative results demonstrate that our Multi-view GAN significantly outperforms state-of-the-art methods.
Congratulations! Dual Graphs Are Now Orientated!

A digital image can be perceived as a 2.5D surface consisting of pixel coordinates and the intensity of pixel as height of the point in the surface. Such surfaces can be efficiently represented by the pair of dual plane graphs: neighborhood (primal) graph and its dual. By defining orientation of edges in the primal graph and use of Local Binary Patters (LBPs), we can categorize the vertices corresponding to the pixel into critical (maximum, minimum, saddle) or slope points. Basic operation of contraction and removal of edges in primal graph result in configuration of graphs with different combinations of critical and non-critical points. The faces of graph resemble a slope region after restoration of the continuous surface by successive monotone cubic interpolation. In this paper, we define orientation of edges in the dual graph such that it remains consistent with the primal graph. Further we deliver the necessary and sufficient conditions for merging of two adjacent slope regions.
Information protection behaviors: morality and organizational criticality

Purpose Organizational insiders play a critical role in protecting sensitive information. Prior research finds that moral beliefs influence compliance decisions. Yet, it is less clear what factors influence moral beliefs and the conditions under which those factors have stronger/weaker effects. Using an ethical decision-making model and value congruence theory, this study aims to investigate how moral intensity and organizational criticality influence moral beliefs and intentions to perform information protection behaviors. Design/methodology/approach The hypotheses were tested using a scenario-based survey of 216 organizational insiders. Two of the scenarios depict low criticality information security protection behaviors and two depict high criticality behaviors. Findings A major finding is that users rely more on perceived social consensus and magnitude of consequences when organizational criticality is low and on temporal immediacy and proximity when criticality is high. In addition, the moral intensity dimensions explain more variance in moral beliefs when organizational criticality is low. Research limitations/implications The study is limited by its sample, which is organizational insiders at a mid-size university. It is also limited in that it only examined four of the six moral intensity dimensions. Practical implications The findings can guide management about which moral intensity dimensions are more important to focus on when remediating tone at the top and other leadership weaknesses relating to information security. Originality/value This study adds value by investigating the separate dimensions of moral intensity on information protection behaviors. It also is the first to examine moral intensity under conditions of low and high organizational criticality.