Reviewerly/exHarmony huggingface dataset:
Distance Restricted Transformer Encoder for Multi-Label Classification

Multi-label image classification is a fundamental but challenging task in Multimedia community. It aims to predict a set of labels presented in an image. Great progress has been made by exploring convolutional neural network with binary cross-entropy loss recently. However, conventional approaches are limited to highlight the key visual contents associated with target labels and pay little attention to confining the distances between visual and positive/negative label representations. To target these aspects, we firstly introduce a variant transformer encoder model for acquiring the underlying and crucial visual information related to ground truth labels. Specifically, a novel primal feature guided net is designed to maintain the original visual features during encoding process. Secondly, we exploit a distance restricted learning strategy in a common semantic space to shrink the distances of images with positive labels while expand with the negative ones during training stage. Extensive experiments are executed on MSCOCO and WIDER Attribute datasets and outstanding performance is achieved compared with other state-of-the-art models.
Good Vibrations: Tuning a Systems Dynamics Model of Affect and Cognition in Learning to the Appropriate Frequency Bands of Fine-Grained Temporal Sequences of Data

Process-oriented studies of cooperative learning from an educational neuroscience perspective has not been firmly quantified experimentally. Within a modeling approach aimed at the development of a systems dynamics model of affect and cognition, the goal of this exploratory study is to identify typical timescales of variation for continuous metrics of affect (Frontal Alpha Asymmetry (FAA): valence) and cognition (Cognitive Load (CL); Index of Cognitive Engagement (ICE); Frontal Midline Theta (FMT): attention). These metrics were obtained from 72 participants paired in dyads (player and watcher) from whom electroencephalography (EEG) was recorded for 2 hours while one participant was playing a serious game to learn Physics, and the other one was watching passively. The results show rather slow cyclical variation for every metric tested, accompanied in certain cases by short bursts of faster variations. This result converges with [Newell 1990] cognitive architecture assuming that psychophysiological measures capture activity at higher levels such as operation tasks and operations. Theoretical, methodological and applied implications are discussed. Also, the need for further fine-grained analyses of the context and other atypical analyses are expressed.
MemA: Fast Inference of Multiple Deep Models

The execution of deep neural network (DNN) inference jobs on edge devices has become increasingly popular. Multiple of such inference models can concurrently analyse the on-device data, e.g. images, to extract valuable insights. Prior art focuses on low-power accelerators, compressed neural network architectures, and specialized frameworks to reduce execution time of single inference jobs on edge devices which are resource constrained. However, it is little known how different scheduling policies can further improve the runtime performance of multi-inference jobs without additional edge resources. To enable the exploration of scheduling policies, we first develop an execution framework, EdgeCaffe, which splits the DNN inference jobs by loading and execution of each network layer. We empirically characterize the impact of loading and scheduling policies on the execution time of multi-inference jobs and point out their dependency on the available memory space. We propose a novel memory-aware scheduling policy, MemA, which opportunistically interleaves the executions of different types of DNN layers based on their estimated run-time memory demands. Our evaluation on exhaustive combinations of five networks, data inputs, and memory configurations show that MemA can alleviate the degradation of execution times of multi-inference (up to 5×) under severely constrained memory compared to standard scheduling policies without affecting accuracy.
A Survey: Development and Application of Behavior Trees

In recent years, artificial intelligence technology has been greatly developed in the game world. Many games use finite-state machines (FSM) to control the intelligent behavior of non-player-controlled characters (NPC). However, due to the complexity of FSM state transition and low reuse, the technology of behavior tree gradually began to develop and replace FSM to a certain extent. This article introduces research on the basic concepts, development trends, and the latest technologies of the behavior tree. In addition, we explore the application and development of the behavior tree in some fields other than the game industry.
Comparison of Eye Movements among Ages while Viewing 3D Video Clips

It is thought that the control system for stereoscopic vision differs between young and elderly individuals. In this study, the authors focused on the differences in eye movements among ages while viewing 3D video clips. The results showed that for the young, eye movements were significantly higher while tracking a visual target than during peripheral viewing of a 3D video clip. However, no significant difference was observed among the elderly. It was suggested that the amplitude of the fixation eye movement increased in the elderly because the visual processing ability in the peripheral vision declined with advancing age.
An Implicit Identity Authentication Method Based on Deep Connected Attention CNN for Wild Environment

Continuous Implicit Authentication (CIA) provides a imperceptible and passive validation in a whole process of operating the mobile devices. In this paper, we describe users’ behavioral patterns through the build-in sensors of smartphones, and apply them to our authentication system. The current work is mainly implemented in a controlled experimental environment. We prove the effectiveness of the implicit authentication method in the natural environment through the analysis of only the touch screen process and the entire process of the user using the mobile phone. We focus on the multi-class strategy in CIA, in which a classifier has to recognize the “owner” of a series of operations from identity banks. To extract temporal domain and space domain features and enhance the classifier's discriminative power, our system utilizes Resnet with Squeeze-and-Excitation incorporating Attention blocks and Additive Angular Margin Loss. We evaluate our system both in our dataset and BrainRun dataset, and the accuracies of distinguish the correct identity achieves are 96.94% and 87.11% respectively. Specially, with the continuous data in BrainRun, the result is generated from behaviors during 5 s in a more interactive game.
The Importance of Virtual Immersion in the Rapid Prototyping of Industrial Products

Abstract The prototype has played a major role in the design of industrial products since the beginning. The series production was never launched until the designers validated the new product by studying in detail a prototype made identical to the ones that would have come out on the tape. Rapid prototyping, especially through 3D Printing, for small and medium-sized products, managed to improve this design phase by the quality of the prototype obtained and by the short times of its realisation. Recently, virtual immersion tends to replace 3D Printing technology (or work together), the prototype made by this technology offering the designer many more features of the designed product. As a concept, Virtual Reality Aided Design (VRAD) significantly reduces production times, costs and especially allows the rapid modification of the prototype, which is impossible to achieve in 3D Printing. Our article presents the study of a Compressed Air Vehicle (CAV) prototype participating in the international academic competition PNEUMOBILE; it is a study carried out by the UPBAIR team of UPB through virtual immersion. We believe that the study can be extended to the prototyping of any industrial product, adding that our CAV prototype is quite simplistic and quite far from the complexity of a series car.
Flat design as a marker of hypermodernism

The subject of this research is flat design as the basic sylistics and visual expression of the era of hypermodernism. Since modern design no longer corresponds to the characteristics of postmodernism, an assumption is made on the advent of the new period in design &amp;ndash; hypermodernism. Hypermodernism is the term that implies the enhancement of design trends founded in the era of modernism: orientation towards modernity, design for manufacturability, split with the past, and absence of aesthetic hierarchy. Flat design is a style that visually expresses the aforementioned trends. The research material includes the final research projects in graphic design, interior design and industrial design, completed by the students of the department of Design of the Moscow Power Engineering Institute and the department of Graphic Design and Visual Communications of the Russian State University named after A. N. Kosygin. The article employs the method of semiotic analysis, which allows determining the key attributes used in flat design explicating the essential characteristics of flat design. The conclusion is made that flat design reflects the basic request of the era of hypermodernism &amp;ndash; democratization of mentality, leveling of any differences up to the destruction of individuality. The idea of leveling of social differences, underlying the design of modernism, in hypermodernism reaches the idea of erasing any differences, any hierarchy, including the aesthetic. The presence of aesthetic hierarchy in design suggests the use of expressive means &amp;ndash; index signs, iconic signs, and symbolic signs. The deconstruction of aesthetic hierarchy leads to the use of unexpressive signs &amp;ndash; simulacrums. Most unexpressive sign is the fourth-stage simulacra &amp;ndash; the sign, which relationship to any reality is reduced to zero. Flat design uses fourth-stage simulacra as the main form, thus meeting the demand of hypermodernism.
A Survey on Current Trends in Human Action Recognition

Human action recognition is a challenging area of research in the field of computer vision due to its complex analytical structure. Researches have been carried out in this field over the past few years. Now, as it has become possible to recognize human action successfully, the researchers are trying to make the recognition system real time in nature. But due to its complex video analysis, it has not been possible for the researchers to make it real time completely. Though some algorithms had been developed that will successfully recognize human action with the minimum delay possible in order to make the recognition system almost real time in nature. In this work, we have compared some of the successful human action recognition methodologies along with the recent trends to accomplish the task in negligible time delay.
Breast Mass Classification Using Classic Neural Network Architecture and Support Vector Machine

Priya, R. Sreelekshmi, V. Nair, Jyothisha J. Gopakumar, G.According to WHO, the most dangerous disease prevailing among women is breast cancer. It is among one of the diseases that is untraceable in the beginning. About 1 in 8 women suffer breast cancer and even results in the removal of their breast. In this domain, a novel experiment to classify breast cancer using convolutional neural network and fuzzy system is introduced. A combination of convolution neural network and fuzzy system has been devised for grouping similar masses of benign and malignant in mammography database based on the mass area in breast. The mammography images are taken for image enhancement and image segmentation for identifying the mass area and the classic neural network architecture (Alexnet) performs the feature extraction. After that it is followed by fuzzy system for finding how much denser the malignant or benign cancer is. A well-known classic neural network architecture AlexNet is employed and is fine tuned to group similar classes. The fully connected (fc) layer is replaced with support vector machine (SVM) to improve the classification effectiveness. The results are derived using the following publicly available datasets: (1) digital database for screening mammography (DDSM), (2) curated breast imaging subset of DDSM (CBIS-DDSM) and (3) mammography image analysis society (MIAS). Data augmentation is also performed to increase the training samples and to achieve better accuracy.
Exploring the Effects of GDPR on the User Experience

With the implementation of the General Data Protection Regulation (GDPR) in the European Union, companies must be able to ensure that applications requesting the user to record personal data inform the user of the purpose for which the data collected is being collected. Furthermore, it is also necessary to ensure the data owner has given his/her explicit consent to the collection of these data and that such consent is recorded. This imperative has required a reworking of the interfaces of the applications. In this sense, this study seeks to explore the policies and practices that are being followed by organizations in designing interfaces. Four case studies were considered in the development of this study to explore this phenomenon in the dimensions of pragmatic hedonic processes of interface design, approaches to collect consent, and how personal data is managed.
Weakly Supervised Volumetric Image Segmentation with Deformed Templates

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume segmentation that is truly weakly-supervised in the sense that we only need to provide a sparse set of 3D points on the surface of target objects instead of detailed 2D masks. We use the 3D points to deform a 3D template so that it roughly matches the target object outlines and we introduce an architecture that exploits the supervision it provides to train a network to find accurate boundaries. We evaluate our approach on Computed Tomography (CT), Magnetic Resonance Imagery (MRI) and Electron Microscopy (EM) image datasets and show that it substantially reduces the required amount of effort.
Fault Maintenance and Repair Application Based on Augmented Reality Technology in Electrical Distribution Systems

This study presents an AR technology-based software that will work on mobile platforms and the pilot application of this software to solve the possible problems that may be experienced other than routine maintenance activities on protection/control panels and devices in SCADA centers. AR is essentially an information technology that provides the ability to present digital data and images in the same environment as our real world through its visualization, instruction/guidance, and interaction capabilities. Object recognition methods, necessary software, and infrastructure requirements are mentioned in this study in order to benefit from these advantages of AR technology correctly. Different scenarios have been created by using AR for possible failures in automation panels in a distribution center with a telemetry system. Finally, an exemplary scenario in the study is realized in a distribution center located in the SEDAŞ area of responsibility.
Pali lexicography using lexonomy

Abstract Lexonomy is a platform which is an open source for writing and publishing dictionaries. Pali Lexicography is actually a dictionary created by using Lexonomy, which is a web based tool for creating dictionaries. Since a dictionary is a means of understanding any language and its translation, the present work is about Pali Lexicography. It is a project that has been created using simple XML tags and technical skills without any cost. In this, word mapping is done using selected words that are chosen from the set of Pali Language which are also present in the Marathi Language set having almost similar meaning in Marathi Language. All the Pali words have their English meaning which are automatically sorted generating the Lexicographic order. Pali dictionary is created with a simple user interface and is easily operable.
Survey of Color Feature Extraction Schemes in Content-Based Picture Recovery System

The method of retrieving most visually similar pictures from a large database or group of picture files is called content-based picture or image recovery (CBIR) system. It is among one of the challenging research areas of multimedia computing and information recovery. In the past few decades, many different picture matching, indexing and recovery algorithms have been developed. In CBIR systems, recovery is based on matching visual content or characteristics of query picture with picture database using a picture—picture similarity calculation. The term “Content” in CBIR refers to the visual content of a picture that means texture, shape, color, etc., or any other feature/descriptor that can be acquired from the picture itself. In this paper, a survey of feature recovery techniques using color feature in the last 10 years is presented and summarized year wise.
Research on Evaluation of Teaching Quality of Marxist Theory in massive open online course Based on Artificial Intelligent

Abstract Machine learning (ML) is a scientific study of algorithms and statistical models used by computer systems in order to perform specific tasks effectively without using clear instructions and relying on patterns and reasoning. It is regarded as a subset of artificial intelligence. The base of machine learning is support vector regression. In contemporary China, with the in-depth development of the sinicization and popularization of Marxism, further strengthening the education of Marxist theory has become its inherent inevitable requirement. Teaching quality is the lifeline of colleges and universities. Effective management of teaching depends not only on the control of teaching process, but also on the establishment of a complete and standardized teaching quality monitoring and evaluation system. This paper puts forward an evaluation model of teaching quality of Marxist theory in massive open online course based on support vector regression, and establishes an evaluation index system of teaching quality of Marxist theory in massive open online course according to the specific needs and the construction principle of evaluation index system. Theoretical and experimental results show that the model has better evaluation effect. Compared with other methods, it has the characteristics of high evaluation accuracy, fast implementation and strong operability, and is suitable for evaluating the teaching quality of massive open online course Marxist theory in colleges and universities.
An Attempt on Twitter ‘likes’ Grading Strategy Using Pure Linguistic Feature Engineering: A Novel Approach

Twitter is one of the people's favorite social media platforms used for sharing thoughts about different aspects may it be emotional like 'love', 'motivation', 'dedication' businesses like 'marketing', 'startup', 'blogging' or health like 'gym', 'fitness', 'food', and similar areas. People follow hashtags for topics in their interest. Agreement of a tweet can be measured by likes or retweets. This paper deals with pure linguistic features other than using embeddings in vector space via TFIDF or Doc2Vec. This paper deals with a collection of tweets on such hashtags and classifying the level of likes the tweet will get using pure linguistic features in the form of a grade.
Case-Based Reasoning for System Anomaly Detection and Management

Integrated System Health Management (ISHM) technologies are mission-critical for deep space exploration. Space habitats are complex systems, made up of several subsystems such as Life Support, Communications, Data Acquisition, Thermal Control, Environmental Monitoring, and Electrical Power Systems. Deep space missions will face increased latency in communications with Earth. As such, it is critical that deep space aircrafts and habitats be capable of detecting anomalies early enough to mitigate the effects of communication delays. It would also be beneficial if the space crew had the tools to not just detect anomalies but also to help them resolve them without waiting for assistance from ground control. Traditionally, model-based reasoning techniques have been effective in monitoring the health of such operations, but the rising demand for rapid fault detection and response in deep-space habitats calls for autonomous monitoring software that is agile, scalable, and can respond to previously unseen events. Data-driven approaches using machine learning techniques can provide the ability to detect novel anomalies and to evolve over time. However, they are not as effective at generating explanations or tracing root causes as rule-based modeling systems. In this paper, we describe a case-based reasoning (CBR) approach to developing ISHM tool that combines the data-driven approach with a method for diagnosing and explaining them. CBR is an artificial intelligence (AI) technique that aims to solve problems by analogy. The system, Anomaly Detection via Topological feature Maps (ADTM), uses self-organizing maps (SOMs) as an unsupervised learning approach for modeling each individual case. ADTM's case-base models represent knowledge about different operational modes, including faults, as individual cases. The design of appropriate indexing mechanisms is crucial to the effectiveness of a case-based model. We describe two different approaches to case-base indexing and retrieval and compare their performance on a data set from a simulation of a CubeSat.
A Software Reusability Paradigm for Assessing Software-as-a-Service for Cloud Computing

Cloud computing is a mode of computing in which there is on demand availability of IT or computer system resources over the network as an online service. Virtualization is the core driving technology of cloud computing. With the increase in utilization of infrastructure, it speeds up IT operations and reduces cost. SaaS is generally operated and it gives numerous advantages to assist end users. To accomplish these advantages, it is important to calculate the reusability of SaaS and there is requirement to develop a reusability paradigm for evaluating SaaS for cloud computing. In this paper, we suggested an inclusive paradigm for assessing reusability of SaaS. We first describe key characteristics of SaaS. And then, we defined reusability attributes from the key characteristics, and derived metrics for the reusability attributes. Correlation measure is used for validating the reusability paradigm of SaaS. This proposed paradigm can be used for evaluation and quality management of SaaS by service providers.
Classification of Sickle Cell Anemia Using Energy-Based KNN Classifier

SCA is an inherited group of red blood cell diseases. Individuals with sickle cells have an irregular protein in their red blood cells. Sickle cell anemia (SCA) is one of a group of SCA disorders. Sickle cell anemia is an inherited red blood cell disease that does not hold enough healthy red cells in your body to carry oxygen. It is dangerous because it can cause extreme pain, anemia, and other symptoms. The early diagnosis is required for sickle cell anemia. In this study, the automatic classification of SCA system is discussed. Initially, the input images are given to gaussian filter, then energy feature is used for feature extraction, and K-nearest neighbor (KNN) classifier is used for classification. The performance of SCA system produces the classification accuracy of 93% using KNN classifier.