Reviewerly/exHarmony huggingface dataset:
Context Sensitivity of Spatio-Temporal Activity Detection using Hierarchical Deep Neural Networks in Extended Videos

The amount of available surveillance video data is increasing rapidly and therefore makes manual inspection impractical. The goal of activity detection is to automatically localize activities spatially and temporally in a large collection of video data. In this work we will answer the question to what extent context plays a role in spatio-temporal activity detection in extended videos. Towards this end we propose a hierarchical pipeline for activity detection which spatially localizes objects first and subsequently generates spatial-temporal action tubes. Additionally, a suitable metric for performance evaluation is enhanced. Thus, we evaluate our system using the TRECVID 2019 ActEV challenge dataset. We investigated the sensitivity by detecting activities multiple times with various spatial margins around the performing actor. The results showed that our pipeline and metric is suited for detecting activities in extended videos.
Ludlings: not all fun and games

Generations of children and adults alike have been using language games as a fun way to secretly communicate. These language games, also known as ludlings, are also used by criminals in furtherance of their crimes. In response, the FBI Laboratory in Quantico, Virginia assembled a team to study these systems and assist law enforcement agencies in the decryption of ludlings. This paper explains the most common English ludling systems encountered by the FBI.
An on-demand cloud storage scheme based on context aware

To protect the confidentiality, integrity and privacy of users' data in cloud storage, a hierarchical cloud storage scheme based on environment features is proposed. The scheme maps the relationship between user attributes, data encryption security requirements, operating environment and data security storage levels. The hierarchical cloud storage addressed twice is realized. Firstly, the starting address of storage area corresponding to user data is determined according to the data security requirement level. Secondly, the data address offset is calculated through the data attribute set and account information set, and finally the hierarchical cloud storage of data is realized. Experimental results show that users' security requirements are met and security is improved, providing a solution for the implementation of cloud storage.
Synthesis of digital twins with use of multidimensional recursive decomposition

The article presents the scientific and methodological issues of formation of digital twins collections based on the use of the multi-aspect recursive decomposition algorithm of the subject area. The general approaches to the solution of topical issues of the modern stage of artificial intelligence are considered. The terminology is concretized in the interrelated areas of knowledge – information – data and its relation with the term of «digital twins» as information containers of knowledge is discussed. The structure, power estimation and metrizability of the information space presented as a recursively defined ordered set of elements – a collection of digital twins (DT-collections) are considered. It is shown that the practical implementation of this approach and its application as part of automated control systems involves maintaining the life cycle of the creation and operation of digital twins in the Integrated information storage, implementing a two-circuit scheme (model) of management. A new cognitive approach to assess the completeness of the knowledge measure in the information space is proposed. The model of the integrated information storage realizing accumulation of knowledge in data banks of primary and secondary information is considered. As an example, a recursive decomposition of a subset of engineering systems of an educational institution is performed.
Baseline Length Estimation Method Based on Binocular Stereo Vision Perception

With the emergence of various 3D stereo imaging devices, stereo viewing has become more and more popular. At the same time, since the technologies of such as WEBGL are used, the design and implementation of 3D scene becomes easier. But most of the WEBGL designs are for single-eye situations. For binocular stereo vision scenarios, we should reasonably design the baseline length of left and right cameras and the related viewing distance and angle. Binocular stereo vision related papers have done a lot of research on the design of stereo vision parameters, but the relationship between baseline length, viewing distance and viewing angle of two cameras for WEBGL scene rendering combined with human eye perception and stereo scene has not been deeply discussed. This paper presents a series of relationship functions with the baseline length of rendered scene, viewing distance and viewing angle combining the binocular stereo vision and imagining theory. The relationship functions are analyzed through experiments. The experimental results show that the method has a good theoretical guidance for setting the relevant parameters reasonably.
Effective orifice area measurement of artificial heart valve based on image processing

Artificial heart valves must be tested and evaluated by in vitro hemodynamic performance from development to clinical application. Effective orifice area(EOA) is one of the important indicators. At present, EOA measurement method mainly relies on manually depicting the contour of the valve opening and calculating it by software. In this paper, a method for measuring the EOA of the artificial heart valve based on image processing is proposed to improve the measurement efficiency of the EOA of the artificial heart valve and solve the problem of low automation and high manual intervention of the traditional method. The morphological image processing can effectively remove the bright noise of the stainless steel bracket and the orifice shaded portion; the region growing algorithm can well segment the open shadow portion. The results show that the method can accurately measure the EOA of artificial heart valves of different specifications, and provide data support for analyzing the relationship between opening area, flow rate and transvalvular pressure difference.
Fast and Scalable 2D Convolutions and Cross-correlations for Processing Image Databases and Videos on CPUs

The dominant use of Convolutional Neural Networks (CNNs) in several image and video analysis tasks necessitates a careful re-evaluation of the underlying software libraries for computing them for large-scale image and video databases. We focus our attention on developing methods that can be applied to large image databases or videos of large image sizes. We develop a method that maximizes throughput through the use of vector-based memory I/O and optimized 2D FFT libraries that run on all available physical cores. We also show how to decompose arbitrarily large images into smaller, optimal blocks that can be effectively processed through the use of overlap-and- add. Our approach outperforms Tensorflow for 5 × 5 kernels and significantly outperforms Tensorflow for 11 × 11 kernels.
Voice-Enabled Intelligent IDE in Cloud

For computer programmers, an Integrated Development Environment (IDE) is an essential part of software development. Online IDEs are gaining popularity in the recent days due to the ease of use and facilities to develop and maintain codes written in various languages online, making it easier to access the codes from different devices. A cloud computing platform is ideal for developing online IDEs due to the elasticity of resource it provides to the users. In this paper, we present a cloud-based IDE that can convert spoken commands to code templates (talk-to-code). The IDE can be used to write codes and compile it by using a varying range of programming languages. The input for the programs can be uploaded directly from files and the output can be downloaded as a text file. The talk-to-code feature of the IDE was developed using a simple convolutional neural network (CNN) trained with some specific words to make it lightweight and to decrease the response time. We have compared our CNN speech recognition model with other models trained on heavier data sets and it has shown better results in terms of time and accuracy for this particular area of application. The cloud-based IDE was also compared with other available online IDEs and exhibited satisfactory results.
A Saliency-Fusion-Based Framework For Co-localization

In the field of computer vision, object colocalization problem is a relatively new branch of object detection. The state-of-the-art methods mainly focus on supervised learning and non-traditional approaches. However, supervised learning certainly has better performance in detecting a conspicuous object, but a mass of images with manual annotation is indispensable, which is a heavy burden for obtaining training images. This paper presents a general co-localization framework and introduces a traditional approach which is based on mathematical optimization, for narrowing the gap of performance between the unsupervised and supervised methods in co-localization. The localization technique we adopted is based on multiple saliency map fusion and mathematical optimization strategies. By extracting the co-saliency map from a set of images with multiple methods and merging them at image level or superpixel level, we derive the object co-saliency fusion maps that the performance is better than single saliency method. This representation overcomes the problem of foreground ambiguity, which scales down the location area and improves the accuracy of object colocalization. And we demonstrate the availability of co-saliency fusion adopted in co-localization problem by the experimental results on the object discovery dataset.
Identification of senior high school students’ preconception on artificial muscle context in chemistry learning

Abstract This research aims to identify students’ pre-conceptions on artificial muscles context as a basis for developing a didactic design in artificial muscle learning, related to chemical contents studied in secondary school. This study employed a descriptive method that was presented quantitatively and qualitatively. The instrument used in this study was a test in the form of essay questions consisted of 7 discourses about artificial muscles. The participants of this study were 25 students from a secondary school in Bandung, West Java. The results of data analysis indicated that 76% of students fell in the category of “low understanding”. The analysis of students’ preconception was used and implemented in the development of a didactic design aiming at connecting artificial muscle context with the application of science and technology in chemistry learning at secondary schools.
Representation Learning for Diagnostic Data

Representation learning algorithms have recently led to a significant progress in knowledge extraction from network structures. In this paper, a representation learning framework for the medical diagnosis domain is proposed. It is based on a heterogeneous network-based model of diagnostic data combined with an algorithm for learning latent node representation. Furthermore, a modification of metapath2vec algorithm is proposed for representation learning of heterogeneous networks. The proposed algorithm is compared with other representation learning approaches in two practical case studies: symptom/disease classification and disease prediction. A significant performance boost can be observed for these tasks, resulting from learning representations of domain data in a form of a heterogeneous network. It is also shown that in certain situations the modified algorithm improves the quality of learned embeddings compared to reference methods.
Pretrained Convolutional Neural Networks for Cancer Genome Classification

Deep learning techniques, particularly convolutional neural networks (CNNs), have proved their success and popularity recently in many fields, especially distinguishing and analyzing medical diseases. Motivated by this direction, our work attempts for the first time to investigate the application of a state-of-the-art deep learning technique on genomic sequences to classify tumours of different classes. The novelty of our approach lies in the application of the popular pre-trained AlexNet on an image version of the RNA-Sequence data. Our methodology demonstrated an outstanding performance with good sensitivity results of 98.3%, 94.1%, 96.6%, 100%, and 100% for selected types of breast, colon, kidney, lung and prostate cancers respectively. The outcome of this work is expected to provide a new direction for genomics data classification and designing accurate automated diagnosis tools.
Target-driven Model Learning for Collision-aware Planar Object Pushing

In this paper, we introduce our end-to-end model learning approach for planar object dynamic pushing from arbitrary position to user-specified positions while avoiding collision with the surroundings. We present the learning using a target-driven deep CNN-based network and the corresponding automated dataset construction using models learned through deep reinforcement learning. We employ customized Deep QNetwork models to let the robot learn separately the greedy pushing policy with plain robot-object-target states (in simple empty pushing environment), and the local collision avoidance strategy with partial implicit local obstacle information (e.g., range data provided by laser scans). The learned models are combined as hierarchical global and local planners to automate the construction of an expert-guided database that is used to train the robot on our target-driven CNN model which maps the robotobject-target states information and local depth information to robot velocity commands. We demonstrate with simulation experiments the effectiveness of our learned models for various pushing cases and show the robustness of the supervised model to generalize to previously unseen situations.
Artificial Intelligent Agent for Energy Savings in Cloud Computing Environment: Implementation and Performance Evaluation

The gaining popularity of the Internet of Things (IoT), big data analytics, and blockchain to make the digital world connected, smart, and secure in the context of smart cities have led to increasing use of the cloud computing technology. Consequently, cloud data centers become hungry for energy consumption. This has an adverse effect on the environment in addition to the high operational and maintenance costs of large-scale data centers. Several works in the literature have proposed energy-efficient task scheduling in a cloud computing environment. However, most of these works use a scheduler that predicts the power consumption of an incoming task based on a static model. In most scenarios, the scheduler considers the CPU utilization of a server for power prediction and task allocations. This might give misleading results as the power consumption of a server, handling a variety of requests in smart cities, depends on other metrics such as memory, disk, and network in addition to CPU. Our proposed Intelligent Autonomous Agent Energy-Aware Task Scheduler in Virtual Machines (IAA-EATSVM) uses the multi-metric machine learning approach for scheduling of incoming tasks. IAA-EATSVM outperforms the mostly used Energy Conscious Task Consolidation (ECTC) based on a static approach. The detailed performance analysis is elaborated in the paper.
Joint HFaster-RCNN and Bayesian Posterior Probabilities for Pedestrian Detection

A novel fusion HOG descriptor and Faster-RCNN (HFaster-RCNN) for pedestrian detection method is proposed. The proposed method generates fine pedestrian candidate regions using fusion saliency map and region proposal network (Saliency RPN). In order to synthesize the complementary advantages of traditional hand-crafted feature method and deep learning, we realized the classifier decision information fusion of Faster-RCNN and HOG feature descriptor by using Bayesian posterior probabilities. Experimental results show that the proposed method has better hit rate compared with single feature extraction method. Furthermore, the detection results on the Caltech pedestrian dataset indicate that the proposed method can achieve state-of-the- art performance compared with the existing pedestrian detection methods. At last, the method is tested on a smart camera of Xilinx Zynq computing platform. Experimental results show that it costs around 0.26 seconds to detect an image and keeps high accurate rate with the network embedded on smart camera.
Containerization of intelligent terminal application in Ubiquitous Power Internet of Things

With the rapid construction of the ubiquitous power internet of things, the increasing demand of embedded hardware usage and the complex usage scenario of the embedded platform hardware in intelligent terminal. A more efficient vitalization solution was demanded. With the analysis of traditional virtualization solutions and the armv8 architecture embedded platform hardware support for virtualization, the implementation details of arm embedded platform supporting containerization technology are described. In particular, the specific steps of application containerization are introduced. In the end, we analyze the performance that application run in container and discuss future research directions.
Research and Implementation of Identity Recognition Based on Multi-Feature Fusion

Face recognition has been applied in numerous identification systems, however the constraint on the distance of faces and video sensors suppresses its development. The practical facts turn out that the performances in terms of accuracy and latency decline along with the distance increase. This paper focuses on identification at distance and proposes a multi-feature fusion identity recognition algorithm (MFIR) based on the Principal Component Analysis. Multi-feature combines height, clothing and functional face features from contour extraction for target identity at a distance. This combination makes the weighted eigenvalue a better representation of the characteristics for the identified object. We conduct experiments on the proposed algorithm with real data. The results show that the accuracy of MFIR compared with face-feature identification outperforms in long-distance scenario.
Circular Template Matching Based on Improved Ring Projection Method

Circular template as matched target is universally applicated in template matching and visual measurement as its isotropy, accurate positioning and other characteristics. Therefore, accurately establishing the correspondence between circular templates from different views is important. However, the circular template degenerates into an oval after perspective projection transformation and loses isotropy. Traditional ring projection method (RPM) is robust only to rotation and noise, establishing the correspondences is difficult. In this paper, an improved RPM, which combines circular template normalization and RPM, can be used to match circular templates of multi-view images. The algorithm is robust to scale, translation, shear, illumination, rotation. The robustness and high matching success rate are confirmed by a set of experiments.
Economic Imperception; or, Reading Capital on the Beach with Thoreau

Abstract This essay argues that throughout his oeuvre, Henry David Thoreau develops a theory of sensory perception that comes to its literary culmination in Cape Cod (1865). I argue that Thoreau’s thinking on the senses demonstrates that the senses are a product of historical development. In Cape Cod, Thoreau is particularly interested in how economic interest has trained the senses to become structurally incapable of sensing the death that is a necessary part of the commodity form and social life in general—similar to what Karl Marx in Capital (1867) would describe as “dead labor.” This essay explores how in Cape Cod, Thoreau offers his reader a method of reading that seeks to make legible through literary form—specifically through puns, metaphor, and juxtaposition—the point at which the senses fail. Thoreau’s method in Cape Cod therefore differs from his more well-known works such as Walden (1854), where he only explicitly tells his readers about that to which they are blind. Ultimately, I claim that Thoreau’s political message throughout his career remains much the same, but the way in which he mobilizes literary form to convey that message marks an important change in how we might make sense of—and make sensible—political economy.
Automatic Generation of Code Comments Based on Comment Reuse and Program Parsing

In order to solve the problem of rare comments and low quality, a method of automatically generating code comments by comment reuse and program parsing is proposed. First, the clone code is detected by Nicad, and the codes and their comments are extracted. Then, through a series of heuristic rules such as dedrying and generalization, the code and the relatively simple code comments are streamlined and optimized. For complex code comments with obvious semantic characteristics, the program parsing method is used to optimize, finally, the comments are mapped to the code to automatically generate comments for the target code. In order to verify the validity of the experiment, the code comments in the 14 target software were manually verified, the comment results were evaluated, and then the five softwares were compared with the previous methods of ours team. The experimental results show that 33.69% of the code comments are good, the generated comments are 14.13% higher than the previous experimental methods, and the quality is improved by about 5%.
DWT-based color image encryption using multi-chaotic maps

This paper introduces a discrete wavelet transform (DWT)-based color images encryption method by multi-chaotic maps. Three improved one-dimensional (1D) chaotic maps are utilized to produce the keystreams, where the original image and the external secret key are employed to update the initial conditions and parameters of the chaotic systems. The image sub-bands obtained via DWT decomposition are firstly encrypted. In what follows, the image after inverse discrete wavelet transform (IDWT) reconstruction is further diffused in the spatial domain by the keystreams to strengthen the security of the cryptosystem. Experimental results and security analysis show the feasibility, validity and security of the proposed scheme.
An Efficient Approach for Document Categorization Using Weighted Sum

Document categorization or classification is an active research area for simplifying the information retrieval due to enormous collection of electronic documents. Large amount of information is generated from day-to-day activities through various sources. Practically categorization of these text documents needs dexterous skills and consumes lot of time. Thus, it becomes challenging to automatically organize and classify documents into the pre-defined classes based on their contents using efficient approaches. In this paper, we adopt the weighted sum approach, for classification of documents. The weights for each word in the document are assigned based on the frequency of its appearance in the document. This approach is efficient and yields better performance than the existing methods. The results show the linear growth in time for the increase in varied data sets.