Reviewerly/exHarmony huggingface dataset:
AntMiner: Bridging the Gap Between Data Mining Classification Rule Discovery and Bio-Inspired Algorithms

An exploratory study is presented which emphasize on how to bridge the gap between data mining classification rule discovery and bio-inspired algorithms. Real-world applications like expert system, facial recognition patterns mainly depends on the classification of structured and unstructured data. In this study, predictive accuracy values are calculated by implementing c-AntMiner2 and c-AntMinerpb (ant colony based algorithms) on six standard datasets. A comparative study of c-AntMiner2, c-AntMinerpb is presented against already existed rule induction algorithms (JRIP and PART) and shows a good performance of these bio-inspired algorithms over JRIP and PART.
One-Shot Digit Classification Based on Human Concept Learning

One of the key challenges of present machine learning approaches is to match the human-level performance in terms of number of examples for training. Even though deep learning has achieved remarkable accuracy and speed for classification problems, the performance still depends on the number of examples used for training. In this paper, we explore the problem of classification of handwritten digits from a single training example using a probabilistic approach based on the process in which characters are generated and learned by humans. The results obtained suggest that understanding of the human process can help in achieving good classification results even with sparse data for training.
Game Comment Data Mining Based on Text Sentiment Analysis

In order to understand the needs of the player, understand the advantages and disadvantages of the game, this thesis proposes a way to evaluate the game by player comment content: (1) from the site to mine comment information. (2) Calculate the importance and score of each attribute through the neural network. (3) Analysis via importance-performance analysis (IPA) using attribute importance and scoring. In this way, the advantages and disadvantages of the game can be clearly demonstrated in the form of charts. This issues the combination of dictionary and machine learning, the text vector of emotional words, negative words, degree adverbs, punctuation and whole sentences is characterized by the emotional analysis of sentences, and the evaluation and analysis of the game in five dimensions by IPA method is used.
Non-Uniform Video Time-Lapse Method Based on Motion Scenario and Stabilization Constraint

Time-lapse of user captured video becomes popular in many applications recently, non-uniform sampling and digital video stabilization (VS) are usually two independent steps to keep meaningful contents and provide stabilized output. However, non-uniform sampling may produce large sampling interval and then result in larger motion, this would beyond the stabilization ability of VS and produce unpleasant output. To address this problem, we propose a new auto time-lapse framework, which selects frames not only based on camera motion scenarios, but also refer to the smoothed camera trajectory and considering VS ability according to Field of View (FOV) loss constraint. More specific, we introduce an advanced Markov chain (MC) model, in which smoothed camera trajectory, FOV loss constraint of VS, camera motion scenario, and sampling interval similarity between consecutive frames are encoded as potential functions. Finally, dynamic programming (DP) is employed to find the best non-uniform sampling. Experimental results demonstrate that the proposed method not only achieves pleasant and meaningful non-uniform sampling, but also provides satisfactory stabilization performance. The whole algorithm can work in real time during video recording on mobile device.
Novel Approach to Detect and Extract the Contents in a Picture or Image

Unmistakable evidence of substance in shaded pictures of complex back ground is a noteworthy troublesome issue. This paper gives a count for perceiving content in pictures. Exploratory outcomes on indoor, outside, captcha and moving follows pictures demonstrate that this system can perceive content words unequivocally. The proposed estimation joins the upsides of two or three already philosophies for substance distinguishing proof, and usages a point of union of thought approach for substance finding. Our trial result on four diverse pictures display that the procedure subject to line edge discovery is reasonably superior to the present methodology. This count has 93.1% recall rate, and average time is 4.12 s, for English content.
Data Aggregation Privacy in WSN

Abstract When WSN is applied to monitoring, the privacy of monitoring data from monitoring objects becomes an important issue for the successful application of WSN, which requires effective privacy protection for data aggregation. CPDA (Cluster-based Private Data Aggregation) has a problem that energy costs become higher with increase of nodes within a cluster. In this paper, UCPDA (Upgrade Cluster-based Private Data Aggregation) is established to perform data aggregation while preserving data privacy. According to required privacy, the sensors in a cluster are partitioned into some groups. Data preprocessing is performed only in the same group. Compared to CPDA, UCPDA has lower energy consumption for required privacy.
A Specific Compression Algorithm Used for Data Acquisition of Focal Plane Arrays

Focal plane arrays (FPA) fabricated with the state-of-the-art technology are widely used in many fields such as target detection and three-dimensional imaging, but how to handle the big data acquired by FPAs is a challenging problem. Based on the statistical distribution of output values of FPA, a specific compression algorithm is presented in this paper. By setting the template, mapping original values to symbols and adopting the lossless Huffman coding, original data can be significantly compressed. Experiments were performed to verify the performance of the proposed algorithm, resulting in compression ratios ranging from 33.4% to 37.3% and mean squared errors ranging from 2.59×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9</sup> to 1.99×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> . To the best of our knowledge, this is the first study on the compression algorithm for big data acquired from FPAs.
HISMA: A Human-Machine Iterative Schema Matching Algorithm

The rapid development of the Semantic Web has led to the emergence of knowledge models in the form of ontology. The schema matching algorithm maps relational databases (RDB) to ontology models, which is an important method to solve the lack of ontology model instance data. However, existing schema matching algorithm is not compatible with the localization features of the data source and does not take full advantage of the results of manual validation. In this paper, we analyzed the localization features of the data source and modeled the manual validation process. We proposed a human-machine iterative schema matching method. This method utilizes the matched result in the schema matching process to optimize the matching algorithm, therefore to reduce the workload of manual validation. We used a real-world data set to evaluate the effectiveness of the algorithm.
IoT in effective distance learning process

IOT in distance learning and self education allow to increase the efficiency of studying up to 20% more. During the video lesson the IOT devices are monitoring the level of the brain activity and sending the feedback to the software program. Digital image and video stream are modulated with the signals which switch the brain into the active state. At the periods of tiredness or/and the important moments of the lecture course these signals are keeping the brain in active state and allow to increase the efficiency of memorisation and boost up the overall learning process.
Exploiting Pose Mask Features For Video Action Recognition

Existing state-of-the-art approaches in video action recognition mostly adopt RGB images and optical flows as input and neglect the abundant information provided by human poses. In this paper, we present a novel method that highlights the essential area of human body and takes it as complementary input for existing action recognition pipelines. The method, called Pose Mask Network (PMN), leverages a 2D pose estimator to extract heatmaps from frames and utilizes them as Pose Masks on the original images. The Pose Masks are robust to the variance of background and focus on key information of human body. Experiments show that our Pose Mask yields a result far exceeding that of using simple pose representations. More importantly, PMN acts as a supplement to other RGB-based approaches. Combining our PMN with Temporal Segment Network, we obtain state-of-the-art performance on the HMDB51 and JHMDB datasets.
Real-Time Dynamic Gesture Recognition based on Boundary-Constraint Dynamic Time Warping

Natural human-computer interaction technology based on gestures has received broad attentions in many fields recently, which requires better performance of gesture recognition algorithms. Real-time gesture recognition is challenging due to the idea of dynamic programming. To address this issue, in this paper, we propose a real-time dynamic gesture recognition algorithm based on boundary-constraint dynamic time warping (BC-DTW). The proposed algorithm reduces the recognition time from the calculation times and the amount of DTW distance. To reduce the calculation times of varies DTW distance between the gesture sequences, the proposed algorithm transforms a gesture sequence from two-dimensional of unequal-length to one-dimensional of equal-length. Moreover, to find the paired range of each gesture data in a sequence during the process of DTW distance calculation, we propose a method based on Freeman chain to determine the boundary width of gesture data. The experimental results show that the proposed BC-DTW algorithm can speed up the recognition compared with the dynamic gesture recognition algorithm based on DTW.
Human Skeleton Control with the Face Expression Changed Synchronously

In this paper, we proposed a method of real-time human motion detection and skeleton control based on the depth image. For the human body part, we use Kinect to detect the original three-dimensional coordinates of the human joint; then smooth the joint movement; calculate the amount of rotation based on the smoothed data. Finally, the FBX file is used to drive the virtual mannequin. For the human face part, we use 3D modeling software to modify the FBX model and replace the head of the human model. With the help of the Kinect device, we combined the idea of linear blend skinning (LBS) to make the human body action effect more realistic. The experimental results show that the motion capture system can better recover the 3D skeleton motion of the captured real human body. Compared with other motion capture system, the motion control time of the human body is greatly reduced, and the 3D mesh and skeleton can be obtained immediately according to the detected human body motion. The modified FBX model can apply specific expression effects.
Control Performance Optimization of Variable Cycle Aero-engine Based on MTLBO

Abstract In view of the characteristics of variable cycle aero-engine with many adjustable variables, Modified Teaching-Learning-Based Optimization (MTLBO) algorithm is adopted to construct an optimization program to optimize the steady-state performance of multi-variable. Under the condition of non-afterburner single-duct with constant main fuel flow, the maximum thrust is optimized under the operation points of low altitude &amp; speed and high altitude &amp; speed, respectively based on MTLBO algorithm. The results show that the convergence of the optimization algorithm is stable, and the possibility of falling into local optimum can be avoided, which proves that the MTLBO algorithm can solve the performance optimization problem of variable cycle aero-engine.
An Annotation Method for Artwork Attributes Based on Visual Perception

In most of the existing online search systems of museums, only the professional knowledge keywords are used to retrieve the artworks. It is a challenge for either professionals or non-professionals. In this paper, we divide the attributes of an artwork into two categories: subjective and objective, and propose a method of attribute annotation for artworks based on the visual perception of the searcher or audiences, which makes the retrieval simpler and more suitable for either professionals or non-professionals.
Text Image Super-Resolution by Image Matting and Text Label Supervision

These days, many methods have been proposed to deal with nature image super-resolution (SR) and get impressive performance. However, these methods don't do well in text images SR due to their ignorance of the difference between nature images and text images. In this paper, we propose a matting-based dual generative adversarial network (mdGAN) for text image SR. Firstly, the input image is decomposed into text, foreground and background layers using deep image matting. Then two parallel branches are constructed to recover text boundary information and color information respectively. Furthermore, in order to improve the restoration accuracy of characters in output image, we use the input image's corresponding ground truth text label as extra supervise information to refine the two-branch networks during training. Experiments on real text images demonstrate that our method outperforms several state-of-the-art methods quantitatively and qualitatively.
Storage design of Marine ship inspection data based on cloud platform

With the development of cloud platform technology, the storage mode of Marine ship inspection data is gradually shifting from offline to online. The massive files generated by ship inspection are mainly small files, and it is difficult to cope with the application of the traditional storage mode. This paper introduces the basic architecture of Marine inspection system based on cloud platform, analyzes the fault tolerance of Hadoop framework and HDFS, and analyzes the characteristics of Marine inspection data. Aiming at the two problems of Hadoop's excessive storage of NameNode memory when storing such massive data, and the low efficiency of file reading and writing, the traditional HDFS storage process was optimized, and a storage and retrieval scheme for small files in Marine ship inspection was proposed. The rationality of the scheme was verified by experiments.
Life Science Immersive System Based on Virtual Reality

This paper presented a virtual reality experience system based on the theme of human body and cold virus struggle. In order to construct a realistic human cell scene, we applied physic-based rendering game production and Unreal® Engine. The proposed project used virtual reality, combining educational theory. Plentiful functionalities of virtual system were discussed in detail. As such, users could well participate in a war of cells to against virus from different perspectives. It is thought that users can become active learners, with they might well taking the initiative to acquire scientific knowledge and summing up their thinking in a lifelike experience, ultimately cultivating the ability of exploration and innovation.
Weaponization OFAI

It is the science and designing of Artificial knowledge including two fundamental thoughts. Also AI majorly signifies "Computerized reasoning”.It is the study of computerizing insightful conduct which could be achieved by individuals. AI secures information, handles information utilized by the administrator, and control of remote gadgets that are the essential building pieces of all present-day utility control frameworks. In this paper application and implementation of Artificial Intelligence in weapons has been discussed. Weaponization of AI has come as an enhancement for the Armed Forces around the world.
Introducing Kosh, a Framework for Creating and Maintaining APIs for Lexical Data

In recent years, the use of application programming interfaces (APIs) throughout the Internet has increased significantly. The main reason for this growth is the multiplicity of scenarios where APIs can be employed. In the case of APIs for lexical data, their use varies from applications for mobile devices, desktop applications to natural language processing (NLP) applications, among others. While some publishers offer their data via APIs, for most small or medium size publishers implementing and providing an API is still an obstacle due to the costs and technical expertise required for their development and maintenance. Against this background, we have developed Kosh, an open-source framework for creating and maintaining APIs for lexical data. Kosh has been conceived to minimize the technical expertise required for its use, while offering generic, flexible and efficient data management. In this article, we present the methodology employed in Kosh’s development and describe its architecture and functionality.
Content Vs Context: How About "Walking Hand-In-Hand" For Image Clustering?

Image clustering has been one of the most important issues in the field of pattern recognition. However, most of existing methods only focus on utilizing either content or context information of images, failing to consider both of them. In fact, the powerful algorithms can be realized by a combination of the rich content and context information. This paper proposes a novel content-context information bottleneck (C2IB) algorithm, which simultaneously explores and exploits the content and context information for discovering image clusters. The "content" describes the intrinsic characteristics contained in each image such as the appearance feature, and the "context" depicts the close correlations between images such as inter-image distance or similarity. Then, we formulate the problem as an information loss function by maximally preserving the content and context information while compressing the images. Finally, we design a new sequential method for the optimization. Experimental results show the superiority of the proposed method.
A Theory for Optimizing Insecure Internetwork Connections

While internetwork connections among private and public networks are inevitable and essential for enhancing the data rate and range of communication among networks, they introduce new vulnerabilities and risks of security breach to the interconnected networks. In this article, a novel information-theoretic model is proposed to analyze the tradeoff between network security and data-rate enhancement considering homogeneous and heterogeneous internetwork connections between two networks. Various patterns of internetwork connections among gateway nodes are studied. The implications of internetwork connections on data manipulation due to security breach by attackers are investigated by modeling the internetwork connections as a noisy communication channel. Specifically, the fundamental limit of the maximum achievable capacity afforded by internetwork connections is characterized, while considering the impact of security vulnerabilities that the internetwork connections introduce. For a nominal data-rate below the fundamental limit, the effect of security breach can be diminished to an arbitrarily low level by channel coding. Notably, any nominal data-rate above the fundamental limit is not achievable; hence, a packet-loss distortion is introduced to keep the data-rate below the fundamental limit. Moreover, the fundamental limit is used to find the optimal number of internetwork connections between two networks under prescribed data-rate and packet-loss distortion constraints.
The Fractional Quaternion Fourier Number Transform

In this paper, we define a fractional version of the quaternion Fourier number transform (QFNT). With this purpose, we first study the eigenstructure of the QFNT; this is used to obtain the eigendecomposition of the corresponding transform matrix, from which the fractional QFNT can be computed. A multiple parameter version of this transform is then employed to perform encryption of color images. Preliminary results suggest that the proposed scheme is secure, while allowing to deal with all color channels in a holistic manner and take into account possible interactions between them.
Transfer Learning from News Domain to Lecture Domain in Automatic Speech Recognition

Nowadays Automatic Speech Recognition (ASR) is increasingly developed, including in the lecture domain. ASR that build from scratch requires very large data, so it can use another approach, transfer learning. Transfer learning is an approach to building models by utilizing existing models as source models. This experiment begins with data collection on the lecture of the Informatics Undergraduate Program in ITB. We used spontaneous language models on the news domain as source models. We divided into three systems, some systems use the news domain, lecture domain, and both of them. In all three systems, the acoustic model used was triphone GMM-HMM and MAP which was only on the 3rd system. The language system uses the N-gram and LSTM with the projection layer. Transfer learning is implemented both in N-gram interpolation and weight initialization LSTM model. The news domain system gives WER score 78.30% (5-fold) and 85.18% (10sp), the lecture domain system is 58.232% (5-fold) and 62.18% (10sp), and the transfer learning system 52.734% (5 -fold) and 67.0 (10sp). The best ASR for the lecture is the system B, but the transfer learning approach gives better result if the acoustic condition from both the test and train data are same. We also conducted topic-specific modeling in the proposed system that divided all courses into 6 clusters and added the text corpus for LM to observe the effect of reducing scope. The experiment shows that OOV is correlated with WER and reducing the data scope not always improve the performance.
Image Splicing Forgery Detection Using DWT and Local Binary Pattern

The broad use of high-performance tools for image acquisition and strong image processing software has made it easy for malicious purposes to manipulate images. Image splicing, which posed a threat to image integrity and authenticity, is a very popular and easy image forgery trick. Therefore, detection of image splicing is one of the major problems in digital forensics. A new passive (non-intrusive) image tampering detection technique is proposed here to detect splicing forgery based on discrete wavelet transform (DWT) and local binary pattern (LBP). First, input image is converted into YCbCr channels, and then, chroma channels are used as input image for feature extraction using 5-bin histogram and 3-CF moments from DWT domain. Then, ensemble classifier is used for detection of spliced and authentic images.
Depth Estimation from a Single Image using Multi Stream and Scale Deep Learning

In this paper, we propose a method to estimate a depth map from a single image by combining the strengths of multi-scale and multi-stream deep neural networks. In the first scale, we use an encoder to allow the system to learn two types of inputs: an RGB image and image patches based on their superpixel. The second network (decoder) is accomplished to get a fine depth map by propagating the previous output with those corresponding RGB image. By combining an RGB image and superpixel patches, we can achieve a reliable feature depth map in the encoder network with a small number of training data. Consequently, we can enhance the ability of the refining network to predict the final depth. The effectiveness of our method is shown by real image experiments.