Reviewerly/exHarmony huggingface dataset:
Estimating and Factoring the Dropout Induced Distribution with Gaussian Mixture Model

The analytical method to capture the dropout induced distribution of forwarding output in a neural network as Gaussian mixture model (GMM) was proposed. In dropout Bayesian DNN, if the network is dropout-trained and a test data is dropout-forwarded for inference, then its output, usually approximated as a single mode Gaussian, becomes a posterior whose variance tells uncertainty of its inference [1]. Here, the proposed method can capture the arbitrary distribution analytically with high accuracy without Monte Carlo (MC) method for any network equipped with dropout and fully connected (FC) layers. Therefore, it is applicable to the general non-Gaussian posterior case for a better uncertainty estimate. The proposed method also has the advantage to provide a multimodal analysis in distribution by factoring which can be tuned with a user defined expressibility parameter while a MC estimate provides only a “flat” image. This helps to understand how the FC layer tries to code a dropout injected highly multimodal data into a single mode Gaussian while the unknown data becomes a complicated distribution.
Experiments on Design of Obstacle Avoiding Robots Based on Sensors, Bluetooth, and IoT

The mobile robotics industry is related to creating mobile robots that can move around in physical environments. Different types of mobile robot designs for obstacle avoidance have been experimented in the past based on different sensors, trajectory algorithms, etc. The chapter presents implementation details of different obstacle avoiding robots (OARs) using sensors, Bluetooth module, and IoT modules. The sensor-based obstacle-avoiding robots are designed using ultrasonic sensors and Arduino microcontrollers. Bluetooth-based obstacle-avoiding robots have been designed using Arduino mega and Bluetooth module and an Android application. IoT-based obstacle-avoiding robots can be designed in three different ways, using ethernet shield, node MCU, or Raspberry Pi. The IoT-based obstacle-avoiding robot using Raspberry Pi is the most popular mobile robot model that uses maximum on-chip modules in comparison to other designs, and also, the design can be extended by using cameras to use images for sensing the objects in order to avoid collisions.
Research on Application of Face Recognition Techniques in Social Networks

Abstract The progress in information technology makes social networks much more compact between people from all over the world. In the age of Internet, many new social interaction ways become more and more prevalent such as instant messaging, Web conference. These tools help people share their ideas and cooperate. However, in the meantime, there exist some risks because the person you are chatting with may just be a fake one. In this situation, the face recognition techniques could help identify the truth and make the social networks safe. In this paper, research is conducted on application of face recognition techniques into daily social networks. By combining robust face recognition techniques into our social networks, we can better discriminate risks on the Internet and make full use of social networks on academics, business or other issues. At first, the basic concept of social networks is introduced. Afterwards, the state of the art in the field of face recognition is described. Finally, some suggested ways are given on how to apply face recognition techniques into our social networks.
HE3: A hierarchical attribute based secure and efficient things-to-fog content sharing protocol

Internet of Things(IoT) based systems and applications are nowadays being enabled by paradigm of edge or fog computing. Central authority controlling the system formulates the policy about which fog device will get data from which IoT device/s. As fog devices have attributes like physical location, storage capacity, computing capability etc, attribute based access policy can be enforced on each IoT data. Ciphertext Policy – Attribute Based Encryption (CP-ABE) is a method to implement the access policies. However, number of access trees becomes equal to the number of access policies. In this paper, hierarchy present in attribute sets of access policies has been exploited to obtain single integrated access policy. A content sharing protocol named Hierarchical Efficient Encryption at Edge (HE3) is worked out to implement the concept. The proposed protocol is demonstrated to be highly efficient in terms of encryption and decryption time as compared to existing approaches. A formal security proof is provided to establish strength of encryption.
Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms.
Tag-Cloud Based Recommendation for Movies

Most of the recommendation systems aim to make suggestions for individuals rather than a group of users. However, people are sociable and most of the items to be recommended like movies, restaurants, tourist destinations, etc. are for group consumption. Making recommendations for a group is not a trivial task due to the diverse and conflicting interests of the group members. In this paper, we present a framework for recommending movies to a group of users. Existing recommendation systems for movies use users’ ratings as a measure to suggest individual recommendations or use them to generate the group profile by using aggregation methods in case of group recommendations. In this work, we focus on two things: exploiting the tags assigned to the movies by the users and leveraging the semantic information present in them to make recommendations. The assigned tags along with their weightages are used to form tag clouds for individual group members as well as for movies. Following this a Group Score is computed for each movie on the basis of the content similarity of the tag cloud of the group and the tag cloud of the movie. The movies having top-N Group Score are recommended to the group. To verify the effectiveness of this framework, experiments have been conducted on the MovieLens-10M and MovieLens-20M datasets. Results obtained clearly demonstrate how the accuracy of the recommendations increase with the increase in the homogeneity of preferences within the group members.
Supporting Transparent Information/Knowledge Federation in Collaborative Administrative Environments

Leading edge ICT facilitates obtaining and interoperating information within collaborative networks (CNs), providing the base to tackle more advanced challenges. The paper addresses provision of transparent federated information/knowledge within administrative CNs. We introduce a methodology and mechanisms for incremental ontology development. The paper first identifies four typical sources of information/knowledge at the organizations involved in targeted emerging CNs, including: (i) database schemas, (ii) mission statements and main application scenarios, (iii) textual communications, and (iv) governance policies. It then introduces a systematic methodology to develop their meta-data and unify them into an ontology. This methodology consists of four semi-automated steps to gradually develop and enhance an ontology for the environment. The paper describes and exemplifies these steps and their mechanisms. An example real emerging case in the field of higher education administration in China is presented to serve as the proof of concept and verification of our proposed solution approach.
PCI: Principal Component Initialization for Deep Autoencoders

An autoencoder (AE) is one of the important neural network methods for dimensionality reduction problems. Unfortunately, however, deep AEs have the drawback in trainability which often makes obtaining a good performance a difficult task owing to their model complexity. This paper proposes a simple weight initialization algorithm called the principal component initialization (PCI) method to improve and stabilize the generalization performance of deep AEs in one shot. PCI uses orthogonal bases of the original data space obtained with principal component analysis and transposed ones as initial weights of the AEs. The proposed method significantly outperforms the current de facto standard initialization method for image reconstruction tasks.
Boosting Reinforcement Learning with Unsupervised Feature Extraction

Learning to process visual input for Deep Reinforcement Learning is challenging and training a neural network with nothing else but a sparse and delayed reward signal seems rather inappropriate. In this work, Deep Q-Networks are leveraged by several unsupervised machine learning methods that provide additional information for the training of the feature extraction stage to find a well suited representation of the input data. The influence of convolutional filters that were pretrained on a supervised classification task, a Convolutional Autoencoder and Slow Feature Analysis are investigated in an end-to-end architecture. Experiments are performed on five ViZDoom environments. We found that the unsupervised methods boost Deep Q-Networks significantly depending on the underlying task the agent has to fulfill. While pretrained filters improve object detection tasks, we find that Convolutional Autoencoders leverage navigation and orientation tasks. Combining these two approaches leads to an agent that performs well on all tested environments.
Password Strength Measurement without Password Disclosure

As a mechanism for promoting improvement in the strength of the user password, there is a mechanism that measures the password strength and gives feedback to the user. There are a wide variety of current strength measurement methods, and there are also methods that transmit a password during input to the remote server to perform strength measurement. However, the threat of sending passwords externally during input has not been sufficiently discussed. In this paper, we first survey the current password strength measurement method, and clarify how much remote side strength measurement exists. Then, the threat of remote strength measurement is organized, and the need for its protection is indicated. The necessity of the method of measuring the password strength without disclosure as the protection method is described, and three approaches are shown. Furthermore, the feasibility of each approach is discussed, and the prototype with the highest feasibility was developed. Moreover, we evaluate the performance and usability of the prototype system. As a result, although basic performance changes depending on system configuration, the result of the user study shows that the usability is not low, and the proposed method is sufficiently practical while reducing the threat.
One-error correcting [5, 2] ternary codes

In this paper, using equivalence, we classify the error correcting linear codes of length 5 and dimension 2 over GF(3). We are able to show that these codes can be partitioned into 3 mutually disjoint equivalence classes. In the final section, we compute the weight distributions of the of the codes belonging to various distinct classes. We did similar investigations for [5,2] codes over GF(4) in [1].
Real-Time Monitoring of Mine Landslide based on Gaussian Mixture Model

With the rapid development of the economy, large-scale infrastructure construction requires a large amount of exploitation of resources. How to ensure the safe mining of mineral resources is an increasingly important topic, and landslides are the most serious category of mine accidents. Affected by complex geological conditions and topography, large-scale landslide accidents have occurred in various places, causing huge losses to the mines. Early warning and forecasting of mine slopes is one of the important measures for disaster reduction and prevention. At present, the methods of landslide monitoring have problems such as low automation and low efficiency.
Dynamic Background Video Forgery Detection using Gaussian Mixture Model

Video as evidence holds an important place in a court case and therefore the integrity of video must be proven. Various studies had been done in video forensics and most of them is only focused on a certain type of forgery, such as histogram correlation analysis that only focused on detecting temporally forged videos with static background. Improving histogram correlation analysis with foreground detection using Gaussian mixture model makes it possible to detect spatially forged videos and further improves its accuracy in detecting dynamic background video. Applying proposed method will yield 20.83% improved detection's accuracy, 34.42% increased localization's precision, and 39.17% increased localization's re-call. Furthermore, object's definition introduced by foreground detection will also open up new possibility to detect spatially forged video.
Hierarchical Cluster-Based Adaptive Model for Semi-Supervised Classification of Data Stream with Concept Drift

Compared with the research of data stream with concept drift in supervised environment, the work that in semi-supervised environment is more challenging. There is currently very little work in this area, although more meaningful. Considering existing chunk-based processing algorithms are only suitable for periodic concept drift and show poor performance in complex concept drift scenarios, such as concept drift may occur at any time, the duration of each concept is not exactly the same and multiple different types of concept drift types may alternate or mixed at the time. In this work, we propose an online plus offline memory model for processing streaming data in online learning mode. Compared with chunk-based incrementally processing mode, our algorithm does not have to set-up chunk size and model is maintained online. Concept drift processing mechanism is triggered at interval to extract knowledge and clean samples of local area. Therefore, it has more adaptability to complex concept drift scenarios. Extensive experiments on benchmark artificial and real-world datasets shows that our algorithm achieves significantly higher or at least comparable accuracy and behaves more stable in comparison with state-of-the-art cluster-based techniques.
path tracking of snake-like robot based on neural network identifier

Due to its under-actuating characteristics, the snake-like robot has become a research hotspot and a difficult point. Based on the model established by Pal Lilebak, a linear path tracking algorithm is proposed, which is based on neural network identifier and can automatically search PI parameters. At the same time, the line-of-sight guidance law with variable forward distance is used to speed up the side shift and track the target straight line. More over the algorithm is extended to curve path tracking. The simulation results show that this path tracking algorithm can stabilize the control system and speed up the tracking of the direction angle by dynamically adjusting PI parameters. The proposed line-of-sight guidance law can significantly accelerate the side shift speed. The result also show that the neural network identification control can avoid the problem that PI control will be unstable due to the change of the forward distance. And the path tracking algorithm also performs well in curve path tracking.
Hybrid data-driven and context-aware activity recognition with mobile devices

We have designed and implemented a real-time hybrid activity recognition system which combines supervised learning on inertial sensor data from mobile devices and context-aware reasoning. We demonstrate how the context surrounding the user, combined with common knowledge about the relationship between this context and human activities, can significantly increase the ability to discriminate among activities when machine learning over inertial sensors has clear difficulties.
Semantic Segmentation via Structured Refined Prediction and Dual Global Priors

Deep residual network and multi-scale context module become two key ingredients in recent researches which made great progress in semantic segmentation tasks. The multi-scale features utilized by these networks are also proven to be effective in refining object boundaries. However, regular convolution kernels are inherently limited to geometric transformation due to the fixed structure. Additionally, when integrating multi-scale information in boundary detection, the capability of global attention starts to degrade and the problem of inconsistent intra-class segmentation will occur. To solve above problems, we propose two novel architectures dubbed as Deformable Residual Boundary (DRB) module and Dual Global Prior (DGP) module. The DRB module can learn more refined boundary information from non-rigid geometric structures by deformable convolutions. The extracted information will be further embedded into the branches of an encoder-decoder architecture to handle the deformation of objects. For multi-scale fusion, the DGP module utilizes global priors to enhance the global attention, which alleviates the inconsistent intra-class segmentation problem especially when dealing with object surfaces of different textures/resolutions. We achieve a mean IOU value of 80.3% without coarse set, outperforming state-of-the-art approaches. When training with the coarse set, the final mIOU of the proposed method is 80.9%, also competitive to other approaches.
Augmenting the Power of Artwork Through a Virtual Reality Partnered Piece

This paper seeks to challenge the existing state of 'augmented reality' within the visual arts.Currently the majority of augmented reality applications are little more than gimmicks viewed through a mobile phone or tablet.The augmentation is generally limited by the hardware restrictions, and even when using high end equipment such as the Microsoft HoloLens the reduced field of digital overlay leads to a weakened immersive experience.Working alongside various artists and Anise Gallery A-VR have pioneered the 'partnered piece' concept through the creation of bespoke curated VR (Virtual Reality) experiences which accompany the original physical artwork.The VR pieces themselves can exist as standalone experiences but when viewed alongside related artwork they increase the power of the whole tremendously.Focussing on a number of example projects this paper charts our investigations into producing the most immersive additive experience to physical artwork.The implementation of such a virtual reality partnered piece has influenced and refined the language of our VR production.The strength of the immersive experience often having profound emotional effects on the viewers and demonstrates the power of a truly 'augmented' creative experience.
Semantically Secure and Verifiable Multi-keyword Search in Cloud Computing

In cloud computing model, the data are usually encrypted before outsourced to the cloud server, which protects the data privacy, but also leaves keyword searches over ciphertext data a challenging problem. A keyword search scheme over encrypted data should achieve both index privacy and query privacy; moreover, verification of search results is desirable because the incorrectf results can be returned owing to system defects or the cloud server’s motivation to save computation recourses. Many multi-keyword search schemes have been proposed; however, few of these schemes are verifiable and adaptively index-hiding and adaptively query-hiding. In this paper, a semantically secure multi-keyword search scheme is constructed, which is adaptively index-hiding and adaptively query-hiding, also supports the correctness verification of search results. We provide a detailed performance comparison and give a thorough security proof by a sequence of games. The combined results demonstrate that our scheme is secure and practical.
A New Image Dataset for Document Corner Localization

Nowadays, the capabilities of smartphones rise, they have become an important part of people’s lives. It can do many tasks and the advantage is that it is often with us. There are many unsolved problems in document digitalization with smartphones, such as noisy images, blurring, non-uniform light and geometric transforms. The presented methods have tried to fix one or more problems and make it easier or faster to find the document in the image. However, they cannot cover difficult situations such as complicated background. In this paper, we present a new dataset covers almost all the scenarios that may exist on document images that were taken by a smartphone. The collection includes 1111 images. We tested algorithms for finding the documents corners in our dataset and the results also provided. The results indicate that there are still situations that these algorithms fail and it needs more research.
Cryptanalysis of the Columnar Transposition Using Meta-Heuristics

Abstract The most commonly used methods for solving classical (historical) ciphers are based on global optimization (meta-heuristic methods). Despite the fact that global optimization is a well-studied problem, in the case of classical ciphers, there are still many open questions such as the construction of fitness functions or efficient transformation of the cryptanalysis (breaking attempt) to an optimization problem. Therefore the transformation of a cryptanalytical task to an optimization problem and the choice of a suitable fitness function form an important part of the topic. In this paper, we focus on the simple columnar transposition in depth. Our main contribution is a detailed analysis and comparison of different fitness functions, fitness landscape analysis and solving experiments.
A Study on Innovative Design of Rotary Pile Foundation Drilling Machine Based on TRIZ Theory

Whereas such problems as small borehole diameter and low efficiency occur in the operation process of rotary pile foundation drilling machine, this paper puts forward an innovative design method based on the TRIZ theory and comes up with the detailed solution to the conflicts, through the use of the contradiction conflict solution matrix comprised by 39 engineering parameters and 40 pieces of inventive principles pertaining to TRIZ, in the aspects of borehole diameter enlargement and operation efficiency improvement. Furthermore, this paper has manifested the feasibility to conduct TRIZ-theory-based innovative design in the field of engineering machinery, thus providing important theoretical tools for design ideas in the field of traditional engineering innovation.