Reviewerly/exHarmony huggingface dataset:
Barrage Video Recommendation Method Based on Convolutional Recursive Neural Network

Aiming at solving the problems of traditional video recommendation process, such as low time efficiency and low accuracy, this paper proposes a convolutional recursive neural network video recommendation method based on barrage. Firstly, according to the number of barrage in video, the method selects the preferable video fragments of users, and adopts the k-means clustering method to extract key frames. Secondly, we construct a convolutional recursive neural network model (RCNN) to classify the similar video fragments. Finally, the recommendation can be achieved by the similarity between video fragments. The experimental results show that the proposed recommendation method improves the accuracy of recommendation by 0.22 compared with CTR, 0.18 compared with CDL, and 0.31 compared with ConvMF. So it improves the accuracy of recommendation in a certain extent.
Privacy and Robust Hashes

Within a forensic examination of a computer for illegal image content, robust hashing can be used to detect images even after they have been altered. Here the perceptible properties of an image are used to create the hash values.Whether an image has the same content is determined by a distance function. Cryptographic hash functions, on the other hand, create a unique bit-sensitive value. With these, no similarity measurement is possible, since only with exact agreement a picture is found. A minimal change in the image results in a completely different cryptographic hash value. However, the robust hashes have an big disadvantage: hash values can reveal something about the structure of the picture. This results in a data protection leak. The advantage of a cryptographic hash function is in turn that its values do not allow any conclusions about the structure of an image. The aim of this work is to develop a procedure for which combines the advantages of both hashing functions.
Semi-supervised Depth Estimation from Sparse Depth and a Single Image for Dense Map Construction

For robust navigation, the objective in visual SLAM is to create a dense map from a sparse input. Although there have been a significant number of endeavors on real-time mapping, the existing works for visual SLAM systems still fail to preserve adequate geometry details that are important for navigation. This paper estimates pixel-wise depth from a single image and a few depth points which are constructed from registered LiDAR or acquired from visual SLAM systems to construct a dense map. The main idea is to employ a set of new loss functions consisting of photometric reconstruction consistency (forward-backward consistency and left-right consistency), depth loss, nearby frame geometric consistency, and smoothness loss and propose a depth estimation network based on ResNet. The experimental results show that the proposed method is superior the state-of-the-art methods on both raw LiDAR scans dataset and semi-dense annotation dataset. Furthermore, the errors of the sparse depth produced by stereo ORB-SLAM2 are evaluated and this sparse depth and a single image are fed into the proposed model to further demonstrate the superiority of the proposed work.
Discriminative Training of Conditional Random Fields with Probably Submodular Constraints

Problems of segmentation, denoising, registration and 3D reconstruction are often addressed with the graph cut algorithm. However, solving an unconstrained graph cut problem is NP-hard. For tractable optimization, pairwise potentials have to fulfill the submodularity inequality. In our learning paradigm, pairwise potentials are created as the dot product of a learned vector w with positive feature vectors. In order to constrain such a model to remain tractable, previous approaches have enforced the weight vector to be positive for pairwise potentials in which the labels differ, and set pairwise potentials to zero in the case that the label remains the same. Such constraints are sufficient to guarantee that the resulting pairwise potentials satisfy the submodularity inequality. However, we show that such an approach unnecessarily restricts the capacity of the learned models. Guaranteeing submodularity for all possible inputs, no matter how improbable, reduces inference error to effectively zero, but increases model error. In contrast, we relax the requirement of guaranteed submodularity to solutions that are probably approximately submodular. We show that the conceptually simple strategy of enforcing submodularity on the training examples guarantees with low sample complexity that test images will also yield submodular pairwise potentials. Results are presented in the binary and muticlass settings, showing substantial improvement from the resulting increased model capacity.
Obstacle Avoidance Using Stereo Vision and Deep Reinforcement Learning in an Animal-like Robot

Obstacle avoidance is a fundamental behavior required to achieve safety and stability in both animals and robots. Many animals perceive and safely navigate their environment using two eyes with overlapping visual fields, allowing the use of stereopsis to compute distances to surfaces and to support collision avoidance. In this paper we develop an obstacle avoidance behavior for the biomimetic robot MiRo that combines stereo vision with deep reinforcement learning. We further show that avoidance strategies, learned for a simulated robot and environment, can be effectively transferred to a physical robot.
Cast Search via Two-Stream Label Propagation

We address the problem of Cast Search by Portraits (CSP) where a facial portrait of a cast member is provided to retrieve from a given video clip those frames containing the query target. The underlying CSP formulation is related to the task of person re-identification. However, CSP is more challenging in that the provided query image is only a portrait of a certain cast member, and the instances of the target in the candidate video could have a very different visual appearance. Such drastic visual variations are not common in addressing the problem of person re-id. We propose a two-stream network architecture for tackling the CSP challenge and also participate in the public CSP competition. The overall outcome in the competition is promising and worth further effort to improve our proposed model.
Extraction Method with Word Distribution Enriched Deep Residual Network

As a core task and important part of information extraction, relation extraction identifies the semantic relation between entity pairs. It plays an important role in semantic understanding of sentences and the construction of knowledge graphs. Most of the existing methods for relation extraction rely on semantic information. Furthermore, many word embedding models do not take position information into considerations. In this paper, combining with word vector representation of word embedding and words' positions, a word distribution model is proposed. It is used as the input of Residual Neural Network to train the classifier for relation extraction and Adversarial Training method is employed to reduce the impact of noise labels in training phase. The experimental results demonstrate the effectiveness of the proposed model on several datasets.
Estimation of Forearm Pose Based on Upper Arm Deformation Using a Deep Neural Network

Activities of body tissues such as muscles, tendons, and bones appear in the deformation of skins. It would be possible to obtain the information of the body motions by measuring the skin deformation. In our research, we focused on upper arm deformation, which is changed with not only the elbow flexion and extension but also the forearm pronation and supination. Thus, with the measurement of the upper arm deformation, we can estimate the elbow pose. In this paper, we propose a forearm pose estimation method based on upper arm deformation. A distance sensor array with eight sensor units is developed to measure the upper arm deformation. From the measured deformation, a deep neural network (DNN) based model is trained and used to estimate the forearm pose. The results of experiment show that our proposed method was able to estimate the forearm pose with RMSE of 2.9 degrees for the elbow joint angle and 7.6 degrees for the forearm pronation and supination angle.
A Novel Local Feature Extraction Algorithm Based on Gabor Wavelet Transform

Aiming at the problem that the feature extraction method based on Gabor wavelet transform makes the feature vector dimension higher, a novel method named GCLBP (Gabor-CSLBP) is proposed in this paper. Based on Gabor wavelet transform, the proposed algorithm is a local feature extraction method, which extracted a new kind of feature through applying the idea of CS-LBP (Center-Symmetric Local Binary Pattern) into the resulted sub-images of Gabor transform. The feature vector obtained by the GCLBP method combines the advantages of Gabor wavelet transform and CS-LBP, which not only reduces the dimension of the feature vector, but also improves the robustness of image variation. The proposed method is evaluated by extensive experiments on benchmark databases CMU PIE, and Extended Yale B. The experimental results show that the proposed method -- GCLBP, can significantly improve the face recognition rate under complex illumination.
Some notes on the binary sequences of length 2<sup>n</sup>– 1 with the run property

In this paper, we calculate the number of binary sequences of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> - 1 that satisfy the run property, called run sequences. We know that only a small portion of those run sequences satisfy the span property. In this paper, in addition, we present some interesting properties of those run sequences with or without the span property.
A framework for sticky note information management

Abstract This chapter examines how sticky notes can be used for information management, beyond the writing or drawing of informational content on the notes themselves. Based on a field study at an international design company, we present the 4C framework – a framework for analyzing how the physical aspects of sticky notes can be used for information management: Through the physical process of writing or drawing sticky note content, through the physical characteristics of sticky notes themselves, through the spatial configuration of sticky notes in relation to other sticky notes, and through the physical context in which the sticky notes are placed. To demonstrate the 4C framework, we present analysis of three examples of sticky note clusters, and follow our analysis with a discussion of the insights gained by doing so, the challenges faced in building the framework, and the strengths and limitations of the framework.
A Co-Random Walks Segmentation Method for Aerial Insulator Video Images

Insulator segmentation is an important premise of automatic state detection and fault diagnosis in image processing. The aerial insulator images are characterized by complex background, low resolution, large number and many pseudo targets. The classical random walks algorithm may segment wrongly. It requires massive interaction to segment multiple images, which makes user fatigue and results in bad segmentation quality. This paper proposes an automatic co-segmentation method called co-random walks, which utilizes the relationship between aerial insulator video images as prior information to find corresponding seed points in order to achieve higher segmentation accuracy. Firstly, we remove the texts in original images, the preprocessed images are over-segmented into super-pixels by SLIC (Simple Linear Iterative Clustering) for fast segmentation. Then the collaborative graph network is constructed, we use a greedy algorithm to get corresponding seed points. Finally, the random walks segmentation for corresponding seed points of each image is performed. The experimental results show that the proposed method can efficiently distinguish the insulator from complex background and eliminate the pseudo target like tower. We only need a few seed points to achieve relatively high accuracy of automatic segmentation, which is instrumental to unmanned aerial vehiclel aerial insulators' state detection and fault diagnosis.
Teaching machines to write like humans using L-attributed grammar

Reading and writing are easy for humans. The automatic reading of handwritten characters has been studied for several decades. Machine learning algorithms for reading tasks often require a huge amount of data to perform with similar accuracy to humans, yet it is also difficult to gain sufficient meaningful data. Automatic writing tasks have not been studied as extensively. In this paper, we teach machines to write like teaching a child by telling the machine the method for writing each character using L-attributed grammar. With the aid of the proposed TMTW (Teaching Machines To Write) interacting system, a human as a teacher only needs to provide the writing sequence of parts and control lines. The proposed system automatically perceives the relationships between control lines and parts, and constructs the grammars. Top-down derivation and the stroke generation method are applied to generate varying characters based on the learned grammars. For as long as a machine can write, it can be applied in robot control or training sample generation for automatic reading tasks. The MNIST and CASIA datasets are used to demonstrate the effectiveness of the proposed system on different languages. The machine written samples are used to train a network, which is evaluated on the MNIST test set. A test error rate of 1.23% is achieved using only approximately 20 grammars on average for each digit. Using the generated and handwritten samples together as a training set can reduce the test error rate to 0.61%. Similar experiments are conducted using the CASIA data set, and the results demonstrated that the proposed method is effective in generating characters with a complex structure. The source codes and grammars used in this paper have been made publicly available in https://github.com/step123456789/TMTW.
Hybridity in Japanese Advertising Discourse

Abstract The purpose of this paper is to reveal the impact of cultural exchange on Japanese advertising discourse construction perpetuated through the idea of prestige. This phenomenon can be explained through the concept of mukokuseki (Jap. 無国籍) ‘lack of nationality’, a term which encompasses the idea of transculturality in Asia. In the context of globalization, erasing the identity or any sort of national mark enhances the absorption and integration of foreign elements and leads to reconsidering aesthetic communication. Japanese advertising discourse is based more on emotional reactions rather than cognitive ones, and this can be noticed through the extensive use of kawaii ‘cute’ symbolism. Considering that the idea encompassed by the concept of “cute” is present in any culture, and its effects are more or less the same, by freeing it from any cultural or societal restraints ( mukokuseki ), it becomes an intermediary space where traditional, national elements can overlap foreign ones without damaging the essence of the discourse, but on the contrary. Interculturality and inbetweenness manifests not only at the visual level but also at the linguistic level through hybrid language use: the coexistence of gairaigo (loan words) along wago (words deriving from Japanese) and kango (words deriving from Chinese). According to several studies, gairaigo and wasei-eigo (Japanglish, English words coined in Japan) create a special effect and often serve as euphemisms. Thus, we have selected three Japanese print adverts (displayed between 2000 and 2012) in order to explain the phenomenon by focusing on their sociolinguistic function and their impact on discourse construction. Advertising discourse has surpassed its primary economic function and has come to be a statement of the global world.
he Innovative Education of “Smart Finance” under the Promotion of Educational Informationization

The rapid development of artificial intelligence has rapidly changed the pattern of higher education, the structure of talent demand and the mode of future learnings. Artificial intelligence talents should break through the traditional single-subject training mode and form a new model of “artificial intelligence + X” compound professional training in the cross-integration with related subjects. Not only programmers can be called digital talents, such talents who form professional insights and knowledge in specific areas and apply them to the digital field may be needed in the future. Teaching informatization is also of great significance for promoting the reform of the school's talent training model. For example, cross-subject, cross-major learning and informatization can help eliminating the boundaries of the department, and jointly build and share some cross-subjects majors, micro-professionals and MOOC, micro-courses, simulation experiments and other “online virtual classrooms”. Artificial intelligence has a strong impact on traditional accounting work, replacing the high repetitiveness in the financial process and the manual operation of occupying a large amount of labor, so that accounting teaching needs to be changed. Promoting the sharing and building of high-quality resources by teaching informatization and providing the soulful university classroom teaching of Chinese wisdom and Chinese programs for the balanced development of education quality to realize the return of learning. Advancing the financial transformation under the intelligent accounting provides a good idea for the accounting teaching. Advocating the implementation of the “learner-centered” educational thinking, paying equal attention to skills and thinking design, and using online courses realize the form of intelligent financial innovation education.
Automating Detection of Security-Related Software Engineering Failures

The software engineering discipline encompasses the techniques that are responsible for defining, designing and managing the development and lifecycle of software systems. Given this, the discipline has key responsibilities for preventing cybersecurity issues in code and software systems, more broadly. Despite goals in this area, numerous software products are released with defects. This paper proposes techniques for the automatic detection of failures of the software engineering process that cause security issues. This automatic detection process is designed to help prevent errors during the design and coding processes as well as to detect errors during product deployment and after release.
The Future of Kenyan Banks: Mitigate Financial Risk Using Cryptocurrencies and Blockchain Technology

The banking sector has undergone tremendous changes in the past decades. This paper seeks to investigate the future of Kenya banks by using cryptocurrency and blockchain technology to mitigate financial risk. A cryptocurrency performs the fundamental function of money, as a medium of exchange. The encryption and decentralization of digital currencies are the most important aspects regarding the applicability of Cryptocurrencies and Blockchain Technology in Kenya. Digital money supports individual investors as opposed to a dominant market player or authority. The fact that no single authority controls cryptocurrencies is the heart of its applicability in Kenya. Digital money is a unifying factor for the world markets defined by growing inequality and financial malpractices. The Kenyan banking system may take advantage of the smart contracts to address the myriad risks owed to the economic actions of the private and public parties. Banks can use the digital money to eliminate intermediaries that often constrain the capacity of the individual traders to enter into contracts. Cryptocurrencies operate on a user-to-user basis to enhance flexibility and control by the individual traders. The use of the technology can help the state to reduce the risk of loss owed to inaccurate authentication and valuation of assets.
Investigating the Impact of Time-Lagged End-to-End Control in Autonomous Driving

End-to-end training, the strategy by which deep neural networks learn to map raw pixels from front-facing cameras directly to steering commands have recently gained attention in the autonomous driving field. Here, we investigate the possibility of extending this approach with a time-lagged procedure, by training the system to map raw pixels at time T to steering commands at time T+Lag. We are interested in evaluating such an approach for two main applications: (1) time-lagged end-to-end control towards an artificial driving instructor (ADI) that recommends future control actions to novice human drivers; (2) time-domain data augmentation to improve the performance of standard non-lagged end-to-end control. Our results show that time-lagged end-to-end training is not appropriate for time-lagged control, but using it for data augmentation leads to a smoother output in standard non-lagged end-to-end control. This suggests that time-lagged training improves the anticipatory ability of end-to-end control and augmentation reduces overfitting.
Efficiency of quadrature Kalman filter within long range target tracking problem

In this paper, the long range target tracking problem is presented. Two versions of nonlinear Kalman Filters are used to bring out their performances, the first one is the Extended Kalman Filter EKF and the second one is the Quadrature Kalman Filter (QKF). The problem addressed is the target tracking with high cross-range that moves in plan. The position measurements are provided by radar in polar coordinates. The simulations results show that, in terms of Root Mean Square Errors (RMSE), the QKF filter performs better than the classical EKF.
Data citizens

Using William Shakespeare's quote as a theme, the premise of this chapter is that data democratization will emerge from the same forces witnessed in two other historical transformations: (a) the new form of American governance after monarch rule and (b) globalization resulting from information democratization. The question remains how our industrial and information economies will prepare for this transformation in data. One dimension of the solution lies in the formation of the data citizen. Democracy at once stirs up a notion of majority rule, but history reveals that the yearning for freedom—a pure democracy—is typically tempered with a desire for representation. For instance, although the Internet has given a voice to nearly anyone, the information consumer still looks to representative forms of journalism and information sources such as The Washington Post and Apple News to maintain “good order and discipline” in a global information cacophony. “How does an industrial economy prepare itself for the coming data democratization?” If past is prologue, it is worthwhile reflecting on how the American colonists learned to function as a democracy having lived under monarchy. By gaining their independence, they also accepted a responsibility in the governance of their country—no longer able to simply be governed by the ruling class. They transformed from subjects to citizens. A data citizen is an extension of this premise. As more businesses and management programs adapt concepts like data dexterity and data literacy, they highlight the growing need for data science management and data-adept leaders. A data democracy will not stand for a “ruling class.” Using a framework of characteristics drawn from the past, this chapter develops the rights and responsibilities of a data citizen.
Quantum error correction nested codes derived from jacket transform

A novel approach for construction of quantum codes employing jacket transform is proposed in this paper. The constructed quantum nested codes has less complexities than the previous quantum codes in encoding procedures as well as decoding procedures. Furthermore, the quantum error codes can be constructed with an arbitrary size using the number decomposition, and the corresponding distance of constructed quantum codes can be adjusted by changing decomposition methods.