Reviewerly/exHarmony huggingface dataset:
Cattle Disease Diagnosis Expert System in Bandes Village, Ainaro District

Most of the people in the Bandes village of Ainaro district work as breeders. Cows that are raised are beef cattle. One of the obstacles that exist in Bandes Village is the existence of a few veterinarians in Bandes Village. This research discusses an expert system for diagnosing diseases in cattle, which functions to help determine the disease that is being suffered by cattle, this system will display the results of the diagnosis in the form of the name of the disease, a description of the disease solution and its prevention. In this study there were 4 diseases studied and 7 symptoms. From the 4 disease data, isolation of the problem area, target decision and dependency diagram is made. After that the formation of IF – THEN rules, after the rules are made, the backward chaining process is made. So as to produce a solution to overcome disease in cattle. In program testing is done by comparing the results of the system with experts. In the trials that have been carried out, the results obtained are appropriate or the accuracy of the data and facts obtained from experts on cattle. So based on the results of tests that have been carried out on the system as much as 4 data, the accuracy value obtained is 100% accurate which shows that the expert system is functioning properly in accordance with the expert's diagnosis
Removing Dimensional Restrictions On Complex/Hyper-Complex Neural Networks

It has been shown that the core reasons that complex and hypercomplex valued neural networks offer improvements over their real-valued counterparts is the fact that aspects of their algebra forces treating multi-dimensional data as a single entity. However, both are constrained to a set number of dimensions, two for complex and four for quaternions. These observations motivate us to introduce novel vector map convolutions which capture this property, while dropping the unnatural dimensionality constraints their algebra imposes. This is achieved by introducing a system that mimics the unique linear combination of input dimensions via the Hamilton product using a permutation function, as well as batch normalization and weight initialization for the system. We perform two experiments to show that these novel vector map convolutions seem to capture all the benefits of complex and hyper-complex networks, while avoiding the dimensionality restriction.
The laws of the origin of culture

The paper, from a dialectical point of view, outlines the essence and main features of the formation, functioning and development of the laws of the emergence of culture. The substantiation is given that the laws of any science are the quintessence of its theoretical knowledge, since they reflect the content of all other aspects of science.
Piecewise Bézier Space: Recovering 3D Dynamic Motion From Video

In this paper we address the problem of jointly retrieving a 3D dynamic shape, camera motion, and deformation grouping from partial 2D point trajectories in a monocular video. To this end, we introduce a union of piecewise Bézier subspaces with enforcing continuities to model 3D motion. We show that formulating the problem in terms of piecewise curves, allows for a better physical interpretation of the resulting priors and a more accurate representation of the motion. An energy based formulation is presented to solve the problem in an unsupervised, unified, accurate and efficient manner, by means of the use of augmented Lagrange multipliers. We thoroughly validate the approach on a wide variety of human video sequences, including those cases with noisy and missing observations, and providing more accurate joint estimations than state-of-the-art approaches.
A Way of Generating True Random Number Based on FPGA and Gate Circuit Multi-vibrator

Random character is one of the most important characters of nuclear radiation, so random number is widely used in nuclear simulation and research. Random numbers are usually generated through some algorithms, so these kinds of random numbers are usually not true random ones. Moreover, the algorithms will consume a great deal of hardware resource and runtime of the signal processing system. To solve this problem, in this paper, a way of generating true random numbers based on FPGA and gate circuit multi-vibrator is put forward ingeniously. If the environment or the component parameters are unstable, the period and pulse width of vibrating pulse will be unstable. If this unstable vibration is used, random number can be generated. This way of random number generating is based on circuit instability caused of threshold errors, electronic noise and interference other than algorithm, so it consumes less hardware resource and the numbers are true random. The principle and the experiment result are analyzed. According to the analysis and experiment, a conclusion is drawn that the way of generating random number is feasible.
Comparison of Three Approaches to Class Enumeration in Growth Mixture Modeling when Time Structures are Variant Across Latent Classes

In conventional approaches to Growth Mixture Modeling (GMM), a trajectory is first estimated using latent growth curve modeling that serves as a baseline trajectory for the GMM. In this approach, time structures are held invariant across latent classes when identifying the number of latent classes. However, this popular way of conducting GMM could undermine a proper estimation, especially under the condition where a distinct trajectory exists for different classes in the population. This study compared the class enumeration performance in a conventional GMM against two alternatives in which latent classes do not take the same functional forms of change across time: (1) Unstructured Mixture Models (UMM) and (2) Latent Basis Models (LBM). Results revealed that the UMM performs well when one latent class takes a different shape of growth. Based on various design conditions, the relative performance of the three approaches in terms of class enumeration is examined and discussed.
Challenges and Countermeasures for the Construction of English Golden Course in Higher Vocational College in the Era of “Internet +”

"Internet +" has brought great challenges to higher vocational college of traditional Chinese medicine English teaching in terms of teaching concept, teaching methods, the role of teachers and teacher-student relationship. How to deal with the challenges of "Internet +" era has become a practical problem for higher vocational colleges of traditional Chinese medicine in golden course teaching. In order to solve the problems, this paper first introduced the close relationship between English teaching and higher vocational college education, and then analyzed the challenges of higher vocational college of traditional Chinese medicine English teaching, and finally put forward the corresponding countermeasures of golden course teaching in the era of "Internet +".
Selective eye-gaze augmentation to enhance imitation learning in Atari games

This paper presents the selective use of eye-gaze information in learning human actions in Atari games. Extensive evidence suggests that our eye movements convey a wealth of information about the direction of our attention and mental states and encode the information necessary to complete a task. Based on this evidence, we hypothesize that selective use of eye-gaze, as a clue for attention direction, will enhance the learning from demonstration. For this purpose, we propose a selective eye-gaze augmentation (SEA) network that learns when to use the eye-gaze information. The proposed network architecture consists of three sub-networks: gaze prediction, gating, and action prediction network. Using the prior 4 game frames, a gaze map is predicted by the gaze prediction network, which is used for augmenting the input frame. The gating network will determine whether the predicted gaze map should be used in learning and is fed to the final network to predict the action at the current frame. To validate this approach, we use publicly available Atari Human Eye-Tracking And Demonstration (Atari-HEAD) dataset consists of 20 Atari games with 28 million human demonstrations and 328 million eye-gazes (over game frames) collected from four subjects. We demonstrate the efficacy of selective eye-gaze augmentation compared to the state-of-the-art Attention Guided Imitation Learning (AGIL) and Behavior Cloning (BC). The results indicate that the selective augmentation approach (the SEA network) performs significantly better than the AGIL and BC. Moreover, to demonstrate the significance of selective use of gaze through the gating network, we compare our approach with the random selection of the gaze. Even in this case, the SEA network performs significantly better, validating the advantage of selectively using the gaze in demonstration learning.
Priority-Objective Reinforcement Learning

Intelligent agents often have to cope with situations in which their various needs must be prioritised. Efforts have been made, in the fields of cognitive robotics and machine learning, to model need prioritization. Examples of existing frameworks include normative decision theory, the subsumption architecture and reinforcement learning. Reinforcement learning algorithms oriented towards active goal prioritization include the options framework from hierarchical reinforcement learning and the ranking approach as well as the MORE framework from multi-objective reinforcement learning. Previous approaches can be configured to make an agent function optimally in individual environments, but cannot effectively model dynamic and efficient goal selection behaviour in a generalisable framework. Here, we propose an altered version of the MORE framework that includes a threshold constant in order to guide the agent towards making economic decisions in a broad range of priority-objective reinforcement learning’ (PORL) scenarios. The results of our experiments indicate that pre-existing frameworks such as the standard linear scalarization, the ranking approach and the options framework are unable to induce opportunistic objective optimisation in a diverse set of environments. In particular, they display strong dependency on the exact choice of reward values at design time. However, the modified MORE framework appears to deliver adequate performance in all cases tested. From the results of this study, we conclude that employing MORE along with integrated thresholds, can effectively simulate opportunistic objective prioritization in a wide variety of contexts.
Liability regimes for creative machines. A European perspective.

Artificial Intelligence liability has been the subject of a lively debate on the European regulators’ agenda for quite some time, part of a larger regulatory framework discussion related to the legal status of intelligent machines and AI regulation in general. As straightforward as the answer might seem to the question on whether AI systems should be granted legal status, and as such become holders of rights and duties, contradictory regulatory loopholes exist that make it possible for machines to be aligned on the same legal frontier as humans. Namely, the recent judicial precedent in the DABUS case, where an AI was granted inventorship, has shed light on the need to harmonise and unify AI regulatory provisions regarding the legals status of AI, while creating and maintaining an ecosystem that could balance the preservation of individual safety and fundamental rights without overly inhibiting innovation in AI.This article addresses the issue of machine liability in light of the current European regulatory framework on AI, while considering machine creativity as a medium for granting legal status to AI systems.
A multi-pronged approach to effective application security

Cyber security has become one of the main pillars of a successful business, especially when you factor in elements such as business innovation, operations and risk management – all of which require adequate security to work. Therefore, investing in adequate cyber security infrastructure and tools is vital for organisations looking to scale and remain competitive.
Learning or Modelling? An Analysis of Single Image Segmentation Based on Scribble Information

Single image segmentation based on scribbles is an important technique in several applications, e.g. for image editing software. In this paper, we investigate the scope of single image segmentation solely given the image and scribble information using both convolutional neural networks as well as classical model-based methods, and present three main findings: 1) Despite the success of deep learning in the semantic analysis of images, networks fail to outperform model-based approaches in the case of learning on a single image only. Even using a pretrained network for transfer learning does not yield faithful segmentations. 2) The best way to utilize an annotated data set is by exploiting a model-based approach that combines semantic features of a pretrained network with the RGB information, and 3) allowing the networks prediction to change spatially and additionally enforce this variation to be smooth via a gradient-based regularization term on the loss (double backpropagation) is the most successful strategy for pure single image learning-based segmentation.
Construction Engineering Technology Specialty Group Construction in Big Data Age

Abstract As an important part of higher education, higher vocational colleges play an important role in cultivating talents needed in various industries. In addition to adapting to the local economic development, we should have our own characteristics, attract more students, and thus win the social reputation, which undoubtedly brings challenges to the construction of teachers’ team in higher vocational colleges. This paper takes the construction of construction engineering and technology specialty group in a university in our city as an example analysis of the construction of teachers in the construction of professional groups, and analyzes the problems and advantages of professional groups in the construction of teachers. Finally, it provides countermeasures for the team building of professional group teachers. Through analysis, we can see that the age distribution of teachers in this professional group is less than 20 people under 30 years old, accounting for 23.2%. The average education is satisfactory.
Benchmarking the Poor Man's Ising Machine

We demonstrate the use of a Time-Multiplexed Opto-Electronic Oscillator based Coherent Ising Machine(CIM) to solve Maxcut Problems. Time multiplexing helps reduce system complexity and size, with good scaling against problem size. Benchmarks against state-of-the-art classical solvers show comparable performance, with encouraging results for further improvements.
A Bayesian solution to the Behrens–Fisher problem

Abstract A simple solution to the Behrens–Fisher problem based on Bayes factors is presented, and its relation with the Behrens–Fisher distribution is explored. The construction of the Bayes factor is based on a simple hierarchical model, and has a closed form based on the densities of general Behrens–Fisher distributions. Simple asymptotic approximations of the Bayes factor, which are functions of the Kullback–Leibler divergence between normal distributions, are given, and it is also proved to be consistent. Some examples and comparisons are also presented.
COVRATIO statistic for replicated linear functional relationship model

Abstract Replicated linear functional relationship model (LFRM) can be categorized under the errors-in-variables model where variables involved are measured with error. However, the presence of outliers in dataset significantly impacts the parameter estimation. We extend the use of the COVRATIO statistic which has been successfully used in unreplicated LFRM for detecting the outliers. A simulation study is used to obtain the cut-off point at 10% upper percentiles. An illustration of this procedure is presented for its potential in a real data set. The procedure successfully identifies the outlier present in the data set.
Perceptual Attributes of Human-Like Animal Stickers as Nonverbal Cues Encoding Social Expressions in Virtual Communication

Communicative expression is a cross-species phenomenon. We investigated the perceptual attributes of social expressions encoded in human-like animal stickers commonly used as nonverbal communicative tools on social media (e.g. WeChat). One hundred and twenty animal stickers which varied in 12 categories of social expressions (serving pragmatic or emotional functions), 5 animal kinds (cats, dogs, ducks, rabbits, pigs) and 2 presented forms (real animal vs. cartoon animal) were presented to social media users, who were asked to rate on the human likeness, the cuteness, the expressiveness and the matchness of each intended expression against the given label. The data shows that the kind of animal that is expected to best encode a certain expression is modulated by its presented forms. The “cuteness” stereotype towards a certain kind of animal is sometimes violated as a function of the presented forms. Moreover, user’s gender, interpersonal sensitivity and attitudes towards the ethic use of animals modulated various perceptual attributes. These findings highlight the factors underlying the decoding of social meanings in human-like animal stickers as nonverbal cues in virtual communication.
ADVMIX: Data Augmentation for Accurate Scene Text Spotting

Accurate scene text spotting models ask for effective data augmentation algorithms. This paper presents a novel data augmentation algorithm called AdvMix that integrates techniques of adversarial attack and mixup. First, it utilizes the PGD method to synthesize adversarial samples. Second, it is the first work to implement feature-wise mixup between original data and the associated adversarial sample to enhance data augmentation. AdvMix has been evaluated over ICDAR2013 and ICDAR2015 datasets, showing its superior performance in improving accuracy of scene text spotting models.
An Improved K-means Clustering Algorithm Based on Beetle Antennae Search

Clustering is a process of dividing the research objects into different classes and has become an important statistical analysis technique in data mining; K-means clustering algorithm is widely used because of its excellent speed and high efficiency. However, the traditional K-means algorithm always falls into the partial optimized problem due to the random selection of the initial clustering centers, which makes the accuracy of clustering affected. In response to the problem, this paper proposes an improved K-means algorithm based on beetle antennae search. The improved algorithm uses BAS to optimize the initial clustering centers so that the clustering results can be more accurate. The simulation results show that compared with the traditional K-means algorithm, the improved algorithm improves both in efficiency and accuracy. are already defined on the style sheet, as illustrated by the portions given in this document.
Cognitive computing in human activity recognition with a focus on healthcare

Human activity recognition (HAR) is a quintessence to empower a robot to distinguish the conduct of a personal care-receiver. As opposed to outward appearances, an activity recognition can see practices of a consideration beneficiary, who might be a senior adult, a youngster, or a chronic patient. Through HAR, a robot tracks the care patient activity and perceive human practices like unhealthy habits and anomalous activities. However, patient activity recognition through simple images is a highly challenging task. Several challenges such as the likeness of unmistakable human behaviors, disorder background, similarities in different human activities may significantly reduce the classification performance. Because of rapid developments in cutting-edge machine learning models, substantial solutions can arise from distinct deep learning algorithms, including convolutional neural network, generative adversarial network. In this chapter, the authors review several cognitive computing approaches in the advancement of human-robot interaction, especially in healthcare industries.
A Two-Stage Autoencoder For Visual Anomaly Detection

Deep convolutional autoencoder (DCAE) is usually optimized to minimize the difference between the input and the reconstruction, and the reconstruction error has been widely used as an indicator for visual anomaly detection. However, DCAE sometimes can reconstruct anomalies very well and thus may yield misdetections. To tackle this issue, we propose a novel non-symmetrical DCAE, which is trained in a two-stage manner. Specifically, a single RotNet is first trained to serve as encoder. Then, discriminative representations generated by the frozen encoder are used to train two parallel decoders for image reconstruction. Finally, the reconstruction errors obtained by the two decoders are combined as the anomaly score. Massive experiments on three public datasets and one practical industrial dataset demonstrate the superiority of the proposed method among existing reconstruction based methods.
Revizor: A Data-Driven Approach to Automate Frequent Code Changes Based on Graph Matching

Many code changes that developers make in their projects are repeated and constitute recurrent change patterns. It is of interest to collect such patterns from the version history of open-source repositories and suggest the most useful of them as quick fixes. In this paper, we present Revizor—a tool aimed to build custom plugins for PyCharm, a popular Python IDE. A Revizor-based plugin can take change patterns and highlight potential places for their application in the developer’s code editor. If the developer accepts the quick fix, the plugin automatically performs the edit. Our approach uses a graph-based representation of code changes, which allows it to support complex distributed code patterns. Experienced developers have also rated the usability and the performance of such Revizor-based plugin positively.The source code of the tool and test plugin prototype are available on GitHub: https://github.com/JetBrains-Research/revizor. A demonstration video with a short tool description can be found on YouTube: https://youtu.be/5eLs14nco7E.
A Proposed Method for Background Subtraction and Adding Scribbles in Video Matting

Identifying moving objects from a video sequence is a fundamental task in most computer-vision applications. It is essential for visual tracking systems. In this paper, a new algorithm for background subtraction has been proposed and evaluated. Decision-level fusion of three background subtraction algorithms; frame difference, approximated median, and Mixture of Gaussians; has been utilized. It has been found that the percentage error for each frame has reduced remarkably when applying the proposed technique. Moreover, an entirely-automatic algorithm for adding scribbles in video matting is proposed to improve video matting results depending on the suggested background subtraction algorithm. The overall performance is evaluated. The average error has been reduced effectively and the overall performance has been enhanced.