Reviewerly/exHarmony huggingface dataset:
Hierarchial Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection.

This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014). We propose a new method to integrate HRL, experience replay and effective subgoal selection through an implicit curriculum design based on human expertise to support sample-efficient learning and enhance interpretability of the agent's behavior. Human expertise remains indispensable in many areas such as medicine (Buch, Ahmed, and Maruthappu 2018) and law (Cath 2018), where interpretability, explainability and transparency are crucial in the decision making process, for ethical and legal reasons. Our method simplifies the complex task sets for achieving the overall objectives by decomposing them into subgoals at different levels of abstraction. Incorporating relevant subjective knowledge also significantly reduces the computational resources spent in exploration for RL, especially in high speed, changing, and complex environments where the transition dynamics cannot be effectively learned and modelled in a short time. Experimental results in two StarCraft II (SC2) (Vinyals et al. 2017) minigames demonstrate that our method can achieve better sample efficiency than flat and end-to-end RL methods, and provides an effective method for explaining the agent's performance.
Directed hypergraph neural network.

To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semi-supervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest accuracies.
From the Universal Declaration on the human genome to the international legal regulation of genomic research and technology: idea and reality

The modern society headlong transition to the genomics era is accompanied by a dangerous lagging the genomic research and technology socio-normative regulation from the progress of genetic engineering, which carries not only colossal opportunities for the medicine development, but also existential risks for humanity. It is long overdue to move the socio-normative regulation of this sphere from the level of international declarations and recommendations to the level of global legal agreements. However, progress in this direction is blocked by an exceptionally high degree of competition between countries and transnational pharmaceutical giants. It is significant that in the Universal Declaration on the Human Genome the most controversial problem of genomics, related to the possibility of human germ line editing, is not even indicated. At the moment this problem requires special attention from the scientific community, including scientists in the field of jurisprudence specializing in the philosophy of law, medical law and legal support of the modern technologies.
A polyhedral conic functions based classification method for noisy data

&lt;p style='text-indent:20px;'&gt;This paper presents a robust binary classification method, which is an extended version of the Modified Polyhedral Conic Functions (M-PCF) algorithm, earlier developed by Gasimov and Ozturk. The new version presented in this paper, has new features in comparison to the original algorithm. The mathematical model used in the new version, is relaxed by allowing some inaccuracies in an optimal way. By this way, it is aimed to reduce the overfitting and improve the generalization property. In the original version, the sublevel set of a separating function generated at every iteration, does not contain any element of the other set. This is changed in the new version, where the sublevel sets of separating functions generated by the new algorithm, are allowed to contain some elements from other set. On the other hand, the new algorithm uses a tolerance parameter which prevents generating "less productive separating functions". In the original version, the algorithm continues till all points of the "first" set are separated from the second one, where a separating function is generated if there still exist unseparated elements regardless the number of such elements. In the new version, the tolerance parameter is used to terminate iterations if there are only a few unseparated elements. By this way, it is aimed to improve the generalization property of the algorithm, and therefore the new version is called Parameterized Polyhedral Conic Functions (P-PCF) method. The performance and efficiency of the proposed algorithm is demonstrated on well-known datasets from the literature and on noisy data.&lt;/p&gt;
Fast Multi-Step Optimization with Deep Learning for Data-Centric Supercomputing

Following the improvement in data acquisition technology and in analysis capability, we develop an optimization method for data-centric supercomputing, which can utilize large-scale data. Prior information of the solution of these optimization problems is often available, and the use of such information is expected to improve the efficiency of the optimization process and the accuracy of its solution. Targeting use on supercomputing environments such as Big Data and Extreme-scale Computing (BDEC) resources, we develop a fast parameter optimization method that combines data-centric fast simulation methods and deep learning. This method comprises multiple steps, i.e., an approximate solution is obtained in a short time using prior information on many compute nodes, and the approximate solution is refined using a small number of compute nodes. Thus, optimum solutions can be obtained in a short time based on the required accuracy, even on many compute-node environments without fast interconnects. The developed method is expected to provide useful knowledge for related optimization problems computed on distributed ubiquitous computing resources using large observation data obtained by the Internet of Things (IoT) and fifth-generation (5G) networks.
Galaxy: A Family of Stream-Cipher-Based Space-Hard Ciphers

Whitebox cryptography seeks to ensure the security of cryptographic algorithms against adversaries who have unlimited access to the environments for their implementation. At ACM CCS 2015, Bogdanov and Isobe proposed a security notion called space hardness and a secure block cipher named SPACE in the whitebox setting. SPACE is a table-based cryptographic primitive whose table comprises the pairs of inputs to a block cipher such as AES and the corresponding outputs. In line with SPACE, other whitebox cryptographic schemes were proposed and offer sufficient security as SPACE does. However, there is still room for improvement in the performance of their encryption and table generation. In this paper, we propose a new family of whitebox cryptographic primitives called Galaxy to enhance the performance of the encryption and table generation. Galaxy employs a stream cipher to generate the table instead of a block cipher. The security of Galaxy against key-extraction attacks in the whitebox setting is reduced to the key-extraction problem for the stream cipher in the blackbox setting. Additionally, we utilize type-2 generalized Feistel network with optimal shuffle layers for the algorithm of Galaxy to improve the encryption performance. Type-2 generalized Feistel network enables parallel table lookups in the algorithm of Galaxy. As a result, we successfully increase the speed of encryption by 1.3–15 times. Besides, when we use chacha for table generation of Galaxy and AES for other existing block-cipher-based whitebox schemes, we can create the table of Galaxy 1.5–10 times faster than that of other existing whitebox schemes.
Silhouette: Efficient Protected Shadow Stacks for Embedded Systems

Microcontroller-based embedded systems are increasingly used for applications that can have serious and immediate consequences if compromised---including automobile control systems, smart locks, drones, and implantable medical devices. Due to resource and execution-time constraints, C is the primary language used for programming these devices. Unfortunately, C is neither type-safe nor memory-safe, and control-flow hijacking remains a prevalent threat. This paper presents Silhouette: a compiler-based defense that efficiently guarantees the integrity of return addresses, significantly reducing the attack surface for control-flow hijacking. Silhouette combines an incorruptible shadow stack for return addresses with checks on forward control flow and memory protection to ensure that all functions return to the correct dynamic caller. To protect its shadow stack, Silhouette uses store hardening, an efficient intra-address space isolation technique targeting various ARM architectures that leverages special store instructions found on ARM processors. We implemented Silhouette for the ARMv7-M architecture, but our techniques are applicable to other common embedded ARM architectures. Our evaluation shows that Silhouette incurs a geometric mean of 1.3% and 3.4% performance overhead on two benchmark suites. Furthermore, we prototyped Silhouette-Invert, an alternative implementation of Silhouette, which incurs just 0.3% and 1.9% performance overhead, at the cost of a minor hardware change.
Feel it: Using Proprioceptive and Haptic Feedback for Interaction with Virtual Embodiment

Virtual embodiment has become popular for enhancing virtual interaction in terms of sharing object information. A user can control a character or object in a virtual environment to provide immersive interactive experience. However, one of the limitations for the virtual interactions was the incapability to receive feedback apart from visual hints. In this demonstration, we present using servo motor and Galvanic Vestibular Stimulation to provide feedback from a virtual interaction. Our technique transforms information of the virtual objects (e.g.: weight) into haptic and proprioceptive feedback that stimulates different sensations to a user. We present the user experience to the attendees of SIGGRAPH 2020 through a live demonstration in a virtual environment controlled using a virtual robotic arm.
Single-stage Instance Segmentation

Albeit the highest accuracy of object detection is generally acquired by multi-stage detectors, like R-CNN and its extension approaches, the single-stage object detectors also achieve remarkable performance with faster execution and higher scalability. Inspired by this, we propose a single-stage framework to tackle the instance segmentation task. Building on a single-stage object detection network in hand, our model outputs the detected bounding box of each instance, the semantic segmentation result, and the pixel affinity simultaneously. After that, we generate the final instance masks via a fast post-processing method with the help of the three outputs above. As far as we know, it is the first attempt to segment instances in a single-stage pipeline on challenging datasets. Extensive experiments demonstrate the efficiency of our post-processing method, and the proposed framework obtains competitive results as a single-stage instance segmentation method. We achieve 32.5 box AP and 26.0 mask AP on the COCO validation set with 500 pixels input scale and 22.9 mask AP on the Cityscapes test set.
Research on Color Algorithm of Gray Image Based on a Color Channel

Traditional grayscale image colorization methods generally have problems such as distortion and inaccuracy, such as pseudo color enhancement and false color processing. This paper uses a more accurate and better color implementation algorithm. First, the source image and the target image are converted from the RGB channel into the lαβ channel, and then the similarity of the pixels in the source image is determined and compared by comparing the similarities of the pixels between the two images, and then the target image is colored. transfer. Finally, the image can be converted from the lαβ space to the RGB space. This paper elaborates on the theoretical basis, formula steps, program implementation, and superiority of the algorithm compared with traditional image processing methods.
Cluster ensemble selection using balanced normalized mutual information

A bad partition in an ensemble will be removed by a cluster ensemble selection framework from the final ensemble. It is the main idea in cluster ensemble selection to remove these partitions (bad partitions) from the selected ensemble. But still, it is likely that one of them contains some reliable clusters. Therefore, it may be reasonable to apply the selection phase on cluster level. To do this, a cluster evaluation metric is needed. Some of these metrics have been recently introduced; each of them has its limitations. The weak points of each method have been addressed in the paper. Subsequently, a new metric for cluster assessment has been introduced. The new measure is named Balanced Normalized Mutual Information (BNMI) criterion. It balances the deficiency of the traditional NMI-based criteria. Additionally, an innovative cluster ensemble approach has been proposed. To create the consensus partition considering the elected clusters, a set of different aggregation-functions (called also consensus-functions) have been utilized: the ones which are based upon the co-association matrix (CAM), the ones which are based on hyper graph partitioning algorithms, and the ones which are based upon intermediate space. The experimental study indicates that the state-of-the-art cluster ensemble methods are outperformed by the proposed cluster ensemble approach.
Phase-measuring profilometry of colored objects based on color coding and sinusoidal correction

To improve the phase-measuring profilometry measurement speed, phase-shifted fringes can be encoded into color channels. However, for colored objects, the intensity of the fringes in each channel is affected by surface texture. Therefore, a measurement method based on color coding and sinusoidal correction is proposed. First, two color images are encoded. The red (R) and blue (B) channels for the first image and the green (G) channel for the second image are used in a three-step phase-shifting process. Meanwhile, the G channel of the first coded image and the R and B channels of the second coded image are corresponding solid colors. Two color-coded images are projected onto the object’s surface, and the CCD captures the modulated deformed fringe pattern. After decoupling, the channels of the images are separated, the pure color channels are extracted, and the reflectivity of each point on the object surface in the RGB channels is calculated. Then, the intensity of the phase-shifted fringes in the two images is corrected by the calculated reflectivity. A 24-color standard color card and color lion mask were used to verify the method. Compared with the traditional color coding method, we were able to reduce the influence of color texture and obtain better results.
Using Uncertain Time Intervals in Linked Data

Uncertain temporal data often included in a dataset are critical, especially in history and archeology. In this study, we proposed a method to represent uncertain time intervals in uncertain temporal data and examine the relationships between them using the Linked Data technology. We created uncertain time intervals by specifying the ranges for the beginning and the ending points, and its features were represented by possible and reliable time intervals. Moreover, we divided the states of relationships between the two uncertain time intervals as reliable, possible, and impossible relations for each Allen’s relation. Using a sample model, we demonstrated that procedures for creating uncertain time intervals and examining the relationships between them can be realized as RDFs in the Linked Data world.
Visual enhancement of brain cancer MRI using multiscale dyadic filter and Hilbert transformation

Abstract The quality of the medical image plays a major role in decision making by the radiologists. There exists a visual differentiation between the normal scene color images and medical images. Due to the low illumination and unavailability of the color parameter, medical images require more attention by radiologists for decision making. In this paper a new approach is proposed that enhances the quality of the Magnetic Resonance (MR) images. Proposed approach uses the spectral information present in form of Amplitude and Frequency within the MR image slices for an enhancement. The extracted enhanced spectral information gives better visualization as compared with original signal image generated from MR scanner. The quantitative analysis of the proposed approach suggests that the new method is far better than the traditional state-of-art image enhancement methods.
The importance of temporal information in Bayesian network structure learning

Several algorithms have been proposed towards discovering the graphical structure of Bayesian networks. Most of these algorithms are restricted to observational data and some enable us to incorporate knowledge as constraints in terms of what can and cannot be discovered by an algorithm. A common type of such knowledge involves the temporal order of the variables in the data. For example, knowledge that event B occurs after observing A and hence, the constraint that B cannot cause A. This paper investigates real-world case studies that incorporate interesting properties of objective temporal variable order, and the impact these temporal constraints have on the learnt graph. The results show that most of the learnt graphs are subject to major modifications after incorporating incomplete temporal objective information. Because temporal information is widely viewed as a form of knowledge that is subjective, rather than as a form of data that tends to be objective, it is generally disregarded and reduced to an optional piece of information that only few of the structure learning algorithms may consider. The paper argues that objective temporal information should form part of observational data, to reduce the risk of disregarding such information when available and to encourage its reusability across related studies.
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis

Machine learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, namely their black-box character. Motivated by the interest to understand the functioning of ML models, methods from the field of Explainable Artificial Intelligence (XAI) have recently become important. This article investigates the usefulness of XAI methods in clinical gait classification. For this purpose, predictions of state-of-the-art classification methods are explained with an established XAI method, i.e., Layer-wise Relevance Propagation (LRP). We propose to evaluate the obtained explanations with two complementary approaches: a statistical analysis of the underlying data using Statistical Parametric Mapping and a qualitative evaluation by a clinical expert. A gait dataset comprising ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls is utilized. We investigate several gait classification tasks, employ multiple classification methods, and analyze the impact of data normalization and different signal components for classification performance and explanation quality. Our experiments show that explanations obtained by LRP exhibit promising statistical properties concerning inter-class discriminativity and are also in line with clinically relevant biomechanical gait characteristics.
Generalization of iterative sampling in autoencoders

Generative autoencoders are designed to model a target distribution with the aim of generating samples and it has also been shown that specific non-generative autoencoders (i.e. contractive and denoising autoencoders) can be turned into generative models using reinjections (i.e. iterative sampling). In this work, we provide mathematical evidence that any autoencoder reproducing the input data with a loss of information can sample from the training distribution using reinjections. More precisely, we prove that the property of modeling a given distribution and sampling from it not only applies to contractive and denoising autoencoders but also to all lossy autoencoders. In accordance with previous results, we emphasize that the reinjection sampling procedure in autoencoders improves the quality of the sampling. We experimentally illustrate the above property by generating synthetic data with non-generative autoencoders trained on standard datasets. We show that the learning curve of a classifier trained with synthetic data is similar to that of a classifier trained with original data.
Query Relational Databases in Punjabi Language

Public relational databases are accessed by end users to get the information they require. Direct interaction with relational databases requires the knowledge of structured query language (SQL). It is not feasible for every user to learn SQL. An access through an application limits the query options. An end user can ask a query very easily in a natural language. To provide the full advantages of public access, the users should be allowed to query the required data through natural language questions. It is possible by providing natural language support to query relational databases. This paper presents the system model, design and implementation to query relational databases in Punjabi language. It allows human–machine interaction in Punjabi language for information retrieval. It accepts a Punjabi language query in flexible format, uses pattern matching techniques to prepare an SQL query from it, maps data element tokens of the query to actual database objects and joins multiple tables to fetch the required data.
Availability of Web Citations in Scholarly Library and Information Science Journals: A Study

The present study examines the availability of web citations in scholarly journals of Library and Information Science. The journals were selected based on their high impact factor published between 2008 and 2017. A PHP script was used to test the Uniform Resource Locators for their availability. A total of 7986 articles were downloaded and 3,24,636 references were extracted. A total of 51,839 URLs were checked for their availability. Further the lexical features of URLs like file extension, path depth, character length and top-level domain was determined. The research findings indicated that the percentage of web citations in articles has been continuously increased from 2008 to 2017. The result of the accessibility check showed that almost 76.90% of URLs were accessible and the remaining 23.10% encountered accessibility errors. The majority of errors were due to HTTP 404 error code (Not found error). The findings of the study will be helpful to authors, publishers and editorial staff to ensure that web citations will be accessible in future.