Reviewerly/exHarmony huggingface dataset:
Breast cancer clustering using modified spherical K-Means

Abstract Clustering is one of common techniques to group dataset into subsets based on distance measure. It has been applied in machine learning, pattern recognition, data mining, image analysis, and bioinformatics. Spherical k-means is one of clustering methods to address computational efficiency and solution quality in terms of deciding an action. In this paper, we used modified spherical k-means by using kernel radial basis function (RBF) by inner product measures in spherical k-means to cluster breast cancer Coimbra dataset from UCI machine learning into clusters. A new clusters will defined to healthy control cluster and patient cluster based on medical records. The highest accuracy results of kernel spherical k-means (SPKM) clustering method with radial basis function (RBF) kernel in breast cancer Coimbra (BCC) dataset is 72,41%. Addition of kernel to spherical k-means makes the results of accuracy be stable than using spherical k-means.
Reinforcement Learning from a Mixture of Interpretable Experts.

Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. These successes however are mostly achieved by 'black-box' policies in simulated domains. When deploying RL to the real world, several concerns regarding the use of a 'black-box' policy might be raised. In an effort to make the policies learned by RL more transparent, we propose in this paper a policy iteration scheme that retains a complex function approximator for its internal value predictions but constrains the policy to have a concise, hierarchical, and human-readable structure, based on a mixture of interpretable experts. We show that our proposed algorithm can learn compelling policies on continuous action deep RL benchmarks, matching the performance of neural network based policies, but returns policies that are more amenable to human inspection than neural network or linear-in-feature policies.
The composition of the improved logistic map and the MS map in generating a new chaotic function

Abstract A new chaotic map is proposed from the composition of the improved logistic map and the MS map. The composition is done by mapping the MS map first, where the result is then mapped by the improved logistic map. The new map as the result of the composition is chaotic. This is shown by the Lyapunov Exponent analysis, bifurcation diagrams, and the NIST randomness test. The Lyapunov Exponent results with x 0 = 0.1 are non-negative for r ∈ [1,4]. Its bifurcation diagrams with p ∈ (0,4) has a better density at r ∈ 2.5. The new chaotic function also passes 10 out of 16 NIST test, with initial value and parameter values x 0 = 0.1, r = 2.5, and p = 2.5.
SqueezeJet-3: An Accelerator Utilizing FPGA MPSoCs for Edge CNN Applications

Most FPGA-based Convolutional Neural Network (CNN) hardware accelerators target the datacenter rather than edge processing units. To further fill this gap, this work presents SqueezeJet-3 - a novel FPGA-based embedded system, consisting of software and hardware, for accelerating edge CNN inference. Even though SqueezeJet-3 is optimized for accelerating small ImageNet class CNNs, such as SqueezeNet v1.1, on low-end lowcost FPGA SoC devices, it can also be used for the acceleration of larger CNNs, such as the VGG16. Evaluation of our accelerator reveals better or comparable performance with that triggered by the current state-of-the-art similar tools and systems.
Cisterns as vital structures: byzantine cisterns and subarachnoid cisterns

Water is essential for life.It is needed not only to survive but also to sustain daily life activities.The maintenance of daily life activities related to hygiene as well as prevention of epidemic diseases and accidents involving fire was as important in the past as today.Powerful empires built many open-air and covered cisterns for water reservation and constructed aqueducts to bring water from hinterland to these cisterns.The first prototypes of cisterns were constructed in the Neolithic age.Byzantine cisterns such as the Basilica Cistern and Aqueduct of Valens are excellent examples that are remnants from the past to present.Similar to these social measures for water preservation, biological structures exhibit their own measures.In the human body, subarachnoid cisterns of central nervous system are the best-known cisterns, despite the presence of the cisterna chyli and Golgi body.The central nervous system produces and stores water in the form of cerebrospinal fluid in the subarachnoid cisterns for mechanical and immunological protection of the anatomical structures and for autoregulation of cerebral blood flow every day.Any condition that may adversely affect the cisterns, public or subarachnoid, may cause serious and irreversible damage to life.Hence, we should appreciate the importance of water for life; moreover, "if there is water there is life" is not a great prophecy.
Dialog Policy Learning for Joint Clarification and Active Learning Queries

Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operation. Prior work on dialog systems has either focused on exclusively learning how to perform clarification/ information seeking, or to perform active learning. In this work, we train a hierarchical dialog policy to jointly perform {\it both} clarification and active learning in the context of an interactive language-based image retrieval task motivated by an on-line shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions.
A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World.

The blocks world is a classic toy domain that has long been used to build and test spatial reasoning systems. Despite its relative simplicity, tackling this domain in its full complexity requires the agent to exhibit a rich set of functional capabilities, ranging from vision to natural language understanding. There is currently a resurgence of interest in solving problems in such limited domains using modern techniques. In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robustly interprets spatial queries, and a constraint solver that derives answers based on 3-D spatial modeling. The contributions of this work include a semantic parser that maps spatial questions into logical forms consistent with a general approach to meaning representation, a dialog manager based on a schema representation, and a constraint solver for spatial questions that provides answers in agreement with human perception. These and other components are integrated into a multi-modal human-computer interaction pipeline.
Factors Influencing Bitcoin Investment Intention: The case of Oman

The current study examines the factors that might increase the investment in Bitcoin among Muslim communities. Oman is selected as a setting for this study due to the rapid progress in the Islamic finance and investment areas that the country has witnessed though ventured in this field relatively late compared to other countries. The study used a survey questionnaire to collect data for a sample of 200 respondents. Subsequently, the collected data was analysed using structural equation modelling (SEM) as well as basic descriptive statistics and one sample t-test. The findings revealed that the respondents perceive themselves to have sufficient awareness and knowledge of the Bitcoin concept and benefits, as well as the techniques used to manage a Bitcoin account. In addition, the findings revealed that factors such as perceived ease of use, compatibility, awareness and facilitating conditions have a significant impact on Omani communities' intention to invest in Bitcoin.
Evolutionary double-pipe prefix-free hash function

Recent works have presented collision attacks based on differentiability of classical hash functions. In this article a new hash function is proposed with enhanced Merkle-Damgard construction. It has a single length compression function which is based on Miyaguchi-Preneel block cipher and benefit of Genetic Algorithm for diffusion. Fitness function of GA provides maximum cryptographic diffusion and resist against well-known weaknesses like differentiability and collision attacks.
Communication Characteristics of Public Service Advertisement based on Big Data

Abstract As we all know, the business image and leading brand of enterprises determine the main position of relevant enterprises and their products in the hearts of consumers. This concept is usually shaped by the comprehensive strength of enterprises and the design and innovation of advertising strategies. In the design of plane advertisement in our daily life, newspaper advertisement and magazine advertisement are paid more and more attention. As a result, they have a large circulation and high credibility. This also means that they have a strong brand building capacity. In addition, the combination of two-dimensional advertising and media technology can greatly enhance the shaping power of daily advertising.
Fair k-Means Clustering

We show that the popular $k$-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups). Such biased clusterings can have deleterious implications for human-centric applications such as resource allocation. We present a fair $k$-means objective and algorithm to choose cluster centers that provide equitable costs for different groups. The algorithm, Fair-Lloyd, is a modification of Lloyd's heuristic for $k$-means, inheriting its simplicity, efficiency, and stability. In comparison with standard Lloyd's, we find that on benchmark data sets, Fair-Lloyd exhibits unbiased performance by ensuring that all groups have balanced costs in the output $k$-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever $k$-means is currently used.
Backtracking Search for Optimal Parameters of a PLL-based True Random Number Generator

The phase-locked loop-based true random number generator (PLL-TRNG) extracts randomness from clock jitter. It is an interesting construct because it comes with a stochastic model, making it certifiable by certification bodies. However, bringing it to good performance is difficult since it comes with multiple parameters to tune. This article proposes to use backtracking to determine these parameters. Compared to existing methods, based on genetic algorithms or exhaustive search of a feasible set of parameters, backtracking has several advantages. Indeed, since this method is expressible by constraint programming, it provides very good readability. Constraints can be specified in a very straightforward and maintainable way. It also exhibits good performance and generates PLL-TRNG configurations rapidly. Finally, it allows to integrate new exploratory design constraints for the PLL-TRNG very easily. We provide experimental results with a PLL-TRNG implemented on three FPGA families that come with different physical constraints, showing that the method allows to find good parameters for every one of them. Moreover, we were able to obtain configurations that lead to an increase 59 % in throughput and 82 % in jitter sensitivity on average, thereby generating random numbers of higher quality at a faster rate. This approach also paves the way for new design exploration strategies for PLL-TRNG. The source code of our implementation is open source and available online for reproducibility and reuse.
Construction and Application of Micro Technology Platform in Applied Technology Universities under the Background of Internet Plus

Abstract With the rapid development of information technology and Internet technology, people have developed a new teaching tool, micro class, and widely used it in the field of education. With its unique characteristics and advantages, micro course has become a common teaching method in higher vocational colleges. The purpose of this paper is to explore the construction and application of micro technology platform in Applied Technology Universities under the background of “Internet plus”. The background of the “Internet plus” era, the emergence of micro class platform and the theory of micro learning are expounded. This paper discusses the development of Applied Technology Education in Colleges and Universities Based on micro course teaching. Based on the investigation and analysis of the practical application of micro course in a application-oriented university, the experimental results show that only 4.2% of the teachers think it is unnecessary to use micro course in classroom teaching, and more than 80.5% of the teachers think it is necessary to apply micro course to traditional classroom teaching; meanwhile, the data also shows that only 18% of the teachers often use micro course to teach, Nearly half of teachers do not use micro class for daily teaching.
Effect of Nutritional Status, Hemoglobin Levels and Psychosocial Emotional Behavior with Cognitive Function of Female Teenager

Teenager are the future generation, with the cognitive abilities of a good expected level of education will be good. The impact of nutritional status abnormal, low hemoglobin levels, and disorders psychosocial emotional behavior associated with cognitive abilities in teenager, especially in female teenager. The objective of this study is to analyze the effect between nutritional status, hemoglobin levels, and psychosocial emotional behavior with cognitive function in female teenager. This research used observational analytic method with cross sectional approach. Data were collected during February‑March 2019. Respondents in this study were students or female teenager in accordance with the inclusion and exclusion criteria. Sampling technique in this research is probability sampling type simple random sampling with a total sample of 72 female teenager respondents. The independent variables studied are nutritional status, hemoglobin levels, and psychosocial emotional behavior and dependent variable was cognitive function in female teenager. Data nutrition status collected by BMI measurements according to age, hemoglobin levels by hemocue tool, psychosocial emotional behavior was measured by PSC‑35 and cognitive function was measured by MMSE‑test. Analysis was using logistic regression. The results of research obtained that there is effect between the nutritional status of skinny, low hemoglobin levels (anemia) and suspect there are disorders psychosocial emotional behavior with a value of p &lt; 0.05. Risk factors nutritional status of skinny, low hemoglobin levels (anemia) and suspect disorders psychosocial emotional behavior on cognitive function below normal, amounted to 81,4% and 18.6% was caused by other factors. Nutritional status, hemoglobin levels, and behavior psychosocial emotions affect the cognitive function of female teenager.
Change of Psychology Teaching Methods in the Era of Big Data

Abstract In today’s highly competitive society, most people have different degrees of psychological problems. The development of psychology courses based on this has also received considerable attention. The integration of big data and education has promoted the reform of psychology teaching methods. However, influenced by many factors, psychology teaching methods in China are still traditional. The purpose of this paper is to study the main problems existing in the teaching methods of psychology courses in the era of big data, and to explore the specific strategies to promote the transformation of teaching methods with the support of big data technology. This article first to the era of big data and psychology course has carried on the summary of relevant, and briefly discusses the big data the significance of the development of psychology course, and then take in the form of questionnaire survey as the main experimental investigation on the teaching of psychology, according to the experimental results obtained conclusions about psychology course teaching, based on this, advances the transformation of the mode of teaching by means of big data technologies, establish efficient interactive teaching mode. The experimental results show that the existing teaching methods of psychology course result in low teaching effect, so we must reform the teaching methods.
Digital infrastructures and technoutopian fantasies

Grounding myself in postcolonial and decolonial thinking on Western modernity, I perform an ethnographic reading of digital infrastructures against a transnational and historical context of colonialism. Framing digital infrastructures as sociotechnical constructs, I trace the public, corporate, and state discourse around digital infrastructure development in India. I focus on infrastructural development propped by foreign investment; examining, in particular, Facebook’s Free Basics initiative. In its two-year Indian campaign for Free Basics, to purportedly bring Internet access to a billion new users, Facebook touted the economic and social benefits of Internet access to underserved people. I argue that Free Basics evokes paradigms of national development, modernization, and progressivism that are rooted in technoutopian narratives. At this nexus of corporate and state interest in digital infrastructure lies a technoutopian belief that social, political, and economic problems in the Global South can be resolved by technological advancement, and nationwide issues of inequality and disparity will be ameliorated if poor citizens have access to the Internet.
Two Level Security in Delivering Message Using Encryption and Steganography Techniques

Data hiding is a process that secretly embedding information inside a data source without changing its original quality. Two popular techniques are steganography and cryptography [1]. Steganography is the art and science of hiding information by embedding messages into another image, audio or video [2]. Cryptography use mathematics to encrypt and decrypt data and transform plaintext into cipher text [3]. This study is about securing message by implementing the combination of steganography using Least Significant Bit (LSB) technique and cryptography using Vigenere cipher [4]. The methodology use is cyclic waterfall model. This model is simple and easy to understand. The phases also do not overlap with each other. The testing involves the process of embedding plaintext and cipher text into cover image. The results from the testing are the Mean Squared Error (MSE) and Peak Signal Noise Ratio (PSNR) value of the stego-image. The lower the MSE value and the higher the PSNR value is better image quality.
IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis

In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this description would take the form of a program written in a high-level language. This task is also known as reverse engineering and plays a pivotal role in software engineering, computer security, but also most recently in interpretability. In contrast to prior work, we do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. We approach this problem by iteratively refining a candidate set using a generative neural program synthesis approach until we arrive at a functionally equivalent program. We assess the performance of our approach on the Karel dataset. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding a functional equivalent program in 78% of cases -- even exceeding prior work that had privileged information on the black-box.
Image classification model based on GAT

Abstract In the field of image classification, graph neural network (GNN) is a kind of structured data modeling architecture with larger functions. However, there are still some problems, such as low efficiency of updating nodes, fixed network parameters and the inability to effectively model the information features of some edges in the graph. In order to solve these problems, this paper introduces attention mechanism on the basis of GNN to improve it, proposes a graph attention network (GAT), establishes a double-layer GAT model, and uses regularization method in model iterative training to achieve image classification. The model is applied to three datasets for experiments. The experimental results show that the average classification accuracy of the proposed model is high and it has good application performance.
Computer Simulation Evaluation of Financial Risk Based on Cuckoo Search and SVM Algorithm

Abstract With the rapid development of science and technology, there are more and more large-scale transnational enterprises, which also cause many financial risks. The financial risk level of listed companies will directly affect the sound development of the financial market, which requires listed companies to do a good job in financial risk assessment. However, most of the current financial risks rely on financial statements and other forms, which will be difficult to form a good early warning mechanism. Through the cuckoo search algorithm and SVM algorithm, we can form a new intelligent evolutionary algorithm, which can carry out multi group search and adaptive step size. Through the improved method, we can optimize the parameters of SVM model, which will be applied to the company financial risk assessment. Through the improved cuckoo search algorithm, we can improve the accuracy of financial data classification and prediction, which will better improve the financial environment. First of all, this paper analyzes the algorithm based on cuckoo. Then, this paper analyzes the SVM algorithm. Finally, some suggestions are put forward.
Enhanced Down/Up-Sampling-Based Video Coding Using the Residual Compensation

Down/up-sampling-based video coding is an effective strategy to cope with limited bandwidth for transmission. In recent years, CNN-based super-resolution methods have also been integrated into the above coding strategy. However, it raises a problem of how to balance the calculation and reconstruction quality. In order to alleviate this problem, we propose an enhanced down/up-sampling-based video coding scheme, in which residual data is used to compensate the unrecovered details of the reconstructed video. We first down-sample the video prior to encoding at the encoder and then obtain the residual data by subtracting the super-resolution reconstructed video frame from the original video frame. The residual data is encoded and transmitted together with the low-resolution video sequence. The decoder first reconstructs the encoded low-resolution video with CNN-based super resolution method, and then uses residual data to compensate the missing details of the reconstructed video. To further enhance the perceived quality, we use a quality enhancement network to enhance the quality-compensated video. The experimental results show that the proposed scheme achieves superior quality improvement compared with HEVC anchor and the general down/up-sampling schemes.