Reviewerly/exHarmony huggingface dataset:
Instrumentality Versus Awareness: Rethinking the Reverse Side of Artificial Intelligence

Every epoch with exciting discoveriesDiscoveries opens up a unique and stunning worldview. Long ago, the attention of archaic man was on Mother Nature. With the utmost amazement and reverence, he prayed for the majesty of nature’s creatures and empowered himself with her magical amulets. Later, man’s attention was attracted to objects in the sky. He worshipped the sun, the moon, and constellations of starsStars. He ritualized cosmic rhythms and formed the cyclicality of daily life. Later, in ancient Greek tragedy, onto the stage of divine circumstances, suddenly came the man himself—a hero. In the Age of Enlightenment, the man unexpectedly became an infinite potential capable of discovering the whole universe. However, with the advancement of techno-scientific transformations, the focusFocus is changed again. It is no longer on the human, but on technology, the development and training of the instrument. Moreover, again, reverently stunned, but this time adorned with the symbolic amulets of technology instead of flora and fauna, the man looks at the immense scaleScale of technology. He marvels at the growthGrowth of “unlimited” possibilities of artificial super-intelligence. The changes brought by artificialIntellectintelligence intelligenceArtificial intelligence are claimed to be radically different, and in fact, they are shaping social, economic, and political reality, transforming the entire human world. However, are they radically different? What underpins artificial intelligenceArtificial intelligence? What can it change and what cannot? With the rise of artificialIntellectintelligence intelligenceArtificial intelligence, we are witnessing many concerns, but also positive opportunities—new human relationsHumanhuman relations with knowledge, and the emergence of a decentralized society, which is focused not solely on consumerism, but also on sharing. [Adopted from https://www.chrismadden.co.uk/cartoon-gallery/turing-test-cartoon-turing-test-being-failed-by-a-human/ , image licensed to the author for free use]. The code of this chapter is 01101110 01101100 01010101 01101001 01100101 01110010 01110110 01100001 01110011.
Smart Tourims: Decision Support Systems In The Strategic Location Of Inn/Hotel For Travelers Using The Naive Bayes Algorithm

Abstract This research makes the system in decision making for tourists who will visit an area. In making decision decision systems using the Bayes algorithm. Bayes’s theorem was put forward by a 1763 British Presbyterian minister named Thomas Bayes. In this study Bayes’s theorem is used to calculate the probability of occurrence of an event based on the influence obtained from the observations. In this paper, the Naive Bayes algorithm is used to support the Decision Making System (DSS) to determine the best and strategic location in determining hotel accommodations, especially for tourists who are associated with having mediocre funds in traveling to Lombok Island. In this study applied a number of criteria and data sets obtained from questionnaires from some visiting tourists. There are 3 variables including Hotel Prices, Distance, and the presence or absence of transportation from tourist destinations, from the three variables, to the training dataset. So that in the future there will be questions in the form of data testing to answer the decision whether the hotel can be take or not by looking at the highest probability value.
The impact of the operation mode of CRACs on the temperature distribution of hot and cold aisles for the data center room

Redundancy is generally considered in the computer room air conditioners (CRACs) of the data center room. The position of the CRAC in the on state and the operating air volume have a great influence on the temperature distribution. The temperature distribution of the data center room will affect the failure rate of the servers and the energy consumption of the air-conditioning system. Taking a typical data center room as an example, this paper uses numerical simulation to study the influence of the on/off state and air volume of CRACs on the temperature distribution of the hot and cold aisles and energy consumption. The calculation results show that we should preferably turn off the staggered CRACs on the opposite side and close to the middle position under the partial load. When the IT load is unevenly distributed, it is irrational to use the setting method of even distribution of the air volume of each CRAC. The air volume of each CRAC should be appropriately adjusted based on the distribution characteristic of IT load to reduce the energy consumption.
Research on dark channel dehazing of single-image based on non-dispersive infrared (NDIR) detection technology

This paper presents an experimental study on the non-dispersive infrared (NDIR) detection technology and dark channel dehazing technology. Based on the analysis of Beer-Lambert Law and differential carbon dioxide detection principle, this paper proposes an atmospheric light value estimation algorithm based on NDIR detection technology. First, the change characteristics of the gas concentration in indoor smoky environment are collected and analyzed. Then appropriate weighting coefficients are chosen based on the gas characteristics to estimate the atmospheric light value. Finally, the digital image dehazing technology through dark channel prior is used for calculation to obtain a haze-free image with high quality and high resolution. The experiment in this paper proves the feasibility of combining NDIR detection technology with dehazing technology, and its ability to improve image quality and achieve better restoration effect.
GSS: Graph-Based Subspace Learning with Shots Initialization for few-shot Recognition

Previous methods of few-shot Learning mostly solve different few-shot recognition tasks in an identical feature space. But identical features are hard to fit various tasks. Some works show that learning a unique subspace for each few-shot recognition task can improve the signal-noise ratio (SNR) of the features and boost the performance. However, there are still two problems remaining. First, in constructing the subspace for few-shot task, often some information (embeddings of queries or labels of shots) are discarded. Second, the eigendecomposition of covariance matrix is usually needed, which degrades the efficiency of the whole model. In this paper, we propose Graph-based Subspace learning with Shots initialization (GSS) for few-shot recognition to learn a better subspace efficiently. In GSS, the bases of the subspace are directly initialized with labels based on shots (given labeled samples) and iteratively updated for better discrimination based on a graph that connects bases and all samples. Extensive experiments on four few-shot benchmark datasets show that GSS reports better performance and higher efficient compared with previous subspace based methods and achieves state-of-the-art performance.
Clustering and Weighted Scoring Algorithm Based on Estimating the Number of Clusters

Imbalanced datasets are still a big method challenge in data mining and machine learning. Various machine learning methods and their combinations are considered to improve the quality of the classification of imbalanced datasets. This paper presents the approach with the clustering and weighted scoring function based on geometric space are used. In particular, we proposed a significant modification to our earlier algorithm. The proposed change concerns the use of automatic estimating the number of clusters and determining the minimum number of objects in a particular cluster. The proposed algorithm was compared with our earlier proposal and state-of-the-art algorithms using highly imbalanced datasets. The performed experiments show that the proposed modification is statistically better for a larger number of reference classifiers than the original algorithm.
Research on Malicious URL Detection Based on Feature Contribution Tendency

With the rapid development of the Internet, the number of malicious URLs is growing, too. It brings great security threats to people. The current malicious URL detection methods based on machine learning don't select the features effectively, so it will directly affect the classification effect of the model. In order to avoid the problem of weak generalization ability caused by the training of small amount of samples, a large number of samples are used for experiments. In this paper, Random Forest is optimized based on feature contribution and hyperparameter optimization, and a large number of sample experiments show that the detection efficiency of the model has been significantly improved. The detection precision reaches 94.85%, and the AUC value reaches 96.51%. It has high application value for malicious URL detection.
Transfer Learning with Demonstration Forgetting for Robotic Manipulator

Deep learning and especially deep reinforcement learning usually require huge amount of data for training and simulators using is perspective approach to provide this data. Model trained in simulator can be transferred on real robot without wasting a lot of time to collect data. Training in simulator also allows using of different techniques to speed up convergence and increase resulting performance. One of them is to train feature-based model with access to the whole information about the environment and use it as expert for main image-based model. This reduces earning time and the computational costs that are necessary to obtain quality results with image-based model. In this work we improve idea of behaviour cloning feature agent and make it more flexible with using of expert demonstrations forgetting. We conducted experiments on transfer learning for a robotic manipulator that interacts with complex objects and compared them with classic off-policy approaches.
Quantum illumination radar with entangled coherent states

There has been a great interest in quantum metrology (e.g., quantum interferometric radar) due to its applications in sub-Rayleigh ranging and remote sensing. Despite interferometric radar has received vast amount of attentions over the past two decades, very few researches has been conducted on another type of quantum radar: quantum illumination radar, or more precisely quantum target detection. It is, in general, used to interrogate whether the low-reflectivity target in a noisy thermal bath is existed using quantum light. The entanglement properties of its emitted light source give it a unique detection advantage over the classical radar. Entangled coherent state (ECS), as a class of quantum states with high entanglement robustness in noisy environments, has been widely used in several fields of quantum science such as quantum informatics, quantum metrology . In this paper, we investigate the target detection performance of quantum illumination radar based on three different types of ECS states. We employ the two-mode squeezed vacuum state (TMSV) and the coherent state as benchmarks to compare and analyze the relationship between the entanglement strength of the three types of ECS states and their quantum illumination detection performance. We found that the detection performance of the three ECS states is better than that of the coherent state. However, it is inferior to that of the TMSV state when the target is of low reflectivity. The emitted photon number is much smaller than the background noise (we call this as “good” illumination conditions). On the contrary, quantum illumination radar has no obvious advantage over coherent state radar for target detection under other illumination conditions; further, the detection performance of these three types of ECS states is not evidently related to that of the TMSV state and the coherent state. Finally, we reveal that the target detection performance of quantum illumination for the first two types of ECS states can be determined by their entanglement strength under “good” illumination conditions by adjusting the inter-modal phase of these two ECS states while keeping the emitted photon number constant. Under other illumination conditions, there is no evidence to demonstrate the entanglement strength of ECS states being associated with their target detection performance.
Direct state measurements under state-preparation-and-measurement errors

Direct state measurement (DSM) is a tomography method that allows for retrieving quantum states’ wave functions directly. However, a shortcoming of current studies on the DSM is that it does not provide access to noisy quantum systems. Here, we attempt to fill the gap by investigating the DSM measurement precision that undergoes the state-preparation-and-measurement (SPAM) errors. We manipulate a quantum controlled measurement framework with various configurations and compare the efficiency between them. Under such SPAM errors, the state to be measured lightly deviates from the true state, and the measurement error in the postselection process results in less accurate in the tomography. Our study could provide a reliable tool for SPAM errors tomography and contribute to understanding and resolving an urgent demand for current quantum technologies.
Cosaliency Detection and Region-of-Interest Extraction via Manifold Ranking and MRF in Remote Sensing Images

Saliency-based region-of-interest (ROI) extraction is significant for the interpretation of remote sensing images (RSIs). Recently, cosaliency detection has shown its superiority of better extraction of common ROIs by using both intraimage and interimage cues. However, most existing methods still suffer from the complex backgrounds of RSIs, resulting in incomplete ROI extraction, many false positives, and blurred boundaries. In this article, we propose a cosaliency detection framework via manifold ranking and the Markov random field (MRF) for RSIs to address these problems. First, we design a two-stage manifold ranking schema for converting single-image saliency maps (SISMs) to multi-image saliency maps (MISMs). This step takes full advantage of the correlation between images to improve the integrity of ROIs and reduce false positives. Second, we locally fuse saliency proposals by minimizing the energy function in an MRF. The design of the energy function comprehensively considers the global and local performance of saliency proposals to assign appropriate fusion weights. Finally, we generate the ROI masks by thresholding the cosaliency maps. Our approach is evaluated on four RSI datasets and compared to the state-of-the-art methods. Experimental results demonstrate the effectiveness of our model in both cosaliency detection and ROI extraction.
Educational Activity in Higher Educational Institutions in the Context of Modern Requirements

The article discusses the features of students' educational activity in the aspect of modern requirements in the system of higher education. The methodological foundations of this process and its specificity are discussed. Particular attention is paid to the innovative learning activities of students. The topicality of the article is due to the need to identify a number of theoretical grounds for organizing student’s educational activity in a modern university. Educational activity is the main type of student’s activity, and the quality of education of university graduates depends on its methodologically corrected implementation. The purpose of the article is to identify individual theoretical aspects of students' educational activity in the light of modern requirements. The following research methods were used in writing the article: analysis of literature on the problem, our own experience in teaching at the university. The result of the article is the development of individual theoretical provisions relating to the improvement of students' educational activity in the light of modern requirements. The results of the article may be applied in studies aimed at improving the quality of education and in the practical work of teachers of higher education. The article is devoted to one of the important problems of the higher education and is aimed at improving the quality of university education.
Efficient Algorithms for Encrypted All-gather Operation

As more High-Performance Computing (HPC) applications that process sensitive data are moving to run on the public cloud, there is a need for the cloud infrastructure to provide privacy and integrity support. In this work, we investigate how to add encryption to all-gather to protect internode communication. This task is challenging since encryption is often more expensive than communication in contemporary HPC systems. We derive performance bounds for encrypted allgather, and develop new algorithms that meet the theoretical lower bounds. Our empirical evaluation on production systems demonstrates that the new algorithms achieve substantially better performance than the naive approach.
Incorporating Attributes Semantics into Knowledge Graph Embeddings

More and more work has focused on incorporating different kinds of literals into Knowledge Graph to promote the performance of knowledge embedding. These literals contain numeric literals, text literals, image literals and so on. These additional descriptions are connected to the entities through certain attributes. To incorporate numeric literals, some methods combine the embeddings of literals part with the traditional part - embeddings of entities. However, in the construction of literals embeddings, these existing methods consider the differences of these attributes: one dimension represents one attribute. But they ignore semantic meanings of attributes themselves. In this paper, we propose two methods to incorporate attributes semantics into knowledge graph embeddings from two perspectives: LiteralEAN and literalE-AT. They concatenate with the embeddings of numeric literals by different ways. Furthermore, their extension model LiteralE-C is also proposed as having a more comprehensive representation of attributes semantics. In an empirical study over two standard datasets FB15k and FB15k-237, we evaluate our models for link prediction. We demonstrate that they show an effective way to improve LiteralE and achieve state-of-the-art results. In ablation experiments, we find combined models do better than their singular counterparts in most cases.
An Ontology Engineering Case Study for Advanced Digital Forensic Analysis

Digital forensics faces some serious challenges at present. Those challenges include ever-increasing processed data volumes, heterogeneous nature of evidentiary artifacts, multiple data sources incompatible with each other, and more. Most of the commonly used forensic tools do not provide an intuitive and convenient way of accessing the data. At the same time, storage types such as relational databases cannot fully satisfy the need to store heterogeneous objects and efficiently provide access to specific properties. In this paper, we present an ontology-based approach to processing digital evidence and handling the course of digital investigation. The proposed system, named ForensicFlow, provides means of automatic artifact extraction from different origin sources, namely volatile and non-volatile memory, and reconstruction of event-artifact graphs in order to assist forensic experts in quickly and efficiently outlining the scope of an incident, and conducting an investigation.
On the asymptotic order of circuit codes

We prove that the maximum length of an n-dimensional circuit code of spread k equals 2n−Θ(log2n) as n→∞, with the implied constant depending only on k.
Voice Conversion Using Spectral Mapping and TD-PSOLA

In this paper, we propose a novel approach for a voice conversion system that makes effective use of spectral characteristics and excitation information, to optimally morph voice. This work addresses some key issues that are not adequately addressed in reported literature and achieves a more holistic voice conversion system. This is achieved using a strategic combination of line spectral frequencies (LSFs) to minimize the effects of over smoothing, a neural network for performing nonlinear spectral mapping and time-domain pitch synchronous overlap add to account for the interaction of excitation signal with the vocal tract. Within this proposed system, two different methods of pitch modification have been suggested, and the performance of these is compared with existing models of comparable complexity. The proposed methods have an average LSF performance index of 0.4082 and 0.4008, respectively, which is higher than existing similar work reported.
BLER performance evaluation of an enhanced channel autoencoder

The concept of using autoencoders (AEs) to represent wireless communication systems as an end-to-end reconstruction task that optimizes the transmitter and receiver components simultaneously in a single process has attracted the attention of wireless practitioners worldwide. This is attributable to the flexibility, and convenience of representing complex channel models. However, owing to the characteristics of deep neural networks (DNNs), as the AE learns the representation of the channel, overfitting limits its performance. In this paper, we propose RegAE, a regularized DNN architecture that overcomes the overfitting limitation in AEs and reduces their training complexity, which are characteristics of models with higher dimensions. We demonstrate that RegAE improves the block error rate (BLER) as compared with equivalent models from the literature. Thereby, it achieves a performance (1) better than that of a 4∕7 rate Hamming code with a 16 phase-shift keying (16PSK) modulation under an additive white Gaussian noise (AWGN) channel, (2) comparable to that of a 4∕7 rate maximum likelihood decoding (MLD) with a Eb∕N0 range from 1 dB to 5 dB, and (3) equivalent to that of an uncoded binary phase-shift keying (BPSK) modulation over a Eb∕N0 range from 0 dB to 10 dB.
GPU based multi-scale depth map calculation for 3D reconstruction

Abstract When using multi-view stereo (MVS) method to calculate depth map for reconstructing dense 3D model, the model precision is often influenced by low-texture areas. To solve this problem, in this paper, we present an efficient multi-scale depth map based MVS method. First, the coarsest scale depth map is calculated which can easily capture the rich texture information in low-texture areas. Then we upsample this depth map to higher scale, and four forward-backward reprojections are used to reduce the error of depth difference between the last scale and the current scale. In addition, a GPU and CPU based cooperative optimization architecture is built to optimize and accelerate depth map computation in Patch Match (PM) stage. Experimental results show that, the proposed method can reconstruct quite accurate and dense point clouds compared to state-of-the-art methods.
Multi-Layer Perceptron Neural Network with Feature Selection for Software Defect Prediction

Software is continuously evolving and hence it is essential for the production of quality and stable software by every software provider. Recently there is a paradigm shift in how software is designed. One of the biggest challenges of software engineering is predicting defects in software modules, to save quality testing time. As software development challenges and constraints rise, unexpected effects such as failure and errors decrease the consistency of software and user loyalty, rendering error-free software more complex and frustrating. In this paper, we analyze the use of Multi-Layer Perceptron Neural Network [5] (MLP-NN) for the efficient prediction of defects. We have also executed the MLP-NN with a subset of features selected using popular feature selection methods. The model was evaluated on 5 datasets from the AEEEM dataset. The results were compared with other common classifiers like Logistic Regression, MLP-NN, and Random Tree. The findings indicate that feature selection has a major role in increasing the accuracy of prediction. Our model had higher accuracy in few cases while at par with others in some.