Reviewerly/exHarmony huggingface dataset:
<i>Verba Bestiae</i>: How Latin Conquered Heavy Metal

The presence of Latin in heavy metal music ranges from full texts, intros, song and album titles to band names, pseudonyms, and literary quotations. This chapter sheds light on heavy metal's fascination with the history and ‘arcane’ sound of Latin, and investigates its patterns of use in lyrics with the help of Natural Language Processing tools and digitally-available linguistic resources. First, the authors collected a corpus of lyrics containing differing amounts of Latin and enhanced it with descriptive metadata. Next, the authors calculated the richness of the vocabulary and the distribution of content words. The authors processed the corpus with a morphological analyser and performed both a manual and a computational search for intertextuality, including allusions, paraphrase and verbatim quotations of literary sources. The authors show that, despite it being a dead language, Latin is very frequently used in metal. Its historical status appears to fascinate bands and lends itself well to those religious, epic and mysterious themes so characteristic of the heavy metal world. The widespread use of Latin in metal lyrics, however, sees many bands simply reusing Latin texts – mostly from the Bible – or even misspelling literary quotations.
Publicly Verifiable Zero Knowledge from (Collapsing) Blockchains

Publicly Verifiable Zero-Knowledge proofs are known to exist only from setup assumptions such as a trusted common reference string or a random oracle. Unfortunately, the former requires a trusted party while the latter does not exist.Blockchains are distributed systems that already exist and provide certain security properties (under some honest majority assumption), hence, a natural recent research direction has been to use a blockchain as an alternative setup assumption.In TCC 2017 Goyal and Goyal proposed a construction of a publicly verifiable zero-knowledge (pvZK) proof system for some proof-of-stake blockchains. The zero-knowledge property of their construction however relies on some additional and not fully specified assumptions about the current and future behavior of honest blockchain players.In this paper we provide several contributions. First, we show that when using a blockchain to design a provably secure protocol, it is dangerous to rely on demanding additional requirements on behaviors of the blockchain players. We do so by showing an “attack of the clones” whereby a malicious verifier can use a smart contract to slyly (not through bribing) clone capabilities of honest stakeholders and use those to invalidate the zero-knowledge property of the proof system by Goyal and Goyal.Second, we propose a new publicly verifiable zero-knowledge proof system that relies on non-interactive commitments and on an assumption on the min-entropy of some blocks appearing on the blockchain.Third, motivated by the fact that blockchains are a recent innovation and their resilience in the long run is still controversial, we introduce the concept of collapsing blockchain, and we prove that the zero-knowledge property of our scheme holds even if the blockchain eventually becomes insecure and all blockchain players eventually become dishonest.
Online Signature Verification using Time Warp Edit Distance based kernel

In this paper, we propose a new method for dynamic signature verification. We adapt a metric known as Time Warp Edit Distance (TWED) for computing the similarity score between two online traces of signature data samples. We derive a set of secondary features from the online trace of a user's signature and further propose to employ a sequence of Autoregressive coefficients. The TWED algorithm is applied on the set of features obtained from the online traces of signatures. In our approach, we use Support Vector Machine for classification of the samples as either genuine or forgery. We formulate a new kernel function based on the TWED metric in the SVM framework for verification. To demonstrate the efficacy of our proposed algorithm, we perform a set of experiments on the publicly available MCYT-100 database. We present improvements over several state of art works.
Application of improved SURF algorithm in real scene matching and recognition

Aiming at the problems of insufficient extraction of stable features by SURF algorithm, high matching error rate, and loss of correct matching information during matching purification, this paper improves from two aspects: feature extraction and feature matching. First multi-scale FAST corners is constructed to make the extracted corners scale-invariant; second, use the improved CANNY algorithm to increase the number of stable features according to the double threshold adaptively set by the Maximum Inter-Class Variance Algorithm (OTSU); then the SURF feature descriptor is constructed; finally, the feature matching effect is improved by combining KNN bidirectional matching and RANSAC algorithm. The experimental results show that the improved algorithm has better results than the original algorithm in both the number of extracted feature points, stability, and final matching accuracy.
Toward a head movement‐based system for multilayer digital content exploration

Abstract In this article, we propose a novel technique based on Head Movement tracking to explore multilayer digital content. We extend an existing method by Kazemi et al. dealing with the extraction of facial landmarks to define the “head‐gaze” of the user. We use the “head‐gaze” to calculate the users' on‐screen coordinates. Hovering the cursor over an interactive area for a given time threshold allows users to explore the next layer contents. Our experimental sessions allowed us to measure the technique's level of control and usability. Our results were promising, and users were able to interact with considerably small regions. Furthermore, our lightweight method can be used with a low‐cost camera or webcam and a wide range of screen sizes and distances.
Simple Estimation of Red Channel’s Transmittance and Balanced Color Correction for Underwater Image Enhancement

This paper proposes an underwater image clear processing algorithm based incorporation of absorption effect, simple estimation of red channel's transmittance and a balanced color correction strategy. Firstly, in the imaging model for underwater images, we define the ratio of scattering coefficient to absorption coefficient and quantize the absorption effect Then, after the scattering rate of the blue and green channels is estimated, their transmittance is obtained, and the linear relation between the transmittance of red channel and that of blue channel and green channel is established, and a simple method to solve the parameters is proposed Finally, according to the degree that the pixel values of each channel deviate from the average brightness of the natural image, a balanced color correction algorithm suitable for underwater image enhancement is designed to improve the contrast and brightness. Subjective and objective performance evaluations demonstrate that our method is effective and efficient.
Smart Vending Machine System Prototyped with Deep- and Machine-Learning Technologies

This paper proposes a smart vending machine system combined with deep learning and machine learning technologies. The proposed system is combined with temperature and camera sensor to obtain consumer without individual information and upload this information to cloud server. The system uses face recognition with deep learning to obtain the gender information. It uses the k nearest neighbors (KNN) machine learning method to group based on temperature, time, price and gender information. The proposed system relies on grouping information to dynamically adjust price in real time.
PMTRU: An Efficient and Resistant Variant of the NTRU Public Key Cryptosystem

This paper proposes a novel cryptosystem (PMTRU) that is a variant of the NTRU public key cryptosystem. PMTRU combines the advantages of the NTRU and its other variant, MATRU. We prove analytically that our cryptosystem improves the speed of encryption and decryption procedures, and significantly increases the security level of the private key. Compared to NTRU and MATRU, PMTRU greatly improves resistance against brute-force and lattice-based attacks, while decreasing the memory space required to register the public key and ciphertexts.
Research on the University Fall Enrollment Strategies Based on Apriori Algorithm

Abstract Using the historical enrollment data of colleges and universities to mine, discover the relationship between the potential data, and use the results of data mining to provide reasonable suggestions for enrollment policies, which is of great significance to college enrollment. This paper aims at the allocation of enrollment colleges and the allocation of college students in the process of autumn enrollment in colleges and universities, through the Apriori algorithm in data mining, mining historical enrollment data for 3 consecutive years after data cleaning. Based on the frequent itemsets obtained from the experimental results, a reasonable allocation of college places and student college allocation plan was made.
Energy Consumption and Economic Development in Sub-Saharan African Countries: Role of Governance Quality

The objective of this paper is to analyze the role of governance quality on the relationship between economic growth and energy consumption in a panel of twenty ( 20) sub-Saharan African (SSA) countries over a period from 1996 to 2017.Techniques such as the generalized method of moments (GMM) in dynamics and the Granger test were utilized.The results of the estimations show that energy consumption is an essential factor in the economic growth of SSA countries and that the quality of governance is a catalyst of this relationship.The policy implications of these results are elaborated.
Hand motion capture system based on multiple inertial sensors

It is important for many applications to capture hand movements with high accuracy to achieve the natural human-computer interaction, such as games, robotics, rehabilitation, and virtual reality (VR). An ideal hand motion capture solution requires good mobility, unobtrusiveness, and high accuracy. In this demo, we show a hand motion capture system including inertial sensor based data gloves with the square-root cubature Kalman Filter multi-sensor fusion algorithm and a biomechanics sensor-to-segment calibration method. The absolute error of the joint angle is measured. As the result, the proposed system shows good accuracy in both static (RMSE = 1.5°) and dynamic (RMSE = 6.6°) conditions.
Context-aware placement of items with gaze-based interaction

Appropriate product placement significantly influences how viewers easily find their favorites, especially when they try to select products from digital signage displays. This increases the demand for dynamic categorization and optimal placement of items according to the context in which viewers explore their preferred choices. In this paper, we present an approach for optimizing the placement of items by respecting the underlying context in the search for favorites. Our approach starts with formulating the static placement of items as a constrained optimization problem, in which we incorporate design rules that highlight the underlying categorization of the items. We then extend this idea to accommodate dynamic placement according to the context in which users explore their preferred choices. This is accomplished by adaptively adjusting the priority of each item based on the distribution of visual attention obtained by an eye-tracking device. In particular, we construct a context map for understanding the relationship between the items by taking advantage of topic-based text mining techniques. We provide several examples of gaze-based interaction to demonstrate the capability of the proposed approach, which is followed by a discussion on possible directions for future research.
Reconfigurable MAC Systolic Array Architecture Design for Three-Dimensional Convolution Neural Network

Nowadays, Convolutional Neural Network (CNN) is widely used in many applications. Plenty of MAC operations may cause long latency for CNN. To solve this problem, 3D stacking of CNN (3D CNN) is applied. However, 3D CNN may lead to the thermal problem. In this work, we propose a reconfigurable MAC systolic array architecture (RMACA) for 3D CNN. If the hotspot in the 3D CNN occurs, partial MACs can be turned off by using RMACA. Thus, the heat accumulation can be reduced and the hotspot can be removed. RMACA is implemented by TSMC 90-nm CMOS technology, 21,670 gates is required to realize 4x4 CNN with RMACA.
A Two Stage Approach for AMR Parsing Using the Concept Inference Order

In this paper we introduce a simple, pipeline graph-based approach for AMR parsing. Transition-based parsers have been researched intensively for the task of AMR parsing since the introduction of AMR and its associated sembank in 2013, but more recently there's been a shift towards graph based approaches, which seem to do better on the task. However, these graph-based approaches often employ large models, trained end-to-end. We intend to see the limitations of a small, pipeline architecture. We use an attention-based sequence-to-sequence model for predicting an ordered list of nodes, then predict the heads for each node in a subsequent step.
A Collision-Free Framework for Navigation of Nonholonomic Vehicle Systems

The task of highly effective navigation is certainly one of the critical technologies of autonomous vehicles that have been widely investigated by researchers. In this paper, a navigation framework with path planning and tracking is presented, where a feasible and collision-free trajectory is generated for nonholonomic vehicle systems. In terms of path planning, an LQR-RRT* algorithm is applied to plan a path without collision. It is an optimal sampling-based planning algorithm with linear quadratic regulation-based heuristics, and able to tackle some rather complicated or under-actuated dynamic systems. In order to improve the feasibility of the planned path and compensate for the deterioration of optimality due to sampling times, the approach is taken where a cubic spline path is additionally generated. The path tracking problem is formulated as a constrained optimization problem for the nonholonomic vehicle system, and then the required steering angle and acceleration is computed to avoid potential collisions. The results of the simulation experiments clearly demonstrate the effectiveness of the proposed navigation framework.
Research on Emotion Classification of Film and Television Scene Images Based on Different Salient Region

With the rapid development of intelligent image processing and computer vision, interpreting and extracting emotion information from images have become a hot research topic, especially the studies on film and television scene images with rich expressions and certain feelings from the aspect of audience. To investigate the distribution of emotions and its affect on the result of machine learning, this paper established the emotion classification models with different salient and non-salient regions based on self-built dataset from film and television works. Three salient extraction algorithms were used to the regional segmentation, and the color and texture features of each region were extracted. Emotion classification models were established based on SVM and RF algorithms respectively. Since features of the salient region could not cover all the information of the original image, the prediction accuracy of the unprocessed image was the highest. Nevertheless, when models made an error in the emotion prediction of the original image, the experimental results of salient region could get the correct answer under the certain circumstances. Through the analysis, the areas of interest can be used for emotion prediction to reduce the redundancy; however, to make it more accurate, the appropriate salient region extraction algorithm should be selected for different types of images and features. Our study provides data and theoretical guidance for image understanding and retrieval, and it has practical significance for more detailed classification based on the affective sensation.
Query Segmentation and Tagging

Query tagging is an important step for query understanding. It applies traditional natural language processing techniques on query strings. Specific challenges are raised due to the shortness of query strings. In this chapter, we describe techniques proposed in the existing literature on how to achieve meaningful query tagging in the following areas: query segmentation, query syntactic tagging, and query semantic tagging.
Cooking Activity Recognition with Convolutional LSTM Using Multi-label Loss Function and Majority Vote

This paper reports the Cooking Activity Recognition Challenge by team Rit’s cooking held at International Conference on Activity and Behavior Computing (ABC 2020). Our approach leverages the convolutional layer and LSTM to recognize macro activities (recipe), and micro activities (body motion). For micro activities consisting of multiple labels in a segment, loss is calculated using BCEWithLogistsLoss function in PyTorch for each body part, and then the final decision is made by majority vote of classification results for each body part.
The Monte Carlo Algorithm for Image Segmentation Based on the MRF Model

Image segmentation is a key technique in the image processing and a classic problem. A Monte Carlo segmentation algorithm based on the Markov Random Field (MRF) image model is proposed to randomly initialize the model parameters, so as to avoid the over-dependence of the algorithm on the initial value and overcome the shortcomings of the local optimal solution of the existing iterative algorithm. Firstly, the MRF model can make full use of the neighborhood relationship of pixel space to obtain the data field information of the image. Then according to the Bayesian theory, the prior knowledge of images is transformed into the prior distribution model. Finally, the Monte Carlo segmentation algorithm is used to iterate until the maximum posterior probability is reached, thus, the distribution of image labels is obtained, that is, the process of image segmentation is completed. The segmentation experiment shows that the Monte Carlo algorithm can overcome the shortcoming of the traditional iterative algorithm, which is trapped in the local optimal, and can segment the image in a more complete and detailed way, effectively realize the accuracy of segmentation, and improve the speed of image segmentation.
Semantic Classification of EMF-related Literature using Deep Learning Models with Attention Mechanism

Semantic classification of scientific literature using machine learning approaches is challenging due to the lack of labeled data and the length of text [1, 4]. Most of the work has been done for keyword based categorization tasks, which take care of occurrence of important terms, whereas the semantic classification is to learn keywords as well as the meaning of sentences. In this study, we have evaluated neural network models on a semantic classification task using a large amount of labeled scientific papers listed in the Powerwatch study. We have conducted neural architecture search to find the most suitable model for the task. In the experiment, we have compared classification accuracy of various neural network models. In addition, we have employed a Fully Convolutional Neural Network (FCN) to implement attention mechanism for the semantic classification of EMF-related literature. The experimental result showed that the FCN-based attention model was able to identify important parts of input texts.
Joint Online Coflow Optimization Across Geo-Distributed Datacenters

Growing data volumes have been generated, stored and processed across geographically distributed datacenters. Processing such geo-distributed datasets should consider task or job placement, heterogeneous available link bandwidth, and routing decisions. Thus optimizing the transmissions of inter-datacenter flows, especially coflows that capture application-level semantics, is important. However, prior solutions on coflow scheduling either merely ignore routing or fix the endpoint placement. This article focuses on the problem of jointly considering endpoint placement, coflow scheduling, and coflow routing to minimize the average coflow completion time (CCT) across geo-distributed datacenters. This paper first presents the system model and problem formulation, then derives a randomized approximation algorithm for a single coflow. After that it also proposes an online algorithm to handle the complex cases of multiple coflows. Through theoretical analysis, a proof that the proposed algorithms have a non-trivial competitive ratio is given. Results from extensive simulations demonstrate that the proposed algorithms can significantly reduce the CCT of coflows and at the same time, have similar algorithm run time, compared with the state-of-the-art solutions.
Intelligent System for Human Context Recognition

Human-Centered computing technique aims to understand human behavior and can serve in fitness observing aging care, and numerous different spaces. Recognizing Human Context at their normal routine life is a very challenging task as behaviors vary from person to person. The current methods available for the detection of human activities are less accurate, inefficient, and are not effective to be used for recognition purposes. This study focuses on the recognition of human activities by using MPU-6050 signals, which is an efficient, cheap and relatively new method as compared to the existing practices. A total of 37 subjects were involved in this work for data acquisition and asked them to do their activities as usual while carrying the MPU-6050 sensor on their chest. Empirical Mode Decomposition (EMD) was employed for the de-noising the signals. Extensive experimentation resulted in the selection of two features, which were giving the best intra-class difference through Support Vector Machines (SVM). An accuracy of 100% was achieved using Quadratic kernel for SVM.
Parking Costs—A Family Affair

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Image Classification Schemes Based on Statistical Moments of Wavelet and Sliced Radial Energy Distribution of DCT

Abstract Texture recognition is used in various pattern recognition applications and texture classification that possess a characteristic appearance. This research paper aims to provide an improved scheme to provide enhanced classification decisions and to decrease processing time significantly. This research studied the discriminating characteristics of textures by extracting them from various texture images using discrete Wavelate transform (DWT) and discrete Cosine transform DCT. Two sets of features are proposed; the first set was extracted using the traditional DCT, while the second used DWT. The features from the Cosine domain are calculated using the radial distribution of spectra, while for those extracted from Wavelet was statistical distribution of various relative moments. Four types of Euclidean distance metrics were used for classification decision purposes. The considered method was applied on 475 classes of textures belonged to 32 sets from Salzburg Texture Image Database, each set holding 16 images per class, so the a total of 7600 images were tested. Each image was separated into three bands of color component (i.e., red, green, blue). Concepts of average and standard deviation were calculated to determine the inter/intra scatter analysis for each feature to find out the best discriminating features that can be used. The final result of DWT was 99.98 for the testing sets and 99.71 for the training sets, while the final result of DCT was 99.06 for the testing sets and 96.77 for the training sets.