Reviewerly/exHarmony huggingface dataset:
Naive Bayes Image Classification Based on Multiple Features

In the field of computer science, in order to realize the classification of images, a naive Bayes method based on multiple features has been proposed. This method is widely used and rich. It uses four features, such as extracting gray histogram features, SIFT features, SURF features, and reducing the dimensions of the dataset from data image sets. Naive Bayesian methods are used to obtain The accuracy, recall, and F1 value of the image for each feature. This paper analyzes the application and feature comparison of Naive Bayes method in images, and shows that image representation using SURF feature description can achieve better classification results.
Classifying Network Vendors at Internet Scale

In this paper, we develop a method to create a large, labeled dataset of visible network device vendors across the Internet by mapping network-visible IP addresses to device vendors. We use Internet-wide scanning, banner grabs of network-visible devices across the IPv4 address space, and clustering techniques to assign labels to more than 160,000 devices. We subsequently probe these devices and use features extracted from the responses to train a classifier that can accurately classify device vendors. Finally, we demonstrate how this method can be used to understand broader trends across the Internet by predicting device vendors in traceroutes from CAIDA's Archipelago measurement system and subsequently examining vendor distributions across these traceroutes.
Hybrid Animation: Implementation of Two-Dimensional (2D) Animation

Abstract In the 21st century, much progress has been made following the industrial revolution 4.0. Therefore, the field of animation has also been injected into technological advances. The transmission of information is difficult to convey accurately and interactively to users. In providing users with more exposure and convenience in a more attractive and convenient way. Therefore, animation and motion capture are the best options for use today. As a result, the results of this project can have a huge impact on the animation industry in making video or animation more interesting in 2D animation.
Research on the Application of Big Data Statistical Analysis in the Field of Economic Management

Abstract Nowadays, the Internet and computer are becoming more and more popular in the society. People become the transmitter and receiver of information through the Internet. This way has changed the traditional life, work and study mode of human beings. With the continuous increase of data and information, its application value gradually increases. Researchers use big data statistics to help people manage their personal data. This paper analyzes the application of big data analysis in the field of economic management and makes the corresponding summary.
A Conceptual Framework for Database Anti-forensics Impact Mitigation

The emergence of databases has become one of the most important achievements in the field of software. Databases are at the core of information systems (Online and Offline), and this has fundamentally made them a target for malicious intents because they contain sensitive information. Digital forensics tends to outwit such malicious activities by providing provable evidence for prosecution. On the other hand, criminals develop and promote counter forensic measures to reduce the quality and quantity of forensic data known as “Anti-forensics”. Such counter measures tend to jeopardize forensics efforts at all stages to ensure that court admissible evidences are not acquired. The aim of this paper is to explore database anti-forensics agents, highlight the challenges and or negative effects they pose at various stages of the database forensics processes, present a taxonomy for database anti-forensics impacts, provide recommendations on how these impacts can be mitigated, and directions for future research opportunities.
Connecting Language, Gender, Literacy and Facebook: A Case Study with Multilingual Teenagers in Pakistan

The aim of this research is to examine the interrelationship among language, gender, and literacy practices of multiling ual y outh on F acebook in P akis tan. T he data, c ollec ted primarily thr ough obs er vations , c ompris ed the participants literacy practices on Facebook which was observed for three months. A convenience sampling technique was used for drawing the sample. Ten participants, five boys and five girls, participated in this research. Data analysis reveals that social media is widely used by young boys and girls to exhibit their gendered identity through language choices, content and purpose of using social media. The research is informed by recent research on the ethnogr aphy of digital literacy pr actices .
The evaluation of compensation techniques in i-vector modelling for sound-based biometric recognition system

Abstract i-vector subspace modelling is one of the recent methods that has become attractive to sound-based biometric recognition domain. This method provides a benefit of modelling both intra-domain and inter-domain variability into one low dimensional space. This paper focuses on the analysis of i-vector channel compensation techniques for the purpose of improving the i-vector sound-based biometric recognition performance. This work was mainly motivated by the need to quantify the impact of different compensation techniques to the i-vector performance specifically towards the fusion compensation approach. The performances of six channel compensation techniques: (a) whitening, (b) Within Class Covariance Normalization (WCCN), (c) Linear Discriminant Analysis (LDA), (d) whitening and WCCN, (e) whitening and LDA and (f) WCCN and LDA have been investigated in this study. 2656 syllables of bio-acoustic sounds are used as experimental data and parameters of the system are initially tuned with different GMM component sizes i.e. 16, 32, 64 and 128 number of Gaussians. To the end, we assess the effect of the tuned parameter and observe the recognition rate. Experimental results reveal that the accuracy of i-vector with the fusion of WCCN and LDA compensation outperforms other compensation approaches with result of 92.00%. Consequently, these findings allow a better understanding of the compensation approaches, in particular, the fundamental concept of the compensation procedure that leads to the success of the i-vector paradigm.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design choices to enable further optimisations. We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning. We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.
An Application of AHP for Measuring the Market Effects of the Film Development Fund in Korean Movie Industry

The Film Development Fund policy was initiated to promote the Korean motion picture industry and has been spent a substantial amount of public fund annually since 2007. The purpose of this study to develop a quantitative model for evaluating the effects of the Film Development Fund to the economic performance of Korean motion picture industry with a new perspective of industry value chain and an application of AHP method. Using the quantitative model developed by this study and data gathered from market and expert survey, we evaluated the effects of the Film Development Fund on the market scale expansion in the Korean film industry. In particular, in the process of assessing the effects, we applied AHP method to quantify the relative contribution of value chain phases to the whole economic performance of film industry that can be determined by the subjective judgment of film industry’s experts. As a result, it is estimated that the whole economic effect of the Film Development Fund was considerable: more than 5.4 times the amount subsidized by the Film Development Fund.
Optimal anonymous location privacy protection algorithm based on grid user density

The existing location anonymity algorithms do not consider the distribution of user density in the region. The area of anonymous domain is not the most appropriate and the query workload is redundant. To solve this problem, this paper proposes optimal anonymous location privacy protection algorithm based on grid user density. Taking the user density of the regional grid as the core and using reasonable dynamic shrinkage and expansion rules to find the most suitable anonymous domain, and reduces the anonymous domain as much as possible to meet the user privacy parameter configuration, thereby improving LBS service quality. This paper builds simulation dataset based on road network moving objects, and simulation experiments are performed on location privacy protection method, which proves the effectiveness of this method. At the same time, the real road network floating vehicle data is selected for the application of algorithm, which proves the feasibility of this method.
Best-item Learning in Random Utility Models with Subset Choices

We consider the problem of PAC learning the most valuable item from a pool of $n$ items using sequential, adaptively chosen plays of subsets of $k$ items, when, upon playing a subset, the learner receives relative feedback sampled according to a general Random Utility Model (RUM) with independent noise perturbations to the latent item utilities. We identify a new property of such a RUM, termed the minimum advantage, that helps in characterizing the complexity of separating pairs of items based on their relative win/loss empirical counts, and can be bounded as a function of the noise distribution alone. We give a learning algorithm for general RUMs, based on pairwise relative counts of items and hierarchical elimination, along with a new PAC sample complexity guarantee of $O(\frac{n}{c^2\epsilon^2} \log \frac{k}{\delta})$ rounds to identify an $\epsilon$-optimal item with confidence $1-\delta$, when the worst case pairwise advantage in the RUM has sensitivity at least $c$ to the parameter gaps of items. Fundamental lower bounds on PAC sample complexity show that this is near-optimal in terms of its dependence on $n,k$ and $c$.
The Adopting Knowledge Management Proves Vital for Bahrain’s Governmental Sector

This study sheds light on how the knowledge management (KM) revolution contributed in evolving the governmental performance in Bahrain since few decades. The study was essential to contribute to the limited literature and establish possible contributions that KM has made in the government sector in Bahrain. Hence, a quantitative research followed an adopted survey; piloted through a non-structured in-depth interview, for testing the credibility of this study’s adopted framework. The collected data was analyzed and revealed that KM in Bahrain was at 50%, though such initiatives significantly affect innovation. In terms of responsiveness, this study found out that those who recorded less information retrieval time had also recorded a high maturity level, and that KM was found to be significant in supporting Bahrain vision 2030. In this vein, the study revealed a strong correlation between competitiveness and KM, since KM enhances management of human capital, vital for Bahrain’s economy. Precisely, the study confirmed that a knowledge creation and management lead to economic transformation from the traditional to a knowledge-based sector. Study recommendations aim for the government to plan for the upcoming organizations amidst stiff competition.
Mitigating the Forward Guidance Puzzle: Inattention, Credibility, Finite Planning Horizons and Learning

This paper develops a simple, consistent methodology for generating empirically realistic forward guidance simulations using existing macroeconomic models by modifying expectations about policy announcements. The main advantage of our method lies in the exact preservation of all other shock transmissions. We describe four scenarios regarding how agents incorporate information about future interest rate announcements: “inattention”, “credibility”, “finite planning horizon”, and “learning”. The methodology consists of describing a single loading matrix that augments the equilibrium decision rules and can be applied to any standard DSGE, including large-scale policy-institution models. Finally, we provide conditions under which the forward guidance puzzle is resolved.
Dots & Polygons (Media Exposition)

We present a new game, Dots & Polygons, played on a planar point set. We prove that its NP-hard and discuss strategies for the case when the point set is in convex position.
VECA: A Method for Detecting Overfitting in Neural Networks (Student Abstract)

Despite their widespread applications, deep neural networks often tend to overfit the training data. Here, we propose a measure called VECA (Variance of Eigenvalues of Covariance matrix of Activation matrix) and demonstrate that VECA is a good predictor of networks' generalization performance during the training process. Experiments performed on fully-connected networks and convolutional neural networks trained on benchmark image datasets show a strong correlation between test loss and VECA, which suggest that we can calculate the VECA to estimate generalization performance without sacrificing training data to be used as a validation set.
An Approach for Building Effective Real Estate Chatbots in Vietnamese

This paper presents a method for building a real estate chatbot automatically to support customers in Vietnamese. The chatbot is trained with data set collected on Facebook groups and from the famous real estate website in Vietnam. Using Logistic Regression, user’s intent recognition task achieves precision = 0.93, recall = 0.87 and F1-score = 0.89, while the automatic entity labeling achieves 83% accuracy thanks to the development of a real estate knowledge base. Besides, we report our experience on the design of dialog management modules.
Distributed, partially collapsed MCMC for Bayesian Nonparametrics

Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the fact that completely random measures, which commonly used models like the Dirichlet process and the beta-Bernoulli process can be expressed as, are decomposable into independent sub-measures. We use this decomposition to partition the latent measure into a finite measure containing only instantiated components, and an infinite measure containing all other components. We then select different inference algorithms for the two components: uncollapsed samplers mix well on the finite measure, while collapsed samplers mix well on the infinite, sparsely occupied tail. The resulting hybrid algorithm can be applied to a wide class of models, and can be easily distributed to allow scalable inference without sacrificing asymptotic convergence guarantees.
The precious treasure of Mariano Valenza: the history of Ludovico Sicardi and the birth of geochemical volcano monitoring

I was lucky enough to meet Mariano Valenza in September 1995. I was hitchhiking on the highway that leads from Cefalu to Palermo to go back home. I had spent my summer holidays in the beautiful and wild Madonie mountains. An off-road vehicle (a Land Rover Defender) stopped and a refined gentleman with a curious and charismatic gaze offered me a ride. During our journey, we chatted pleasantly and he told he was originally from that area. When I told him, I was a Geology student, he smiled at me and said Then we will meet again soon, I am going to be your Teacher of Geochemistry!. After a few weeks the lessons began and I met again Professor Valenza in Via Archirafi 36, at the University of Palermo. I will never forget the first introductive lesson of his course: ... we are going to study how the chemical elements have formed in the stars, and how these elements have spread out on our planet; we are going to study the chemical- physical laws regulating their geochemical cycles and how they move in between the atmosphere, the hydrosphere and the lithosphere. We will also learn how the isotopes of these elements allow us to date the geological phenomena and the age of our own planet Earth; ...let's imagine that we are ourselves made of billions and billions and billions of atoms, and it is statically possible that one of Napoleon atom could be here, in this class room!. I was truly fascinated and I discovered my passion for this interesting subject.In via Archirafi 36, in the historical building of the University of Palermo, once home of the Istituto di Mineralogia, I have graduated and got a Ph.D. in Geochemistry, and still nowadays I am working there. In these last 25 years I have learnt to know the stories of different personalities and their scientific researches, which have been hidden and looked after in the ancient building of the University for almost one century. With this article, we would like to remember Professor Mariano Valenza, by telling some stories about him and some others told by himself. Amongst these extraordinary stories we have focused on the one of a little-known scientist, Ludovico Sicardi (1895 - 1987), a modest man who followed his passion for volcanoes. In his field, he was a true innovator and the present research in the field of the geochemical surveillance of volcanos is deeply in debt to him. The Scuola di Geochimica dei Fluidi, born in the '70s at the University of Palermo, has the most debt of gratitude to him, but also the one which has treasured best his memory. This special paper is dedicated to Professor Valenza, who was one of the founders of this school and, before that, the teacher of most of this piece's authors. He had preserved, beside the historical memory, also many documents, photos, and the scientific equipment used by Sicardi for his studies.Sergio Calabrese, Palermo, March 2020
Montgomery-friendly primes and applications to cryptography.

This paper deals with Montgomery-friendly primes designed for the modular reduction algorithm of Montgomery. These numbers are scattered in the literature and their properties are partially exploited. We exhibit a large family of Montgomery-friendly primes which give rise to efficient modular reduction algorithms. We develop two main outcomes. The first one is dedicated directly to cryptography, in particular for isogeny-based approaches and more generally to elliptic curves cryptography (ECC). We suggest more appropriate finite fields and curves in terms of complexity for the recommended security levels, for both isogeny-based cryptography and ECC. The second issue is mainly arithmetic (even if its main use is cryptography), and we propose families of alternative RNS bases. We show that, for dedicated architectures with word operators, we can reach, for a same or better complexity, larger RNS bases with Montgomery-friendly pairwise co-primes than the RNS bases generally used in the literature with pseudo-Mersenne numbers.
Dictionary optimization and clustering for exemplar-based voice conversion

Exemplar-based voice conversion (VC) methods have several disadvantages: too many exemplars, phoneme mismatches, and low conversion efficiency. To solve these problems, this paper proposes a voice conversion method based on nonnegative matrix factorization (NMF) using Dictionary optimization and clustering, which applies low-resolution features instead of high-resolution features to construct dictionaries. Dictionary optimization based on minimizing cepstrum distortion selects some fitter exemplars from the original dictionary. Exemplar clustering divides the dictionary into multiple sub-dictionaries which have better representation based on feature parameters. The ARCTIC database is used for experiments. Results show that the proposed method can significantly improve the quality of converted speech while reducing the number of exemplars and improving efficiency.
Can the Operator of a Drone Be Located by Following the Drone’s Path?

Small commercial Unmanned Aerial Systems (UASs), called drones in common language, pose significant security risks due to their agility, high availability and low price. There is, therefor, a growing need to develop methods for detection, localization and mitigation of malicious and other harmful operation of these drones. This paper presents our work towards autonomously localizing drone operators based only on following their path in the sky. We use a realistic simulation environment and collect the path of the drone when flown from different points of view. A deep neural network was trained to be able to predict the location of drone operators, given the path of the drones. The model is able to achieve prediction of the location of the location of the operator with 73% accuracy.
Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses.

We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses. SMDA utilizes recent transformer-based models to encode each sentence and employs back translation techniques to paraphrase given sentences as augmented data. For labeled sentences, we performed data augmentations to uniform the label distributions and computed supervised loss during training process. For unlabeled sentences, we explored self-training by regarding low-entropy predictions over unlabeled sentences as pseudo labels, assuming high-confidence predictions as labeled data for training. We further introduced consistency regularization as unsupervised loss after data augmentations on unlabeled data, based on the assumption that the model should predict similar class distributions with original unlabeled sentences as input and augmented sentences as input. Via a set of experiments, we demonstrated that our system outperformed baseline models in terms of F1-score and accuracy.
Fast and Bayes-consistent nearest neighbors.

Research on nearest-neighbor methods tends to focus somewhat dichotomously either on the statistical or the computational aspects -- either on, say, Bayes consistency and rates of convergence or on techniques for speeding up the proximity search. This paper aims at bridging these realms: to reap the advantages of fast evaluation time while maintaining Bayes consistency, and further without sacrificing too much in the risk decay rate. We combine the locality-sensitive hashing (LSH) technique with a novel missing-mass argument to obtain a fast and Bayes-consistent classifier. Our algorithm's prediction runtime compares favorably against state of the art approximate NN methods, while maintaining Bayes-consistency and attaining rates comparable to minimax. On samples of size $n$ in $\R^d$, our pre-processing phase has runtime $O(d n \log n)$, while the evaluation phase has runtime $O(d\log n)$ per query point.