Reviewerly/exHarmony huggingface dataset:
Vehicle Classification with Audio and Video Modalities Using CNN and Decision-Level Fusion

The paradigm of vehicle classification is ever evolving, with new requirements and challenges adding up at every juncture. It remains an important task in the efficient management of traffic and road infrastructure. Much work has been done over the years to perfect this task. Complementarity of information has been used before to improve classifier performance using various classifier fusion techniques. This has however been not explored in the case of a completely neural network based multi-modal classification system. In this work, a multi-modal vehicle classification system is proposed which utilizes the useful property of complementarity to achieve improved performance. Classification of vehicles is performed with the two modalities separately, using Convolutional Neural Network (CNN) classifiers. In the case of audio modality, sets of Mel Frequency Cepstral Coefficients (MFCC) are the feature vectors. The individual predictions from the base classifiers are fused at the decision-level to arrive at a final prediction. The results of both single and multi-modal classification are compared. The results show that decision-level fusion improves the accuracy of classification.
Multicollinearity Correction and Combined Feature Effect in Shapley Values

Model interpretability is one of the most intriguing problems in most machine learning models, particularly for those that are mathematically sophisticated. Computing Shapley Values are one of the best approaches so far to find the importance of each feature in a model, at the instance (data point) level. In other words, Shapley values represent the importance of a feature for a particular instance or observation, especially for classification or regression problems. One of the well known limitations of Shapley values is that the estimation of Shapley values with the presence of multicollinearity among the features are not accurate as well as reliable. To address this problem, we present a unified framework to calculate accurate Shapley values with correlated features. To be more specific, we do an adjustment (matrix formulation) of the features while calculating independent Shapley values for the instances to make the features independent with each other. Our implementation of this method proves that our method is computationally efficient also, compared to the existing Shapley method.
Cloud Position Forecasting Based on ConvLSTM Network

A technique for cloud position prediction for intra-hour Irradiance prediction is presented. This cloud motion prediction technique is mainly used as an ultra-short-term photovoltaic power prediction in the field of electrical engineering. However, due to the impact of complex cloud motion processes, it is difficult to obtain accurate results using traditional algorithms. Many scholars have applied deep learning to predict and process images and achieved good results. This paper proposes a machine learning algorithm-ConvLSTM to predict cloud position. The cloud position and corresponding irradiance fluctuations were predicted ten minutes ahead.
A Comparison of Variational Bounds for the Information Bottleneck Functional

In this short note, we relate the variational bounds proposed in Alemi et al. (2017) and Fischer (2020) for the information bottleneck (IB) and the conditional entropy bottleneck (CEB) functional, respectively. Although the two functionals were shown to be equivalent, it was empirically observed that optimizing bounds on the CEB functional achieves better generalization performance and adversarial robustness than optimizing those on the IB functional. This work tries to shed light on this issue by showing that, in the most general setting, no ordering can be established between these variational bounds, while such an ordering can be enforced by restricting the feasible sets over which the optimizations take place. The absence of such an ordering in the general setup suggests that the variational bound on the CEB functional is either more amenable to optimization or a relevant cost function for optimization in its own regard, i.e., without justification from the IB or CEB functionals.
Content-Based Learning Object Recommendation System Using a User Profile Ontology for High School Students

The lack of quality of education in Peruvian schools has caused young people to look for other ways to obtain information, of which the web stands out. However, this tool is made up of billions of web pages, which affects the time each student takes to search. To address this situation, we propose the development of a content-based recommendation system that uses ontologies for data storage. Our recommender system allows the user profile data to be integrated into the model to consider its characteristics as part of the recommendation. We carried out two sets of validations for the evaluation of our proposal, one with expert judgment and the other by gathering the opinion of the end-users. As a result of the first evaluation, we found that 76.25% of the items were highly related to the search. For the second evaluation, we found that our system obtained a usability of 78.67%, considering the opinion of the students tested.
On the Problem of $$p_1^{-1}$$ in Locality-Sensitive Hashing

A Locality-Sensitive Hash (LSH) function is called (r, cr, \(p_1,p_2)\)-sensitive, if two data-points with a distance less than r collide with probability at least \(p_1\) while data points with a distance greater than cr collide with probability at most \(p_2\). These functions form the basis of the successful Indyk-Motwani algorithm (STOC 1998) for nearest neighbour problems. In particular one may build a c-approximate nearest neighbour data structure with query time \(\tilde{O}(n^\rho /p_1)\) where \(\rho =\frac{\log 1/p_1}{\log 1/p_2}\in (0,1)\). This is sub-linear as long as \(p_1\) is not too small. Such an algorithm is significant, since most high dimensional nearest neighbour problems suffer from the curse of dimensionality, and can't be solved exact, faster than a brute force linear-time scan of the database.
Various Approaches in Sentiment Analysis

In natural language processing (NLP), Sentiment analysis is flourishing rapidly as it aims at finding the emotional tone behind a piece of text and helps in understanding the attitudes, opinions, and emotions expressed within an online mention. The internet is flooded with large amounts of data such as texts, photos, audio, and video. With the help of sentiment analysis, we can find useful information from this data. This paper presents various approaches in sentiment analysis used by researchers and the major challenges faced in this field. By analyzing different papers, we came to know that the most widely used techniques in machine learning are Naïve Bayes and SVM and in NLP, the lexicon-based approach is used. With the advancement in technology, sentiment analysis will be available for the public and smaller companies as well.
Janus Screen

In the area of ubiquitous computing, there is research that use surfaces of elements in the environment such as windows and walls as screens. It is necessary to install an opaque screen and projectors on both sides of the screen in order to switch projection surfaces, i.e., front, back, or both sides. In this paper, we propose Janus Screen, a novel screen system with polarizers that allow switching of the projection surface to the front, back, or both sides using only projectors on one side. Janus Screen employs projectors equipped with polarizers and a screen with multiple layers. We implement a prototype and confirm that Janus Screen can selectively switch the projection surface. Moreover, we discuss unique characteristics and possible applications of the proposed system.
Domain-Specific Alignment Network for Multi-Domain Image-Based 3D Object Retrieval

2D image-based 3D object retrieval is a very important task in computer vision and big data management. Conventional image-based 3D object retrieval usually assumes that the images are from one single domain. However, for real applications, 2D images may be from multiple domains (e.g., real image, sketch, and quick draw). It raises significant challenges for this task since these 2D images have a great domain gap with each other as well as a great modality gap with 3D objects. To address these issues, we propose an unsupervised Domain-Specific Alignment Network (DSAN) for multi-domain image-based 3D object retrieval. The proposed method aims to reduce domain discrepancy by domain-specific alignment network with multi-level moment matching, including first-order moment and second-order moment. Based on the observation that for any given sample, different domain classifiers should output the same label, we design a domain-specific classifier alignment module. To our knowledge, the proposed method is the first unsupervised work to align multiple-domain 2D images with 3D objects in an end-to-end manner. The multi-domain dataset MDI3D is utilized to advocate the research on this task, and the extensive experimental results demonstrate the superiority of the proposed method.
A Natural Language Processing Approach to Predicting the Persuasiveness of Marketing Communications

The use of language is ubiquitous in any marketing communication, such as advertisements or product descriptions, and the decision of how to formulate marketing communications so that they are persuasive is a problem for most marketers. In this paper, the authors take a natural language processing approach to investigate the role of syntax in marketing communications. Using a data set of 134 debates with 129,480 sentences, they show that the syntax of a message communicated, as captured by specific elements of syntax referred to as dependencies, is predictive of its persuasiveness. They identify the specific dependencies that directly influence the persuasiveness of a message and demonstrate how communicators can use the findings to write persuasive marketing communications. In two follow up experiments, conducted in the lab and on Facebook, the authors show that marketing messages written in accordance with their findings are more successful in persuading consumers. Click-through rates on Facebook, for example, increase by 41%.
Fall Detection Using a Smartphone

Fall is one of the most common causes of injury. Falling is especially dangerous for the elderly people. Fortunately in recent years, there has been a growing popularity of smartphone devices, also among older people. Each such device is equipped with different sensors, including an accelerometer, gyroscope and magnetometer. The purpose of the article is to verify whether it is possible to effectively detect a fall from other daily physical activities. For the purposes of the research, linear and angular acceleration signals and magnetic field strength signals were recorded using a smartphone for various physical activities. An algorithm for extracting features based on energy calculated for wavelet details and approximation as well as entropy and Sevcick fractal dimension was proposed. The classification results obtained are good. The best results of classification accuracy were obtained for the gyroscope signal (average 96%). The precision was 0.88, the specificity −0.96 and the sensitivity −0.92. The proposed fall detection method was also verified on the UniMiB SHAR database, containing 7 types of activities registered for 30 people aged 18–60 (a total of 11771 examples of user activity).
Performance of ELM Using Max-Min Document Frequency-Based Feature Selection in Multilabeled Text Classification

In text classification feature selection is used to reduce the feature space to improve the classification accuracy. In this paper, we propose a method max-min document frequency-based feature selection and we applied Extreme Learning Machine (ELM) model to improvise the text classification performance. For this text classification, we used the multilabel Reuters dataset which consists of 10788 number of documents. In this experiment, the ELM model performs better using max-min document frequency-based feature selection in terms of precision, recall, and F-measure as is compared to the ELM model using full feature space without using any feature selection technique.
Ursa

With the increasing number of users in blockchain-based cryptocurrencies, the public has raised the demand for transaction throughput, and many protocols are designed to improve the throughput following the Nakamoto consensus. Although astonishing progress has been made in the on-chain throughput improvement, high throughput makes the blockchains suffer from the increasing blockchain size, hard forks, and possible attacks. In this work, we propose a quantitative model to describe and analyze the Nakamoto consensus. We then design a robust scheme named Ursa to reduce storage requirements and to reduce the forks by automatically adjusting block size according to users’ needs.
Modeling Value-Based Decision-Making Policies Using Genetic Programming

Abstract. An important way to develop models in psychology and cognitive science is to express them as computer programs. However, computational modeling is not an easy task. To address this issue, some have proposed using artificial-intelligence (AI) techniques, such as genetic programming (GP) to semiautomatically generate models. In this paper, we establish whether models used to generate data can be recovered when GP evolves models accounting for such data. As an example, we use an experiment from decision-making which addresses a central question in decision-making research, namely, to understand what strategy, or “policy,” agents adopt in order to make a choice. In decision-making, this often means understanding the policy that best explains the distribution of choices and/or reaction times of two-alternative forced-choice decisions. We generated data from three models using different psychologically plausible policies and then evaluated the ability and extent of GP to correctly identify the true generating model among the class of virtually infinite candidate models. Our results show that, regardless of the complexity of the policy, GP can correctly identify the true generating process. Given these results, we discuss implications for cognitive science research and computational scientific discovery as well as possible future applications.
Answer Set Programming for Regular Inference

We propose an approach to non-deterministic finite automaton (NFA) inductive synthesis that is based on answer set programming (ASP) solvers. To that end, we explain how an NFA and its response to input samples can be encoded as rules in a logic program. We then ask an ASP solver to find an answer set for the program, which we use to extract the automaton of the required size. We conduct a series of experiments on some benchmark sets, using the implementation of our approach. The results show that our method outperforms, in terms of CPU time, a SAT approach and other exact algorithms on all benchmarks.
Establishing a Formal Benchmarking Process for Sentiment Analysis for the Bangla Language

Tracking sentiments is a critical task in many natural language processing applications. A lot of work has been done on many leading languages in the world, such as English. However, in many languages such as Bangla, sentiment analysis is still in early development. Most of the research on this topic suffers from three key issues: (a) the lack of standardized publicly available datasets, (b) the subjectivity of the reported results, which generally manifests as a lack of agreement on core sentiment categorizations, and finally, (c) the lack of an established framework where these efforts can be compared to a formal benchmark. Thus, this seems to be an opportune moment to establish a benchmark for sentiment analysis in Bangla. With that goal in mind, this paper presents benchmark results of ten different sentiment analysis solutions on three publicly available Bangla sentiment analysis corpora. As part of the benchmarking process, we have optimized these algorithms for the task at hand. Finally, we establish and present sixteen different evaluation matrices for benchmarking these algorithms. We hope that this paper will jumpstart an open and transparent benchmarking process, one that we plan to update every two years, to help validating newer and novel algorithms that will be reported in this area in future.
Rivest Cipher 4 Cryptography and Elliptical Curve Cryptography Techniques to Secure Data in Cloud

Data security is the major issue in the field of research and in the field of cloud computing; it is a major concern as data are located across the globe in the cloud. As cloud computing technology is used by government, businesses, and industries, many studies have been investigated recently to achieve authentication and the confidentiality of data while they are stored in the cloud. Cryptography plays an important role in achieving data security. The paper proposes a hybrid model that uses the symmetric RC4 algorithm to encrypt the data and the key performance encryption is secured by the ECC algorithm which is a public key algorithm. The proposed method provides high security by encrypting the session key and also needs less time for encrypting and decrypting the data than the existing model. The proposed hybrid model provides data confidentiality and also secures the session key used for encryption and decryption.
A brief overview on current status of nanomedicines for treatment of pancytopenia: Focusing on chemotherapeutic regime

In pancytopenia, the number of cells reaches to white blood cell count (WBC) < 3500 cells/mm3, hemoglobin (Hb) < 11 g/dL and platelet count <150000 cells/mm3. Nanomedicine is the specific area of medicine that comprises of tools and knowledge of nanotechnology in the prevention, diagnosis, control and treatment of life-threatening disease. It paved a way to regulate the pancytopenic settings and emerged as an alternative treatment that uses specific delivery system, improves efficacy of drugs and reduces detrimental side effects to healthy cells. In this review article, variant search engines and scientific databases of PubMed, Scopus, Google Scholar, Science Direct and Medline for current status of nanomedicines in the treatment of pancytopenia were reviewed. Among the findings, high quality articles were selected from 2012 to 2020, and after reviewing them, authors have conducted a comprehensive compilation of the research work carried out on various nanomedicines for treatment of pancytopenia.
The Effects of Digitalization on Entrepreneurial Innovation in Sub-Saharan Africa

The objective of this study is to show the effects of digitalization on the innovative capability of companies in Sub-Saharan Africa. To meet this objective, the authors used the World Bank data (Enterprise Survey) on survey of service companies and manufacturing industries in 2016. Making use of a binary probit model and a recursive bivariate probit model, they found that digitization has a significant effect on business innovation in Sub-Saharan Africa. Specifically, they found that when a company has a website, its probability of introducing an innovation in to the market increases by 27% as compared to companies without a website. However, when a company has its own website, the probability to introduce an incremental innovation is 0.34 higher than radical innovation. Thus, they can recommend to entrepreneurs in Sub-Saharan Africa to invest not only in the digitalization of their companies but also and above all in research and development, which is a translational and inevitable determinant for radical innovation.
Paralinguistic Privacy Protection at the Edge

Voice user interfaces and digital assistants are rapidly entering our lives and becoming singular touch points spanning our devices. These always-on services capture and transmit our audio data to powerful cloud services for further processing and subsequent actions. Our voices and raw audio signals collected through these devices contain a host of sensitive paralinguistic information that is transmitted to service providers regardless of deliberate or false triggers. As our emotional patterns and sensitive attributes like our identity, gender, and well-being are easily inferred using deep acoustic models, we encounter a new generation of privacy risks by using these services. One approach to mitigate the risk of paralinguistic-based privacy breaches is to exploit a combination of cloud-based processing with privacy-preserving, on-device paralinguistic information learning and filtering before transmitting voice data. In this article we introduce EDGY , a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and contain sensitive attributes at the edge prior to offloading to the cloud. We evaluate EDGY’s on-device performance and explore optimization techniques, including model quantization and knowledge distillation, to enable private, accurate, and efficient representation learning on resource-constrained devices. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in “zero-shot” ABX score or minimal performance penalties of approximately 5.95% word error rate (WER) in learning linguistic representations from raw voice signals, using a CPU and a single-core ARM processor without specialized hardware.
Automated Extraction of Requirement Entities by Leveraging LSTM-CRF and Transfer Learning

Requirement entities, "explicit specification of concepts that define the primary function objects", play an important role in requirement analysis for software development and maintenance. It is a labor-intensive activity to extract requirement entities from textual requirements, which is typically done manually. A few existing studies propose automated methods to support key requirement concept extraction. However, they face two main challenges: lack of domain-specific natural language processing techniques and expensive labeling effort. To address the challenges, this study presents a novel approach named RENE, which employs LSTM-CRF model for requirement entity extraction and introduces the general knowledge to reduce the demands for labeled data. It consists of four phases: 1) Model construction, where RENE builds LSTM-CRF model and an isomorphic LSTM language model for transfer learning; 2) LSTM language model training, where RENE captures general knowledge and adapt to requirement context; 3) LSTM-CRF training, where RENE trains the LSTM-CRF model with the transferred layers; 4) Requirement entity extraction, where RENE applies the trained LSTM-CRF model to a new-coming requirement, and automatically extracts its requirement entities. RENE is evaluated using two methods: evaluation on historical dataset and user study. The evaluation on the historical dataset shows that RENE could achieve 79% precision, 81% recall, and 80% F1. The evaluation results from the user study also suggest that RENE could produce more accurate and comprehensive requirement entities, compared with those produced by engineers.
Pedagogy of online courses: rhizomatic learning

The paper presents the results of the analysis of Russian and foreign scientific and pedagogical literature devoted to various aspects of the rhizomatic approach which is used in the online learning. The author provides a brief description of its philosophical framework, states the peculiarities of the educational process organization in this approach, gives the interpretation of the learning process in the context of the rhizomatic approach, lists relevant characteristics of the main difficulties, as well as its difference from connectivism, another common online learning approach.