Reviewerly/exHarmony huggingface dataset:
A safety navigation method for integrating global path planning and local obstacle avoidance for self-driving cars in a dynamic environment

In this paper，a novel method for obtaining high-quality paths for self-driving cars in underground parking lots is proposed. Self-driving cars require fast and accurate planning of collisionless path. When the self-driving car arrives at the parking lot, the car downloads the layout from the intelligent system of the parking lot and is assigned a parking space, then the location of the designated parking space and the car are provided by the intelligent system. A global path is planned by the global algorithm according to the location of the parking space and the car as well as the layout. If dynamic or unknown obstacles are detected in the process of moving along the global path, the parameters of obstacles can be estimated by the obstacle-detection algorithm. According to obtained parameters, the local obstacle avoidance path can be planned by the behavioral dynamics method. After completing obstacle avoidance, then the car will return to the global path and continue to move toward the target parking space. Finally, the proposed method is simulated by MATLAB, and the results show that the car can safely park in the target parking space. This method simultaneously satisfies the smooth and the real-time requirements of path planning.
Movie Recommendation System through Movie Poster using Deep Learning Technique

Movie recommendation system plays an important role for all media service providers like Netflix, Amazon Prime etc. to increase their business by providing user with appropriate list of movies from very broader lists. By helping user to choose movies of their interest, media service providers thus attract huge traffic which indeed helps them to increase revenue. Very often target audience opt to watch movie by just looking at the movie poster. Thus, users can immediately gain an idea about a particular movie from its movie posters. So, from movie posters one can easily guess the genres of the movie. In this paper we have proposed movie recommendation system which recommends the movies based on its genres which are predicted through its movie posters. Firstly, the movie posters are obtained from IMDB website by performing web scraping. These movie posters are then used to train the Convolution Neural Networks. Convolution Neural Networks then predicts the genres of the movie and then based on predicted genres movies are recommended to the end user based on their cosine similarity. The proposed model is than compared with the existing baseline model and found that our proposed model performs slightly better in terms of accuracy, f1-score and precision.
Framework for Educational Domain-Based Multichatbot Communication System

Education is an area where innovation moves slowly. In this study, we will propose a framework with a novel approach that will support the development of a multi-interactive chatbot’s system for an educational area using AIML 2.0. The system will facilitate the students for their learning towards an outcome-based education domain. The proposed framework will be composed of a user module which consists of user and user interface, chat agents module which will respond to the user query, chatbot KB which will act as the brain for the chatbot system, and socket system for establishing the communication link. Finally, the proposed system will be evaluated using a confusion matrix. The multichatbot communication system will support text-based dialogues on a limited set of questions related to education. However, the system will be implemented in java. The outcomes of this research will be useful for the education sector where these intelligent systems will help the students in schools, universities, and other training scenarios.
A Study of Blockchain Technology, Bitcoin and other Cryptocurrencies as Means of Money Laundering, Frauds and Scams

Blockchain Technology, a whole new innovation in the financial technology industry used to transfer monetary value around the world. This growing Blockchain technology is just 10 years old, without regulations and monitoring, the sector is still known as the ‘Wild West’ of the financial industry. With the rise in market capitalization of Cryptocurrencies, there has been increase in money laundering, thefts, hacks, frauds and scams. This innovation of transferring value has proven to be safe haven for criminals and fraudsters to in washing their illicit proceeds. This research is conducted to look into the fact that millions of dollars are lost to cryptocurrency each day just because of the naive behavior of Investors in investing in cryptocurrency and believing in different scams. In the Covid-19 Pandemic, authorities all over the world have been reserved for the mitigation of health and economic crisis, it has been proposed that this unstable economic and health condition throughout the globe can cause people to suffer with more crypto crimes as people seek basic health necessities through web, avoiding personal contact. This article debates on the Bitcoin concept, Anonymity for transfer of money, challenges to Anti-Money Laundering Authorities and Recommendations of the FATF in response to the risk arise due to large scale adoption of this new innovation.
The Influence of Digital Native Media Utilization and Network Homogeneity on Creative Expressive Ability in an Open Innovation Paradigm

This study conducted a digital content development class that conform to the interests of the education students. We analyzed their creative expressive ability before and after participating in the said class. Furthermore, this study evaluated how media utilization and network homogeneity among the characteristics of digital natives affect their creative expressive ability. The subjects of the study are 320 students from four-year E universities in Gyeonggi-do. The results revealed that the digital content development class proposed in this study can be fully utilized as a class model that reflects the needs of the current generation and invigorates the curriculum for digital nativization.
Accumulations of Projections—A Unified Framework for Random Sketches in Kernel Ridge Regression

Building a sketch of an n-by-n empirical kernel matrix is a common approach to accelerate the computation of many kernel methods. In this paper, we propose a unified framework of constructing sketching methods in kernel ridge regression (KRR), which views the sketching matrix S as an accumulation of m rescaled sub-sampling matrices with independent columns. Our framework incorporates two commonly used sketching methods, sub-sampling sketches (known as the Nystrom method) and sub-Gaussian sketches, as special cases with m=1 and m=infinity respectively. Under the new framework, we provide a unified error analysis of sketching approximation and show that our accumulation scheme improves the low accuracy of sub-sampling sketches when certain incoherence characteristic is high, and accelerates the more accurate but computationally heavier sub-Gaussian sketches. By optimally choosing the number m of accumulations, we show that a best trade-off between computational efficiency and statistical accuracy can be achieved. In practice, the sketching method can be as efficiently implemented as the sub-sampling sketches, as only minor extra matrix additions are needed. Our empirical evaluations also demonstrate that the proposed method may attain the accuracy close to sub-Gaussian sketches, while is as efficient as sub-sampling-based sketches.
Using the messenger software opportunities in distance education

Nowadays, the use of distance learning in the educational process of a university is becoming relevant. The current conditions stimulate and intensify the use of innovative training tools, in particular, network software products. This paper analyzed the possibilities and costs of using various digital products in an educational environment. In particular, they consider the use of mobile digital solutions for distance learning, as well as the requirements for the development of such tools by teachers who do not have the skills to create software at a professional level. The Telegram messenger example describes the experience of using instant messengers in the learning process, namely, a chat bot was created to organize the control of knowledge of students in a higher educational institution. The original chat bot, which was developed using the Telegram API interface and the Chattera chat bot designer, tests the knowledge and skills of students undergoing “General and professional pedagogy” educational discipline. This study showed that the potential of the messenger is not only in communication between users in the form of sending audio, video, and text messages, but also in its educational capabilities.
Skin Cancer Classification Using Random Forest Algorithm

Skin cancer is an excessive lump of skin tissue that affects the skin, has an irregular structure with cell differentiation at various levels in chromatin, nucleus and cytoplasm, is expansive, infiltrative to damage the surrounding tissue, and metastasizes through blood vessels and lymph vessels. Diagnosis of skin cancer by biopsy process is considered less effective because it costs a lot and can injure human skin as a sample. For that, we need a system for classification of skin cancer types that are effective and accurate. The application of machine learning has been widely used in the health sector. One of the machine learning methods is Random Forest. In this study, the histogram color feature extraction will be carried out, the hue moment shape extraction, and the haralick texture extraction. Furthermore, the image will be classified using the Random Forest algorithm. The best accuracy value obtained from the histogram feature extraction process and classification with Random Forest is 0.850822. The novelty of this research is the use of more diverse feature extraction and better accuracy results than previous studies. Future research is expected to use deep learning algorithms with CNN (Convolutional Neural Network) architecture to get better accuracy results and add application designs for the application of models that have been formed in the study so that they can be directly applied by the medical team.
Learning French Liaison with Gradient Symbolic Representations: Errors, Predictions, Consequences

Smolensky &amp; Goldrick (2016) first made the case for Gradient Symbolic Representations (GSRs) as the inputs to phonological grammar using the phenomena of French liaison. Under this view, many common French words are stored underlyingly with partially-activated word-final consonants, and others with gradient blends of partially-activated word-initial consonants. In this paper, we follow up some of that view's predictions and consequences, focusing on the acquisition of French liaison using GSRs. We compare our simulations of error-driven GSR learning with observed errors made by French-learning children, and find the results to be encouragingly similar. We also compare predictions about the end state of GSR learning with a pilot study reporting adult French speakers' use of liaison in nonce words, where we find a rather less good explanatory fit. The paper emphasizes the role of word and collocation frequency in the development of phonological patterns by a GSR learner, and outlines many future avenues for research.
Graph-based Feature Selection Method for Learning to Rank

In this paper, a graph-based feature selection method for learning to rank, called FS-SCPR, is proposed. FS-SCPR models feature relationships as a graph and selects a subset of features that have minimum redundancy with each other and have maximum relevance to the ranking problem. For minimizing redundancy, FS-SCPR abandons redundant features which are those being grouped into the same cluster. For maximizing relevance, FS-SCPR greedily collects from each cluster a representative feature which is with high relevance to the ranking problem. This paper utilizes FS-SCPR as a preprocessor for determining discriminative and useful features and employs Ranking SVM to derive a ranking model for document retrieval with the selected features. The proposed approach is evaluated using the LETOR datasets and found to perform competitively when being compared to another feature selection method, GAS-E.
Pedagogical Innovations in Elementary Mathematics Instructions: Future Learning and Research Directions

Literature reviews about pedagogical innovations are of importance to determine further interventions for learning refinement and future research directions. The present study describes existing pedagogical innovations, identifies learning problems underlie the innovations, and outlines development models used for developing elementary mathematics educational innovations in Indonesia. A systematic literature review was administered to review scholarly articles published between 2014 and 2019. This time frame was drawn in relation to a new curriculum called Kurikulum 2013 introduced in early 2014. The results of this study reveal that the majority of pedagogical innovations focus on developing learning materials and techniques while fewer innovations develop learning environments. Problems underlie existing innovations mainly due to the elementary students who reluctant to learn mathematics, the less competent teachers, and the old-fashioned learning resources. Research and development in elementary mathematics were commonly conducted using Borg Gall, ADDIE, and 4D models. The evidence from this study suggests that forthcoming mathematics teaching and learning process should be conducted in more enjoyable manners such as practicing microgame-based learning in either physical or digital learning spaces. One of the most sensible research agendas is to develop joyful learning environments to address the interrelated complex problems among students, teachers, and learning resources in elementary mathematics learning in Indonesia.
SOA Security Strategy Based on Cloud Immune Protection

At present, the research and application of cloud security is still in its infancy and the vulnerability of SOA security has become a major problem that plagues its development. To increase its security from within the system architecture, we try to gradually apply mature cloud security technologies to enhance the security of SOA in this paper. Based on the operation mechanism of human immune system and its functional characteristics, we propose a cloud security immune system using the principles of human immune. Then, the paper analyzes the problems of immune steady state and web service load balancing on SOA framework, proposes the extended SOA framework based on immune system, and puts forward the dynamic load balancing model of web service. The simulation results show that the SOA security detection model based on cloud immune protection can keep a high detection rate and greatly reduce the false alarm rate, and achieve a better experimental effect.
Klasifikasi Gerakan Jari Tangan Berdasarkan Sinyal Electromyogram Pada Lengan

An electromyogram is a recording of muscle activity. These signals have been used both for medical diagnosis and engineering such as finger motion detection in healthy people and rehabilitation patients. Many studies have been conducted to map the relationship between electromyogram and finger movements, one of which is the relationship between the number of channels used and the complexity of the system. The number of channels used is directly proportional to the complexity of a system. The more complex the system, the heavier the data processing is so that it requires greater resources. Therefore, this study focuses on the construction of a classification system for human finger movements using fewer channels. The number of channels used in this study is 4. Root Mean Square is applied in a sliding window as feature extraction. The classifier used is the artificial neural network. System validation is done with 10-fold cross-validation. The test results of the average accuracy value for the thumb, index finger, middle finger, ring finger, little finger, grip, and relaxation were 89%, 90%, 93%, 95%, 93%, 94%, and 91% respectively which can be said to be quite good considering the number of channels relatively few compared to previous studies.
BiLSTM-CRF with Compensation Method for Spatial Entity Recognition

As a basic task, named entity recognition (NER) plays a very important role in the field of natural language processing. In recent years, the neural network method has achieved excellent results in NER. However, the NER method is not very effective in fine-grained entity recognition tasks in the subdivision field. In order to solve this problem, we proposed the BiLSTM-CRF model with compensation method (BiLSTM-CC) by increasing the vector representing the semantic information of the word and compensating the model output. In the task of spatial entity recognition, the improved algorithm shows excellent performance.
Vocabulary Learning through Duolingo Mobile Application: Teacher Acceptance, Preferred Application Features and Problems

Vocabulary learning is vital towards acquiring new languages and therefore insufficient vocabulary knowledge can be considered a significant barrier to such learning. Availability of tools to enhance or simplify the learning process is an added advantage to encourage learners to learn new languages. This research was conducted to investigate teacher acceptance of mobile applications in Saudi Arabian universities for vocabulary learning, identify suitable features for vocabulary learning applications and to identify problems that may arise through use of mobile applications in classroom settings with the aim of solving them. Duolingo mobile application was employed as a study tool to investigate effectiveness of the applications and their technological features. Twenty English language teachers participated in the study by answering questionnaires and three of them were also interviewed and resulting data analyzed quantitatively and qualitatively. The main findings from the research are that teachers showed positive attitudes towards teaching and learning using vocabulary learning applications and that several challenges faced by users inform on unsuitability of the applications and reluctance to use by teachers. Therefore, appropriate measures should be taken to address the problems associated with use of mobile applications in vocabulary learning and teaching so as to explore the vast potentials they have towards learning.
Algorithm visualizers design technology

The topic of the article relates to the introduction of such forms of education as visualizers of algorithms into the distance education system of Novosibirsk State Technical University.
ADCI-Net: an adaptive discriminative clip identification strategy for fast video action recognition

The most common method for video-level classification is to operate models over a whole series of fixed-temporal-length clips in a long video. However, a video usually consists of several meaningless sections, and similar clips that are fed into models could lead to a suboptimal recognition accuracy and waste of computing resources. To address this issue, we introduced an adaptive discriminative clip identification network to evaluate every video clip with respect to its relevance. We adaptively choose top-ranked clips as inputs for prediction and filter out irrelevant clips. Specifically, for a given trained cumbersome convolutional neural network action recognition model, we use a lightweight hallucination network (H-Net) to study its generalization ability based on distillation. Then, the evaluation of relevant clips is performed by considering the imitated features of H-Net. Thus, heavy calculations of overlapping content or meaningless clips can be avoided during inferences. We validate our approach by examining its performance on two datasets: UCF101 and HMDB51. Our strategy can be applied to any clip-based action recognition model. Experimental results demonstrate that on UCF101, we reduced the computational cost by 70% and increased the accuracy.
A Cluster Analysis with Complete Linkage and Ward's Method for Health Service Data in Makassar City

Health care facilities are a place used to organize health efforts. Health service data in Makassar City has not shown which sub-districts have excellent service criteria, good enough, and not good. Therefore, it is necessary to group sub-districts with cluster analysis using hierarchy method. The hierarchy method used in this study is only 2, namely complete linkage and ward's method. Complete linkage method is the opposite of the approach to the minimum distance principle that is the furthest distance between objects while Ward's Method is a method that aims to minimize variance between objects in one cluster. There are four health services used, namely Hospitals, Health Centers, Home Care and Telemedicine with 15 sub-districts. This study also used a validity test namely Index Davies Bouldin (IDB) to determine the criteria of health services. The results of the analysis on complete linkage formed 3 clusters, namely cluster 1 with good health services, cluster 2 with excellent health services, and cluster 3 with poor health services. In addition, ward's Method also formed 3 clusters, namely cluster 1 with good health services, clusters 2 with poor service, and cluster 3 with excellent health services.
PMBA: A Parallel MCMC Bayesian Computing Accelerator

Bayesian computing, including sampling probability distributions, learning graphic model, and Bayesian reasoning, is a powerful class of machine learning algorithms with such wide applications as biologic computing, financial analysis, natural language processing, autonomous driving, and robotics. The central pattern of Bayesian computing is the Markov Chain Monte Carlo (MCMC) computing, which is compute-intensive and lacks explicit parallelism. In this work, we propose a parallel MCMC Bayesian computing accelerator (PMBA) architecture. Designed as a probabilistic computing platform with native support for efficient single-chain parallel Metropolis-Hastings based MCMC sampling, PMBA boosts the performance of probabilistic programs with a massive-parallelism microarchitecture. PMBA is equipped with on-chip random number generators as the built-in source of randomness. The sampling units of PMBA are designed for parallel random sampling through a customized SIMD pipeline supporting data synchronization every iteration. A respective computing framework supporting automatic parallelization and mapping of probabilistic programs is also developed. Evaluation results demonstrate that PMBA enables a 17-21 folds speedup over a TITAN X GPU on MCMC sampling workload. On probabilistic benchmarks, PMBA outperforms prior best solutions by factor of 3.6 to 10.3. An exemplar based visual category learning algorithm is implemented on PMBA to demonstrate its efficiency and effectiveness for complex statistical learning problems.
BAHAYA HOAKS DAN URGENSI LITERASI MEDIA: STUDI PADA MAFINDO SOLO RAYA

This research was based on the massive political hoax circulating on social media. The spread of hoaxes was increasing day by day with the 2019 simultaneous election. Hoax became an important problem in society because the level of community literacy was still low. The Indonesian Anti Hoax Society (MAFINDO) Solo Raya invited the society to fight hoaxes in the 2019 political year. This study aims to describe how the media literacy strategies carried out by MAFINDO Solo Raya in overcoming hoaxes in the 2019 political year. The theory used in this research was Potterâ€™s literacy media. This research used a descriptive qualitative approach. The data collection technique used in this study were interviews, observation and documentation. The results of this research indicated that MAFINDO Solo Raya's media literacy strategies in overcoming hoaxes in the 2019 political year used socialization, education and fact checking training with the Hoax Buster Tools application.