Reviewerly/exHarmony huggingface dataset:
Bayesian optimization in variational latent spaces with dynamic compression

Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work, we focus on robotics problems with a budget of only 10-20 trials. This is a very challenging s ...
HUCDO

Outsourcing helps relocate data from the cyber-physical system (CPS) for efficient storage at low cost. Current server-based outsourcing mainly focuses on the benefits of servers. This cannot attract users well, as their security, efficiency, and economy are not guaranteed. To solve with this issue, a hybrid outsourcing model that exploits both cloud server and edge devices to store data is needed. Meanwhile, the requirements of security and efficiency are different under specific scenarios. There is a lack of a comprehensive solution that considers all of the above issues. In this work, we overcome the above issues by proposing the first hybrid user-centric data outsourcing (HUCDO) scheme. It allows users to outsource data securely, efficiently, and economically via different CPSs. Brielly, our contributions consist of theories, implementations, and evaluations. Our theories include the first homomorphic collision-resistant chameleon hash (HCCH) and homomorphic designated-receiver signcryption (HDRS). As implementations, we instantiate how to use our proposals to outsource small- or large-scale data through distinct CPS, respectively. Additionally, a blockchain with proof-of-discrete-logarithm (B-PoDL) is instantiated to help improve our performance. Last, as demonstrated by our evaluations, our proposals are secure, efficient, and economic for users to implement while outsourcing their data via CPSs.
Dual-Task Integrated Network for Fast Pedestrian Detection in Crowded Scenes

Pedestrian detection is a critical problem in computer vision with significant impact on many real-world applications. In this paper, we introduce an fast dual-task pedestrian detector with integrated segmentation context (DTISC) which predicts pedestrian location as well as its pixel-wise segmentation. The proposed network has three branches where two main branches can independently complete their tasks while useful representations from each task are shared between two branches via the integration branch. Each branch is based on fully convolutional network and is proven effective in its own task. We optimize the detection and segmentation branch on separate ground truths. With reasonable connections, the shared features introduce additional supervision and clues into each branch. Consequently, the two branches are infused at feature spaces increasing their robustness and comprehensiveness. Extensive experiments on pedestrian detection and segmentation benchmarks demonstrate that our joint model improves the performance of detection and segmentation against state-of-the-art algorithms.
English Spelling Errors Committed by the Blind

Despite the outgrowing trend on assistive technology assisting blind student to be fully participate in education, the challenges remain in the area of English language learning. The inconsistency of the sound-spelling relationship in English apparently triggers the spelling problems for the Blind student since he tends to encode syllables by as it sounded. This study aims to identify, classify and analyze the spelling errors committed by the blind students in learning English using the classification of NEFR (National Foundation for Educational Research) that comprises the aspects of omissions, substitutions, insertions, transpositions and grapheme substitution. Employing case study design, this research elaborate the spelling error committed by 1 blind EFL learner enrolling an Academic Writing course in English Language Education Program, Faculty of Cultural Studies, Universitas Brawijaya, Indonesia. The findings expose that the blind students tend to frequently do spelling error in the aspect of grapheme substitution followed by omissions, substitutions, and insertion. Some contributive factors that presumably affecting the errors are (1) lack exposure to the formation of the words, (2) inherent of language (silent letter, homophone and borrowing words), (3) different characteristics of L1 and L2, and (4) student’s preferences on reading (use screen reader more than braille text). Thus, the exposure toward phonetics symbol and the sound-letter relationship in English should be more disseminated so the blind student is capable of having a correct spelling.
Analysis of Customer Transaction Data Associations Based on The Apriori Algorithm

UD Dian Pertiwi is one of the small and medium enterprises engaged in materials with the main product is building materials. This business experiences large amounts of transactions every day, the data obtained becomes increasingly large, and it will only be limited to a pile of useless data or commonly called junk. By utilizing a data mining approach apriori algorithm technique, the data can be utilized to support the sales process and achieve a target of UD Dian Pertiwi. Based on research and data mining that has been done using association analysis and apriori algorithms by applying a minimum of support = 1% and a minimum of confidence = 70% resulted in the ten strongest association rules can be used by UD Dian Pertiwi in the process of applying a sales strategy including determining interrelationships, in short, the product has the potential to be purchased at the same time, increasing the amount of product stock and conducting promotions.
Rank-embedded Hashing for Large-scale Image Retrieval

With the growth of images on the Internet, plenty of hashing methods are developed to handle the large-scale image retrieval task. Hashing methods map data from high dimension to compact codes, so that they can effectively cope with complicated image features. However, the quantization process of hashing results in unescapable information loss. As a consequence, it is a challenge to measure the similarity between images with generated binary codes. The latest works usually focus on learning deep features and hashing functions simultaneously to preserve the similarity between images, while the similarity metric is fixed. In this paper, we propose a Rank-embedded Hashing (ReHash) algorithm where the ranking list is automatically optimized together with the feedback of the supervised hashing. Specifically, ReHash jointly trains the metric learning and the hashing codes in an end-to-end model. In this way, the similarity between images are enhanced by the ranking process. Meanwhile, the ranking results are an additional supervision for the hashing function learning as well. Extensive experiments show that our ReHash outperforms the state-of-the-art hashing methods for large-scale image retrieval.
Foundations of regular coinduction

Inference systems are a widespread framework used to define possibly recursive predicates by means of inference rules. They allow both inductive and coinductive interpretations that are fairly well-studied. In this paper, we consider a middle way interpretation, called regular, which combines advantages of both approaches: it allows non-well-founded reasoning while being finite. We show that the natural proof-theoretic definition of the regular interpretation, based on regular trees, coincides with a rational fixed point. Then, we provide an equivalent inductive characterization, which leads to an algorithm which looks for a regular derivation of a judgment. Relying on these results, we define proof techniques for regular reasoning: the regular coinduction principle, to prove completeness, and an inductive technique to prove soundness, based on the inductive characterization of the regular interpretation. Finally, we show the regular approach can be smoothly extended to inference systems with corules, a recently introduced, generalised framework, which allows one to refine the coinductive interpretation, proving that also this flexible regular interpretation admits an equivalent inductive characterisation.
Rényi and von Neumann entropies of thermal state in Generalized Uncertainty Principle-corrected harmonic oscillator

The Rényi and von Neumann entropies of thermal state in generalized uncertainty principle (GUP)-corrected single harmonic oscillator system are explicitly computed within the first order of GUP parameter [Formula: see text]. While the von Neumann entropy with [Formula: see text] exhibits a monotonically increasing behavior in external temperature, the nonzero GUP parameter makes a decreasing behavior at large temperature region. As a result, for the case of [Formula: see text], the von Neumann entropy is maximized at the finite temperature [Formula: see text]. The Rényi entropy [Formula: see text] with nonzero [Formula: see text] also exhibits similar behavior at large temperature region. In this region, the Rényi entropy exhibits a decreasing behavior with increasing temperature. The decreasing rate becomes larger when the order of the Rényi entropy is smaller.
Performance Analysis of Wavelet on Image Compression

In this paper we viewed an approach for image compression using DWT technique.The DWT technique is explained with its algorithm, advantages.This technique has features like simplicity, handling complex kind of information, cost deduction in data storage, comparatively more fast than other compression algorithm, transmission on networks without major reduction in quality of the images.
Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.
Segmented Douglas-Peucker Algorithm Based on the Node Importance

Vector data compression algorithm can meet requirements of different levels and scales by reducing the data amount of vector graphics, so as to reduce the transmission, processing time and storage overhead of data.In view of the fact that large threshold leading to comparatively large error in Douglas-Peucker vector data compression algorithm, which has difficulty in maintaining the uncertainty of shape features and threshold selection, a segmented Douglas-Peucker algorithm based on node importance is proposed.Firstly, the algorithm uses the vertical chord ratio as the main feature to detect and extract the critical points with large contribution to the shape of the curve, so as to ensure its basic shape.Then, combined with the radial distance constraint, it selects the maximum point as the critical point, and introduces the threshold related to the scale to merge and adjust the critical points, so as to realize local feature extraction between two critical points to meet the requirements in accuracy.Finally, through a large number of different vector data sets, the improved algorithm is analyzed and evaluated from qualitative and quantitative aspects.Experimental results indicate that the improved vector data compression algorithm is better than Douglas-Peucker algorithm in shape retention, compression error, results simplification and time efficiency.
Imbalanced Data Classification Using Auxiliary Classifier Generative Adversarial Networks

This paper explores an Auxiliary Classifier Generative Adversarial Networks (AC-GAN) model to address imbalanced data classifications.More specifically, debit card transaction data from a private Indonesian bank were categorized into fraudulent and non-fraudulent transactions.Training and testing datasets were then analyzed.Due to its architectural similarity, this study explores the learning performance of an AC-GAN Discriminator model and compares it with the learning performance of a Convolutional Neural Network (CNN) model, as a baseline model.The experimental results illustrate that the training and testing accuracy of the AC-GAN discriminator model (i.e., 81.1% and 93.0%, respectively) outperformed the CNN model (i.e., 68.1% and 67.7%).Given the similar structures of AC-GAN discriminators and CNN models, it is hypothesized that AC-GAN discriminators can perform better than CNNs.This is because the effect of the adversarial training approach embedded up-sampling techniques into the model-training process.
An automatic multi-axis solar tracking system in Ramadi city: design and implementation

In this paper, a complete design and implementation of an automatic Multi-Axis solar tracking system has been introduced. The main purpose of this system is to track Sun location and gain the maximum energy output of the solar panels. The system is Multi-axis using microcontroller and photocells to control the direction of the penal, whereas the penal is facing the sun at all times of the day. The system is a combination of hardware and software parts that work concurrently to achieve a precise angular Sun tracking. A Base, Penal Frame, Super Jack Motors, high-efficiency Solar Panel, Arduino Uno microcontroller, Relays, Rechargeable battery, Light Dependent Resistor (LDR) have been used for the system’s hardware part. These hardware parts need a high-level programming code, as a software part, to be embedded in the microcontroller to get an effective and precise solar tracking system. The results of the presented system were compared to a fixed direction system. The results show significant efficiency improvement of 24% over the static one. For a city like Ramadi, which has a high irradiance all over the year, it is very fruitful to use such tracking systems along with photovoltaic installation systems.&lt;div align="center"&gt; &lt;/div&gt;
A social constructivism framing of mobile pedagogy in english language teaching in the digital era

&lt;p&gt;Social Constructivism is elaborated as a theoretical background to support modern pedagogy with mobile technology in the digital age. Studies of theories and framework in terms of mobile pedagogy are combed up to find its connection between language teaching and Social Constructivism. Four theoretical framework of mobile pedagogy are listed in this paper to establish the link with Social Constructivism. The analysis of the Zone of Proximal Development from Social Constructivism theory indicates that mobile pedagogy, integrated with technology, pedagogy and subject content, is student-driven model which needs teacher’s guidance in the level that students cannot scaffold knowledge on their own. Teachers have to update pedagogical concept with enhanced technological knowledge for the demand of education in the digital era.&lt;/p&gt;
Indistinguishability and unextractablility of password-based authentication in blockchain

Password is commonly used to protect Bitcoin wallet, the most known application of blockchain. In this paper, we investigate a subtle issue when forgetting password: The account owner uses guessed passwords during the authentication with a service provider. This is different from password guessing by cyber attackers, because passwords guessed by the account owner are (most likely) his/her passwords (or their minor variations) registered with other service providers. Thus, the confidentiality of incorrect passwords in unsuccessful authentication needs protection. To capture this security requirement, we define two security goals: Indistinguishability of Incorrect Passwords (IND-PW) and Unextractablility of Incorrect Passwords (UNE-PW). Our analysis shows that: (1) IND-PW is NOT achievable if password is the only authentication credential of the client, and (2) Two common authentication methods in online services, Basic and Digest Access Authentication (in conjunction with SSL), CANNOT provide UNE-PW.
A study about Explainable Articial Intelligence: using decision tree to explain SVM

The technologies supporting Artificial Intelligence (AI) have advanced rapidly over the past few years and AI is becoming a commonplace in every aspect of life like the future of self-driving cars or earlier health diagnosis. For this to occur shortly, the entire community stands in front of the barrier of explainability, an inherent problem of latest models (e.g. Deep Neural Networks) that were not present in the previous hype of AI (linear and rule-based models). Most of these recent models are used as black boxes without understanding partially or even completely how different features influence the model prediction avoiding algorithmic transparency. In this paper, we focus on how much we can understand the decisions made by an SVM Classifier in a post-hoc model agnostic approach. Furthermore, we train a tree-based model (inherently interpretable) using labels from the SVM, called secondary training data to provide explanations and compare permutation importance method to the more commonly used measures such as accuracy and show that our methods are both more reliable and meaningful techniques to use. We also outline the main challenges for such methods and conclude that model-agnostic interpretability is a key component in making machine learning more trustworthy.
MLP Model for Emotion Recognition using Acoustic Features

Emotion recognition is an important topic of research lately.There are already a few methods that can predict emotion but, in this paper, we im to make a unique model that is not only lightweight but also fast and accurate.We are currently focussing on predicting 4 emotions like angry, sad, neutral and happy through frequencyanalysis.To achieve this, we first needto extract7 features including 195 sub-features from an audio file.The features like MFCC, Root mean square, Spectral contrast, Tonnetz, Zero-crossing rate, Mel spectrogram frequency and Chroma combined are useful in determining emotion from the audio frequencies accurately.We have trained the model using RAVDESS and TESS which are both open source databases for audio emotions.We made an intelligent python program that can analyze the frequency pattern of each emotion using MLP classifier and predict the emotion with an accuracy of 83%.Moreover, we can predict emotion either from a file or from the microphone instantly.
Reverse Engineering of Human Brain for the field of Artificial Intelligence

What differentiate human beings from other animal world is human brain.What if we can somehow imitate working of human brain into our computers for processing highly computation tasks in much less time?Nowadays research is going on in this topic about reverse engineering the human brain to better know about how information is processed in our biological neural network.Using concept of reverse engineering applied to human brain, researchers and scientists came to know about working of brain and it is imitated in computers to develop various algorithms used in the field of Artificial Intelligence.One of the most prominent amongst them is the Convolutional Neural Networks(CNN).In this paper we focus on how biological neural networks work with the help of reverse engineering and the consequences of this reverse engineering which led to development of Artificial Neural Networks(ANN) and Convolutional Neural Networks(CNN).
THE PERFORMANCE OF SOME MACHINE LEARNING APPROACHES IN HUMAN MOVEMENT ASSESSMENT

The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of human movements.Statistical and machine learning approaches map recorded movement instances to expert scores to train models for the automated assessment of new movements.However, there are many variations in selecting the approaches and setting the parameters for achieving high performance, i.e., high accuracy and low response time.The present paper researches the design space and the impact of approaches of statistical and machine learning on accuracy and response time in human movement assessment.Results show that a random forest regression approach outperforms linear regression, support vector regression and neuronal network approaches.Since the results do not rely on the movement specifics, they can help improving the performance of automated human movement assessment, in general.
Connecting with Faculty and Students through Course-Related LibGuides

Academic librarians face increasing challenges when trying to connect their services and resources with faculty and students, especially as courses move online and are taught by adjunct faculty. Online research guides, most commonly LibGuides, can be automatically inserted into course shells within Learning Management Systems (LMS) using Learning Tool Interoperability (LTI) protocols, but an alternative to automatic integration is for librarians to match and suggest appropriate subject guides to faculty for inclusion in their courses. Personally matching and recommending LibGuides to faculty can be an effective form of library outreach and relationship-building. This article describes a pilot project of suggesting specific LibGuides to faculty and its success in forging and strengthening several library-department connections.
Leveraging Professional Wordlists for Productive Vocabulary Knowledge

Productive knowledge of subject-specific vocabulary is essential for successful professional communication.This article puts forward the case for an innovative approach to course and materials design in English for Professional Purposes (EPP) that highlights the importance of careful analysis of the vocabulary of specific professional discourse.It argues that EPP courses would benefit from being informed by corpus-based analysis of vocabulary and collocational choices in texts used in professional contexts.The argument is supported by the results of the corpus-based analysis of the discourse in the professional context of the European Union institutions.The analysis was carried out using the 1-million-word English EU Discourse (EEUD) Corpus, which was created based on a target needs analysis.The present study contributes to knowledge in the field by establishing the first comprehensive EU word and collocation list, which comprises 405 word families and is complemented by collocational patterns specific to English EU discourse.The results underpin the article's central argument that collocational information should be used to enrich professional wordlists as they reveal subject-specific patterns that are fundamental for productive vocabulary knowledge in efficient professional communication.The pedagogic applications of the word and collocation lists are also demonstrated.