Reviewerly/exHarmony huggingface dataset:
An Obstacles Avoidance Algorithm Based on Improved Artificial Potential Field

Obstacle avoidance is one of the most important issue in the motion planning and control of robot. There are many algorithms to avoid obstacles, but obstacle avoidance algorithm for manipulator is needed consider not only end effector, but also all links of manipulator. In terms of real-time and speed of all system, the algorithm also should be easy as possible. In this paper, an algorithm based on the improved artificial potential field is proposed to solve the problem of the local optimum. The idea of improvement is to find a path by fitting curve with target point and critical oscillating points. There is also a comparison in this paper with planning a path by tangent line. The conclusion proves that the path is much smoother by the algorithm based on improved artificial potential field in this paper. Furthermore, the problem of judder on the trajectory of traditional artificial potential field is also solved in this paper by an improved repulsive potential field function. Finally, the algorithm is proved by planning a smooth path for obstacle avoidance on manipulator: KinovaGen3 in MATLAB.
Guessing State Tracking for Visual Dialogue

The Guesser is a task of visual grounding in GuessWhat?! like visual dialogue. It locates the target object in an image supposed by an Oracle oneself over a question-answer based dialogue between a Questioner and the Oracle. Most existing guessers make one and only one guess after receiving all question-answer pairs in a dialogue with the predefined number of rounds. This paper proposes a guessing state for the Guesser, and regards guess as a process with change of guessing state through a dialogue. A guessing state tracking based guess model is therefore proposed. The guessing state is defined as a distribution on objects in the image. With that in hand, two loss functions are defined as supervisions to guide the guessing state in model training. Early supervision brings supervision to Guesser at early rounds, and incremental supervision brings monotonicity to the guessing state. Experimental results on GuessWhat?! dataset show that our model significantly outperforms previous models, achieves new state-of-the-art, especially the success rate of guessing 83.3% is approaching the human-level accuracy of 84.4%.
Design and Development of Attendance System Application Using Android-Based Flutter

Student attendance application prototype using Android-based flutter: a case study in the electrical engineering department, University of Mataram. It is an application whose purpose is used in student attendance system activities by preventing cheating. This application is equipped with a validation feature using a QR code and geolocation to overcome student cheating during attendance activities. This application development using a flutter software development kit. This application development is used by two users that are students and lecturers, students use the application for attendance facilities, lecturers use the application to set open or close sessions, and check attendance. Tests carried out on the application are installation, application usage, QR code scanning methods, geolocation features, and Mean Opinion Score (MOS). As a result of the development of this application, it can be used to simulate attendance activities in a class attended by 10 students and it takes 5 minutes 58 seconds. This application also gets a mean opinion score measurement of 81.08.
Transporting Labels via Hierarchical Optimal Transport for Semi-Supervised Learning

Semi-Supervised Learning (SSL) based on Convolutional Neural Networks (CNNs) have recently been proven as powerful tools for standard tasks such as image classification when there is not a sufficient amount of labeled data available during the training. In this work, we consider the general setting of the SSL problem for image classification, where the labeled and unlabeled data come from the same underlying distribution. We propose a new SSL method that adopts a hierarchical Optimal Transport (OT) technique to find a mapping from empirical unlabeled measures to corresponding labeled measures by leveraging the minimum amount of transportation cost in the label space. Based on this mapping, pseudo-labels for the unlabeled data are inferred, which are then used along with the labeled data for training the CNN. We evaluated and compared our method with state-of-the-art SSL approaches on standard datasets to demonstrate the superiority of our SSL method.
Graph Regularized Deep Discrete Hashing for Multi-Label Image Retrieval

Multi-label hashing is a new research topic in image retrieval. As images are usually associated with multiple semantic labels, there is multi-level semantic similarity such as very similar, normally similar and dissimilar among multi-label images. In order to obtain the multi-level semantic similarity, this letter constructs a hypergraph in label space by creating a hyperedge for each semantic label and including all images annotated with a common label into one hyperedge. In this way, the number of common hyperedges shared by the vertices in hypergraph can be used to encode the high-order semantic relations among multiple images. Considering the useful similarity information hidden in the instance space, a kNN graph in instance space is further constructed. By learning from both the hypergraph and kNN graph with spectral learning strategy, a graph regularized deep discrete hashing is developed which updates graph regularized binary codes and deep neural network based robust features iteratively in a discrete optimization framework. The results in comparison with nine state-of-the-art hashing methods on two multi-label image datasets such as MIRFLICKR-25 K and NUS-WISE demonstrate its effectiveness.
Evolving the Behavior of Machines: From Micro to Macroevolution

Evolution gave rise to creatures that are arguably more sophisticated than the greatest human-designed systems. This feat has inspired computer scientists since the advent of computing and led to optimization tools that can evolve complex neural networks for machines—an approach known as “neuroevolution.” After a few successes in designing evolvable representations for high-dimensional artifacts, the field has been recently revitalized by going beyond optimization: to many, the wonder of evolution is less in the perfect optimization of each species than in the creativity of such a simple iterative process, that is, in the diversity of species. This modern view of artificial evolution is moving the field away from microevolution, following a fitness gradient in a niche, to macroevolution, filling many niches with highly different species. It already opened promising applications, like evolving gait repertoires, video game levels for different tastes, and diverse designs for aerodynamic bikes.
A Blockchain-based Architecture for Query and Registration of Student Degree Certificates

Blockchain is an emerging technology that has piqued the interest of researchers and industry today. Following the success of this technology in the financial market with virtual currencies, blockchain begins to be used by different domains such as government and education as it provides a reliable, scalable and unchanging distributed environment for conducting and storing transactions on a network. Higher education is a system with many challenges that could be solved by adopting this technology. Protecting data transactions that involve student degrees is one of the challenges considered by educational institutions. Thus, this paper presents a reference architecture proposal based on blockchain technology and a proof of concept to register and consult student certificates issued by higher education institutions. With this architecture, we intend to evaluate a new business model in this technology applied to the educational domain. As a proof of concept, we presented a prototype implementation using smart contracts based on the Ethereum platform and an evaluation made by experts in high education.
Improved Emotion Recognition from Microblog Focusing on Both Emoticon and Text

Microblog is very popular among social medias for expressing emotions. Therefore, emotion recognition from microblogs has emerged as an interesting research topic in different prospects. Automatic emotion recognition from microblogs is a challenging machine learning problem. Since emoticons (the graphical emotional icon) are gradually becoming one of the most used elements with texts in microblogs, its proper focus is important for appropriate emotion recognition. Emoticon for emotion recognition has been ignored in most of the previous studies. In this paper, an improved emotion recognition from microblog has been proposed preserving the semantic relation between texts and emoticons. In this case, we considered emoticons as special expressions of emotions of the user and represented the emoticons by appropriate emotional words. We maintained the same sequence of emoticons and text appeared in the microblog. Long Short-Term Memory (LSTM) is used for the classification of emotion. Experimental results on Twitter data reveal the efficiency of the proposed method with higher recognition accuracy compared to recognition considering texts only.
Distributed Density Peaks Clustering Revisited

Density Peaks (DP) Clustering organizes data into clusters by finding peaks in dense regions. This involves computing density ( <inline-formula><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> ) and distance ( <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> ) of every point. As such, though DP has been very effective in producing high quality clusters, their complexity is O( <inline-formula><tex-math notation="LaTeX">$N^2$</tex-math></inline-formula> ) where <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of data points. In this paper, we propose a fast distributed density peaks clustering algorithm, FDDP, based on the z-value index. In FDDP, we first employ the z-value index to map multi-dimensional data points into one dimensional space, and then range-partition the data according to the z-value to balance the load across the processing nodes. We ensure minimal overlapping range to handle computations at the boundary points. We also propose FC, an efficient algorithm that employs a forward computing strategy to calculate <inline-formula><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> linearly. Additionally, we propose another algorithm, CB, which uses a caching and efficient searching strategy to compute <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> . Moreover, FDDP is able to reduce the time complexity from <inline-formula><tex-math notation="LaTeX">$O(N^2)$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(N\cdot log(N))$</tex-math></inline-formula> . We provide a theoretical analysis of FDDP and evaluated FDDP empirically. Our experimental results show that FDDP outperforms the state-of-the-art algorithms significantly.
Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

Abstract High dynamic range (HDR) and wide color gamut imagery has an established video ecosystem, spanning image capture to encoding and display. This drives the need for evaluating how image quality is affected by the multitudes of ecosystem parameters. The simplest quality metrics evaluate color differences on a pixel‐by‐pixel basis. In this article, we evaluate a series of these color difference metrics on four HDR and three standard dynamic range publicly available distortion databases consisting of natural images and subjective scores. We compare the performance of the well‐established CIE L * a * b * metrics ( Δ E 00 , Δ E 94 ) alongside two HDR‐specific metrics ( Δ E Z [J z a z b z ], Δ E ITP [IC T C P ]) and a spatial CIE L * a * b * extension ( ). We also present a novel spatial extension to Δ E ITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.
Mobile Phones Know Your Keystrokes through the Sounds from Finger’s Tapping on the Screen

Mobile phones nowadays are equipped with at least dual microphones. We find when a user is typing on a phone, the sounds generated from the vibration caused by finger's tapping on the screen surface can be captured by both microphones, and these recorded sounds alone are informative enough to infer the user's keystrokes. This ability can be leveraged to enable useful application designs, while it also raises a crucial privacy risk that the private information typed by users on mobile phones has a great potential to be leaked through such a recognition ability. In this paper, we address two key design issues and demonstrate, more importantly alarm people, that this risk is possible, which could be related to many of us when we use our mobile phones. We implement our proposed techniques in a prototype system and conduct extensive experiments. The evaluation results indicate promising successful rates for more than 4000 keystrokes from different users on various types of mobile phones.
Increasing User Engagement in Virtual Reality:The Role of Interactive Digital Narratives to Trigger Emotional Responses

Immersive multimedia technologies such as virtual reality (VR) create narrative experiences in the digital medium, thus revolutionizing how people communicate, learn, and think. These Interactive Digital Narratives (IDN) shape end-users’ experience with a broad potential for various applications. A fundamental aspect of achieving this potential is the establishment of a positive and engaging user experience. This study investigates how enabling the interactive narrative in a VR setting affects the engagement of the users. The study we base this work on involved thirty-two participants in a controlled experiment where they were asked to explore a designed VR environment, with and without a digital narrative. We observed a significant increase in the participants’ level of engagement in the narrative-based environment compared to the non-narrative VR environment. The results showed how the IDN in VR generates an increased emotional response, strengthening the users’ engagement, showing that IDN can be considered an essential factor in shaping the positive experience of end-users, thus shaping a better society.
Towards Certifiable Adversarial Sample Detection

Convolutional Neural Networks (CNNs) are deployed in more and more classification systems, but adversarial samples can be maliciously crafted to trick them, and are becoming a real threat. There have been various proposals to improve CNNs' adversarial robustness but these all suffer performance penalties or have other limitations. In this paper, we offer a new approach in the form of a certifiable adversarial detection scheme, the Certifiable Taboo Trap (CTT). This system, in theory, can provide certifiable guarantees of detectability of a range of adversarial inputs for certain l-∞ sizes. We develop and evaluate several versions of CTT with different defense capabilities, training overheads and certifiability on adversarial samples. In practice, against adversaries with various l-p norms, CTT outperforms existing defense methods that focus purely on improving network robustness. We show that CTT has small false positive rates on clean test data, minimal compute overheads when deployed, and can support complex security policies.
Learning to Learn Parameterized Classification Networks for Scalable Input Images

Convolutional Neural Networks (CNNs) do not have a predictable recognition behavior with respect to the input resolution change. This prevents the feasibility of deployment on different input image resolutions for a specific model. To achieve efficient and flexible image classification at runtime, we employ meta learners to generate convolutional weights of main networks for various input scales and maintain privatized Batch Normalization layers per scale. For improved training performance, we further utilize knowledge distillation on the fly over model predictions based on different input resolutions. The learned meta network could dynamically parameterize main networks to act on input images of arbitrary size with consistently better accuracy compared to individually trained models. Extensive experiments on the ImageNet demonstrate that our method achieves an improved accuracy-efficiency trade-off during the adaptive inference process. By switching executable input resolutions, our method could satisfy the requirement of fast adaption in different resource-constrained environments. Code and models are available at https://github.com/d-li14/SAN.
A Robust Approach for Continuous Interactive Reinforcement Learning

Interactive reinforcement learning is an approach in which an external trainer helps an agent to learn through advice. A trainer is useful in large or continuous scenarios; however, when the characteristics of the environment change over time, it can affect the learning. Robust reinforcement learning is a reliable approach that allows an agent to learn a task, regardless of disturbances in the environment. In this work, we present an approach that addresses interactive reinforcement learning problems in a dynamic environment with continuous states and actions. Our results show that the proposed approach allows an agent to complete the cart-pole balancing task satisfactorily in a dynamic, continuous action-state domain.
GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-Tuning for Alzheimer’s Disease Diagnosis from MRI

Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest dosage of radiation. Magnetic Resonance Imaging (MRI), in contrast, is more widely available and provides more flexibility when setting the desired image resolution.
Pre-trained Data Augmentation for Text Classification

Data augmentation is a widely adopted method for improving model performance in image classification tasks. Although it still not as ubiquitous in Natural Language Processing (NLP) community, some methods have already been proposed to increase the amount of training data using simple text transformations or text generation through language models. However, recent text classification tasks need to deal with domains characterized by a small amount of text and informal writing, e.g., Online Social Networks content, reducing the capabilities of current methods. Facing these challenges by taking advantage of the pre-trained language models, low computational resource consumption, and model compression, we proposed the PRE-trained Data AugmenTOR (PREDATOR) method. Our data augmentation method is composed of two modules: the Generator, which synthesizes new samples grounded on a lightweight model, and the Filter, that selects only the high-quality ones. The experiments comparing Bidirectional Encoder Representations from Transformer (BERT), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Multinomial Naive Bayes (NB) in three datasets exposed the effective improvement of accuracy. It was obtained 28.5% of accuracy improvement with LSTM on the best scenario and an average improvement of 8% across all scenarios. PREDATOR was able to augment real-world social media datasets and other domains, overcoming the recent text augmentation techniques.
R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions

Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications. Since the meaning of data utility in different applications may vastly differ, a key challenge is to find the optimal randomization mechanism, i.e., the distribution and its parameters, for a given utility metric. Existing works have identified the optimal distributions in some special cases, while leaving all other utility metrics (e.g., usefulness and graph distance) as open problems. Since existing works mostly rely on manual analysis to examine the search space of all distributions, it would be an expensive process to repeat such efforts for each utility metric. To address such deficiency, we propose a novel approach that can automatically optimize different utility metrics found in diverse applications under a common framework. Our key idea that, by regarding the variance of the injected noise itself as a random variable, a two-fold distribution may approximately cover the search space of all distributions. Therefore, we can automatically find distributions in this search space to optimize different utility metrics in a similar manner, simply by optimizing the parameters of the two-fold distribution. Specifically, we define a universal framework, namely, randomizing the randomization mechanism of differential privacy (R2DP), and we formally analyze its privacy and utility. Our experiments show that R2DP can provide better results than the baseline distribution (Laplace) for several utility metrics with no known optimal distributions, whereas our results asymptotically approach to the optimality for utility metrics having known optimal distributions. As a side benefit, the added degree of freedom introduced by the two-fold distribution allows R2DP to accommodate the preferences of both data owners and recipients.
What affects the price movements in Bitcoin and Ethereum?

Abstract This study aims to explain price movements in the two largest cryptocurrencies that represent the majority of cryptocurrency market capitalization—Bitcoin and Ethereum. A VAR‐GARCH‐BEKK model is estimated to analyze how Google search interest, number of tweets and active addresses on the blockchain impact prices of Bitcoin and Ethereum over time. We find solid evidence that the amount of active addresses is the most significant variable among others influencing price movements in Bitcoin and Ethereum. Based on spillover effects and GIRFs, Google searches and tweets, to a certain extent, have impacts on the Bitcoin and Ethereum prices, but the impacts are weaker than that of active addresses in terms of magnitude and significance.