Reviewerly/exHarmony huggingface dataset:
Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning

Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods that use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment.
Cirebon Mask Classification using Robust K-Nearest Neighbour

Indonesia culture captivates people both domestically and internationally. One of the prominent cultures is Cirebon Mask. There are five types of Cirebon masks: Panji, Samba, Rumyang, Tumenggung, and Kelana. In this work, we present texture features using first order statistical feature with outlier detection using z-score while K-Nearest Neighbour acted as the classifier. From this experiment our proposed methods increase the accuracy up to 10 % compared to basic method.
Enhance Particle’s Exploration of Particle Swarm Optimization With Individual Particle Mutation

This paper proposed the restart of slow improvement particle with Mutation of its position. This can alleviate the trapped in local optima problem of Particle swarm optimization (PSO). The main characteristic of this method is the gradually restart of particle instead of restart all particle at the same time (rerun). This seems to maintain fast convergence of PSO and avoid overhead of restart the whole swarm. The performance of the proposed method is compared to others algorithms with 26 benchmark test functions. The results confirm the faster convergence with optimum results of the proposed method in most test functions.
Securing User Access at IoT Middleware Using Attribute Based Access Control

IoT middleware is an additional layer between IoT devices and the cloud applications that reduces computation and data handling on the cloud. In a typical IoT system model, middleware primarily connects to different IoT devices via IoT gateway. Device data stored on middleware is sensitive and private to a user. Middleware must have built-in mechanisms to address these issues, as well as the implementation of user authentication and access control. This paper presents the current methods used for access control on middleware and introduces Attribute-based encryption (ABE) on middleware for access control. ABE combines access control with data encryption for ensuring the integrity of data. In this paper, we propose Ciphertext-policy attribute-based encryption, abbreviated CP-ABE scheme on the middleware layer in the IoT system architecture for user access control. The proposed scheme is aimed to provide security and efficiency while reducing complexity on middleware. We have used the AVISPA tool to strengthen the proposed scheme.
Scam Detection Assistant: Automated Protection from Scammers

Scams, also known as social engineering attacks, are an extremely common and dangerous threat today. Scams typically result in financial loss by convincing a victim to perform an ill-advised action such as sending money, or convincing him to provide private information. In this paper we present an approach to detect scams, focusing on scams which are conveyed in-person, over the phone, or via text/chat message. We present a tool called Scam Detection Assistant (SDA) which analyzes attack content to detect inappropriate statements which are indicative of social engineering attacks. A great deal of previous research in the detection of scams focuses on the detection of email scams, phishing emails. Previous work relies heavily on the analysis of various metadata specific to the email attack vector, including header information and URL links. SDA is novel compared to previous work because it focuses on the natural language contained in the attack, performing semantic analysis of the content to detect malicious intent. Focusing on content analysis makes our approach applicable to detect scams using non-email attack vectors, including texting applications, chat applications, and phone/in-person attacks which have been converted to text using a speech-to-text application.
Fractal Image Processing and Analysis for Compression of Hyperspectral Images

Hyperspectral imaging is a technique in which the information is collecting and processing across an electromagnetic spectrum. Fractal Image Processing deals with various dimensions of an image. Hyperspectral analysis are applied on multiple frequency bands of images, and produces spatial and spectral information. HSI can elongate 390 to 700 nm i.e UV to infrared and near-infrared wavelength regions. This technique based system engenders narrow band(<; 390 to 700 nm>) images of different wavelengths. By utilizing HIS, visualization can be elongated to invisible wavelengths. Hyperspectral based techniques have applications in various fields such as medical diagnosis, agriculture, food processing, remote sensing etc. In this project, the compression of hyperspectral images is considered. Present work involves Modified DCT-Discrete Cosine Transformation based image compression. Compared with the convention lossless compression techniques of the benchmark multi-component and hyperspectral (JPEG2000), the MOD-DCT lossless algorithm produces considerable reduce in compressed file size for beyond visible range. After implementation of Modified DCT for hyperspectral images high compression ratio was achieved.
deb2viz: Debiasing gender in word embedding data using subspace visualization

Word embedding have been used in numerous Natural Language Processing and Machine Learning tasks. However, it is a high-dimensional vector field that propagate stereotypes to software applications. Its current debiasing frameworks do not completely capture its embedded patterns. In this paper, we propose deb2viz, a visual debiasing approach that explores and manipulates the high-dimensional patterns of word embedding field. First, we partition this vector field into interrelated low-dimensional subspaces to equalize and neutralize distances between gender-definitional and gender-neutral words. To further reduce gender bias, we update the distances of appropriate nearest neighbors for gender-neutral words to be arbitrarily close. Experimental results on several benchmark standards show the competitiveness of our proposed method in mitigating bias within pre-trained word2vec embedding model.
Performance Evaluation of Real-Time Object Detection Algorithms

We compare several techniques for object detection regarding to speed and accuracy. The purpose of object detection is to find precise locations of the objects, one location at a time or all locations of all objects in an image. There are two categories of feature extraction in object detection algorithms: knowledge-based and learning based approaches. Knowledge-based approach is a hand-craft design feature extraction that is image representation is created by using engineering design. Therefore, we had known a priori what hand-craft features will look like. In contrast to learning based approach, the features are extracted by machine learning such as neural network and deep learning. Hence, image representation does not known before extracting the features. Experimental results have shown that YOLO and R-CNN are the most appropriate algorithm for real-time and low-error rate object detections with the help of GPU hardware acceleration. We will show the comparison on speed and accuracy of several algorithms.
Tsunami Early Warning Detection using Bayesian Classifier

Tsunami is a term for disaster where the seawater rises to the land caused by the great earthquake with a shallow epicenter in the ocean. Indonesia is vulnerable to the tsunami; especially in the area where the junction of Eurasia, Indo-Australia, and Pacific Plates meet. Soon after the earthquake happened, there is a time interval before a tsunami to come. The break-time can be used to alert people to evacuate themselves from the Tsunami. This study proposed a tsunami early warning system, which autonomously predicts the potential of tsunami hazard using machine learning techniques. Bayesian classifier was trained to predict the tsunami potential. The tsunami training data was taken from the InaTews website; a national project of Indonesia that involves many institutions, local or international. InaTews informed the real-time data of earthquakes and tsunami that happened in Indonesia. The proposed methods extracted some invariant characteristics from the data and trained the machine learning classifiers to predict the potential result; either tsunami or not-tsunami using three parameters of earthquake: magnitude, epicenter, and location. The experiment gave an optimistic result; using a varying number of training data, it gained 92.37% on the average accuracy rate and 0.98 on the F1 score.
Industrial Production Process Improvement by a Process Engine Visual Analytics Dashboard

Digitalization reshapes production in a sense that production processes are required to be more flexible and more interconnected to produce products in smaller lot sizes.This makes the process improvement much more challenging, as traditional approaches, which are based on the learning curve, are difficult to apply.Data-driven technologies promise help in learning faster by making use of the massive data volumes collected in production environments.Visual analytics approaches are particularly promising in this regard as they aim to enable engineers with their rich domain knowledge to identify opportunities for process improvements.Based on the assumption that process improvement should be connected with the process engine managing the process execution, we propose a visual analytics dashboard which integrates process models.Based on a case study in the smart factory of Vienna, we conducted two pair analytics sessions.The first results seem promising, whereas domain experts articulate their wish for improvements and future work.
A Workflow Interoperability Approach Based on Blockchain

Workflow interoperability generally refers to the ability for workflow enactment services to coordinate work. However, the lack of trust is often a roadblock, especially when workflow enactment services interoperate across organizational boundaries. Blockchain technology is a technology for data sharing across a network of untrusted participants. In this paper, we propose a blockchain-based workflow interoperability approach. Workflow enactment services communicate and interoperate with each other via blockchain instead of trusting a central authority, but trust is maintained. Furthermore, blockchain documents interoperation of workflow enactment services, such an audit trail can be used to depict a complete inter-organizational collaboration. Our approach comprises the combination of an interoperability interface and a general interoperability service. Interoperability service runs on a blockchain environment, and workflow enactment services can call interoperability service through their interoperability interface to communicate with each other. We implement the prototype of our approach and demonstrate its feasibility by applying it to an inter-organizational collaboration case. We evaluate our approach mainly via conducting a performance evaluation.
Attentional Generative Adversarial Networks With Representativeness and Diversity for Generating Text to Realistic Image

In recent years, with the emergence and rapid development of Generative Adversarial Networks (GANs), the generation of realistic images consistent with their semantics based on text description has become one of the most popular research directions in the field of computer vision. Although the idea of applying attention mechanism has been raised out in many implementation methods, it is required to treat the sub-regions of the generated images equally. For this reason, this paper proposes a novel generative adversarial networks, rdAttnGAN, which generate text to fine images by training multi-pair generators and discriminators. Comparing with the conventional models, it pays more attention to the representativeness and diversity of the generated images. In addition, an optimization method for calculating the similarity between the generated image and the text description is also introduced to enhance the representative judgment of the images. By paying more attention to the generation of important sub-regions of images, the model can further optimize the training of generators. In order to verify the effectiveness of our proposed framework, a comprehensive set of experiments are conducted on CUB dataset and COCO dataset. The results demonstrate viability to improve the representativeness and diversity of images with our rdAttnGAN.
Partial Order Rank Features in Colour Space

Partial orders are the natural mathematical structure for comparing multivariate data that, like colours, lack a natural order. We introduce a novel, general approach to defining rank features in colour spaces based on partial orders, and show that it is possible to generalise existing rank based descriptors by replacing the order relation over intensity values by suitable partial orders in colour space. In particular, we extend a classical descriptor (the Texture Spectrum) to work with partial orders. The effectiveness of the generalised descriptor is demonstrated through a set of image classification experiments on 10 datasets of colour texture images. The results show that the partial-order version in colour space outperforms the grey-scale classic descriptor while maintaining the same number of features.
Forecast uncertainty‐based performance degradation diagnosis of solar PV systems

In this study, the authors are interested in estimating how much a PV system underperforms than expected by exploiting forecast uncertainty. For this, they first study a forecast accuracy-related forecast uncertainty metric using the ensemble method based on the dropout technique, which is widely used in deep learning forecasting models. Given the forecast accuracy-related uncertainty metric, the rationale of the authors' approach is that forecast accuracy is likely to decrease compared to the normal case of similar uncertainty metric values if any performance degradation happens. It is because similar uncertainty metric values are likely to show similar forecast accuracy. Therefore, they generate a standard table by simulating possible performance degradation cases and conduct the performance degradation diagnosis by looking up the standard table based on the uncertainty metric. From the experiments, in the case of persistent degradation, they show that their approach estimates the performance degradation with the estimation error of around 1% while an uncertainty-unaware approach shows the estimation error of up to 5%. In the case of temporal degradation, their approach shows the estimation error of around 3%, while the uncertainty-unaware approach does not show meaningful result.
Potential of using visual imagery to revolutionise measurement of emotional health

Appropriate measurement of emotional health by all those working with children and young people is an increasing focus for professional practice. Most of the tools used for assessment or self-assessment of emotional health were designed in the mid-20th century using language and technology derived from pen and paper written texts. However, are they fit for purpose in an age of pervasive computing with increasingly rich audiovisual media devices being in the hands of young people? This thought piece explores how the increased use of visual imagery, especially forms that can be viewed or created on digital devices, might provide a way forward for more effective measuring of emotional health, including smiley faces, other emojis and other potential forms of visual imagery. The authors bring together perspectives from healthcare, counselling, youth advocacy, academic research, primary care and school-based mental health support to explore these issues.
Competing Ratio Loss for Multi-class image Classification

Cross-entropy loss function (CEL) is widely used for training a multi-class classification deep convolutional neural network (DCNN). While CEL has been successfully implemented in image classification tasks, it only focuses on the posterior probability of correct class when the labels of training images are one-hot. It cannot be discriminated against the classes not belong to correct class (wrong classes) directly. Negative Log Likelihood Ratio Loss (NLLR) is proposed to better discriminate the correct class from competing wrong classes. But optimization of the loss function is normally presented as a minimization problem. In training DCNN, the value of NLLR is not constantly positive or negative, which affects the convergence of NLLR adversely. So, we propose competing ratio loss (CRL), which calculates the posterior probability ratio between the correct class and competing wrong classes to better widen the difference between the probability of the correct class and the probabilities of wrong classes, which also assures the value of CRL is constantly positive. Through massive experiments, we demonstrate the effectiveness and robustness of CRL on deep convolutional neural networks, our CRL outperforms CEL and NLLR on CIFAR-10/100 datasets.
A Hybrid Cryptosystem and Watermarking for Secure Medical Image Transmission

Advances in computing and communication technologies have provided new methods to store and access medical data electronically and distribute them over open communication networks. Today, patients themselves can access their medical information themselves and medical information can be transmitted among medical institutions as well as stakeholders in the health sector. Accompanying these benefits are concomitant risks for patient medical records in electronic formats and strictly personal medical documentations being transmitted and accessible over open communication channels such as the Internet. Thus it is common knowledge that there should be in place network-level security measures and protocols in medical information systems. Many security schemes that were based on cryptography, watermarking and steganography have been proposed and implemented to secure medical data. However, an apt review of relevant literature revealed that in many implementations robustness against attacks is not guaranteed. Issues bordering on low embedding capacity, low robustness, low imperceptibility and bad trade tradeoff between robustness and capacity are evident in many implementations. In this paper, a hybrid Rivest-Shamir-Adleman (RSA) algorithm, Rivest Cipher 4 (RC4) algorithm and Spread Spectrum techniques were proposed for securing medical image data over open communication networks. The performance of the proposed scheme was evaluated using Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Mean Square Error (MSE) and Bit Error Rate (BER). For the five sample medical images used to test the scheme, the BER value is zero while the PNSR and SNR are consistent and they returned desirable high values. The MSE values for the images were low. The average values of the PSNR, SNR and MSE are 51.88 dB, 43.38 dB and 0.113 respectively. Hence, the proposed scheme is utterly revertible, robust and highly imperceptible; the original images can be retrieved by the recipient without any deformation or alteration.
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision TreeRay Li, Percy Liang, and Stephen MussmannRay Li, Percy Liang, and Stephen Mussmannpp.102 - 121Chapter DOI:https://doi.org/10.1137/1.9781611975994.7PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Decision Tree is a classic formulation of active learning: given n hypotheses with nonnegative weights summing to 1 and a set of tests that each partition the hypotheses, output a decision tree using the provided tests that uniquely identifies each hypothesis and has minimum (weighted) average depth. Previous works showed that the greedy algorithm achieves a O(log n) approximation ratio for this problem and it is NP-hard beat a O(log n) approximation, settling the complexity of the problem. However, for Uniform Decision Tree, i.e. Decision Tree with uniform weights, the story is more subtle. The greedy algorithm's O(log n) approximation ratio was the best known, but the largest approximation ratio known to be NP-hard is 4 – ε. We prove that the greedy algorithm gives a approximation for Uniform Decision Tree, where COPT is the cost of the optimal tree and show this is best possible for the greedy algorithm. As a corollary, we resolve a conjecture of Kosaraju, Przytycka, and Borgstrom [20]. Our results also hold for instances of DecisioN Tree whose weights are not too far from uniform. Leveraging this result, for all α ϵ (0, 1), we exhibit a approximation algorithm to Uniform Decision Tree running in subexponential time . As a corollary, achieving any super-constant approximation ratio on Uniform Decision Tree is not NP-hard, assuming the Exponential Time Hypothesis. This work therefore adds approximating Uniform Decision Tree to a small list of natural problems that have subexponential time algorithms but no known polynomial time algorithms. Like the analysis of the greedy algorithm, our analysis of the subexponential time algorithm gives similar approximation guarantees even for slightly nonuniform weights. A key technical contribution of our work is showing a connection between greedy algorithms for Uniform Decision Tree and for Min Sum Set Cover. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Liver CT Image Segmentation Based on Modified Canny Algorithm

A modified Canny algorithm called Canny edge detection Level set algorithm is proposed in this work for the segmentation of liver CT image. This modified algorithm based on the combination of Level set image segmentation method and Canny edge detection method. The basic purpose is that the liver CT images affected by noises can be segmented according to the mapping from low-dimension to high-dimension. This segmentation method has a high degree of adaptability and precision characteristics on the uneven liver CT image and can accurately extract the edge of the target. The experiments also provide desirable results.