Reviewerly/exHarmony huggingface dataset:
TSR-TVD: Temporal Super-Resolution for Time-Varying Data Analysis and Visualization

We present TSR-TVD, a novel deep learning framework that generates temporal super-resolution (TSR) of time-varying data (TVD) using adversarial learning. TSR-TVD is the first work that applies the recurrent generative network (RGN), a combination of the recurrent neural network (RNN) and generative adversarial network (GAN), to generate temporal high-resolution volume sequences from low-resolution ones. The design of TSR-TVD includes a generator and a discriminator. The generator takes a pair of volumes as input and outputs the synthesized intermediate volume sequence through forward and backward predictions. The discriminator takes the synthesized intermediate volumes as input and produces a score indicating the realness of the volumes. Our method handles multivariate data as well where the trained network from one variable is applied to generate TSR for another variable. To demonstrate the effectiveness of TSR-TVD, we show quantitative and qualitative results with several time-varying multivariate data sets and compare our method against standard linear interpolation and solutions solely based on RNN or CNN.
Convergent Policy Optimization for Safe Reinforcement Learning

We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both the objective and the constraint being nonconvex functions. For such a problem, we construct a sequence of surrogate convex constrained optimization problems by replacing the nonconvex functions locally with convex quadratic functions obtained from policy gradient estimators. We prove that the solutions to these surrogate problems converge to a stationary point of the original nonconvex problem. Furthermore, to extend our theoretical results, we apply our algorithm to examples of optimal control and multi-agent reinforcement learning with safety constraints.
Symptom-based patient stratification in mental illness using clinical notes

Mental illnesses are highly heterogeneous with diagnoses based on symptoms that are generally qualitative, subjective, and documented in free text clinical notes rather than as structured data. Moreover, there exists significant variation in symptoms within diagnostic categories as well as substantial overlap in symptoms between diagnostic categories. These factors pose extra challenges for phenotyping patients with mental illness, a task that has proven challenging even for seemingly well characterized diseases. The ability to identify more homogeneous patient groups could both increase our ability to apply a precision medicine approach to psychiatric disorders and enable elucidation of underlying biological mechanism of pathology. We describe a novel approach to deep phenotyping in mental illness in which contextual term extraction is used to identify constellations of symptoms in a cohort of patients diagnosed with schizophrenia and related disorders. We applied topic modeling and dimensionality reduction to identify similar groups of patients and evaluate the resulting clusters through visualization and interrogation of clinically interpretable weighted features. Our findings show that patients diagnosed with schizophrenia may be meaningfully stratified using symptom-based clustering.
Introduction to Microsoft Bot, RASA, and Google Dialogflow

In the previous chapter, we discussed how to build an in-house chatbot framework with natural language and conversation capabilities. Building a solution from scratch has advantages that we discussed previously. However, there are use cases and scope where it could be easier, quicker, and cheaper to use readily available online intent classification and conversation management frameworks to build your chatbot client.
Embedding topological features into convolutional neural network salient object detection

Salient object detection can be applied as a critical preprocessing step in many computer vision tasks. Recent studies of salient object detection mainly employed convolutional neural networks (CNNs) for mining high-level semantic properties. However, the existing methods can still be improved to find precise semantic information in different scenarios. In particular, in the two main methods employed for salient object detection, the patchwise detection models might ignore spatial structures among regions and the fully convolution-based models mainly consider semantic features in a global manner. In this paper, we proposed a salient object detection framework by embedding topological features into a deep neural network for extracting semantics. We segment the input image and compute weight for each region with low-level features. The weighted segmentation result is called a topological map and it provides an additional channel for the CNN to emphasize the structural integrity and locality during the extraction of semantic features. We also utilize the topological map for saliency refinement based on a conditional random field at the end of our model. Experimental results on six benchmark datasets demonstrated that our proposed framework achieves competitive performance compared to other state-of-the-art methods.
BB-KBQA: BERT-Based Knowledge Base Question Answering

Knowledge base question answering aims to answer natural language questions by querying external knowledge base, which has been widely applied to many real-world systems. Most existing methods are template-based or training BiLSTMs or CNNs on the task-specific dataset. However, the hand-crafted templates are time-consuming to design as well as highly formalist without generalization ability. At the same time, BiLSTMs and CNNs require large-scale training data which is unpractical in most cases. To solve these problems, we utilize the prevailing pre-trained BERT model which leverages prior linguistic knowledge to obtain deep contextualized representations. Experimental results demonstrate that our model can achieve the state-of-the-art performance on the NLPCC- ICCPOL 2016 KBQA dataset, with an 84.12% averaged F1 score(1.65% absolute improvement).
Estimating Service Quality in Industrial Internet-of-Things Monitoring Applications With Blockchain

Internet of Things (IoT) plays a big role in automating information generation and consumption in industrial monitoring applications. Blockchain can allow this information to be stored in a manner that is both accessible and reliable for the IoT devices to work with. Blockchain has the capability to collect data from IoT devices and store it in a distributed manner that prevents tampering with the data. This paper discusses the use of blockchain to calculate the Service Quality (SQ) in an Industrial IoT for monitoring application. The proposed framework looks at the blockchain as a finite number of fragmented pieces of data corresponding to a specific industrial process. The SQ is expressed as penalties which is the difference between the expected IoT sensor values and the actual sensor data in reported events from the IoT devices. It also moderates the penalty between similar industrial processes based on each other. The moderation allows better understanding of the system functions and identification of specific problems rather than simply recording the sensor data for a single process. Furthermore, this paper analyzes private blockchains for suitability in IIoT and summarizes some key challenges for IoT to be used with blockchain in context of the proposed framework. The paper uses supply chain as a use case scenario for describing the proposed framework and presents results on its technical feasibility.
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint

This paper investigates the generalization properties of two-layer neural networks in high-dimensions, i.e. when the number of samples $n$, features $d$, and neurons $h$ tend to infinity at the same rate. Specifically, we derive the exact population risk of the unregularized least squares regression problem with two-layer neural networks when either the first or the second layer is trained using a gradient flow under different initialization setups. When only the second layer coefficients are optimized, we recover the double descent phenomenon: a cusp in the population risk appears at $h\approx n$ and further overparameterization decreases the risk. In contrast, when the first layer weights are optimized, we highlight how different scales of initialization lead to different inductive bias, and show that the resulting risk is \textit{independent} of overparameterization. Our theoretical and experimental results suggest that previously studied model setups that provably give rise to double descent might not translate to two-layer neural networks.
Current opinion, status and future development of digital pathology in Switzerland

Aims The transition from analogue to digital pathology (DP) is underway in Switzerland. To assess relevant experiences of pathologists with DP and gauge their outlook towards a digital future, a national survey was conducted by the Swiss Digital Pathology Consortium. Similar surveys were conducted in other countries, enabling a meta-analysis of DP experiences. Methods Pathologists and residents were asked to complete a survey containing 12 questions. Results were compared with similar studies conducted in the United Kingdom, Sweden, Canada, and India. Results The estimated response rate among practicing pathologists and trainees nationwide was 39.5%. Of these, 89% have experience with digital slides, mainly for education (61%) and primary diagnostics (20%). Further, 32% have worked with an image analysis programme and 26% use computer-based algorithms weekly. Interestingly, 66% would feel comfortable making a primary diagnosis digitally, while 10% would not. Most respondents believe more standards and regulations are necessary for the clinical employment of DP. Noted advantages include ease of access to slides and the resulting connectivity benefits, namely collaboration with experts across disciplines, off-site work, training purposes, and computational image analysis. Perceived disadvantages include implementation costs and issues associated with IT infrastructure and file formats. Conclusion The survey results suggest that experiences and perspectives of Swiss pathologists concerning DP is comparable to that of the other reporting countries undergoing transitions to digital workflows. Although more standards and regulations are needed to ensure the safe usage of these technologies, pathologists in Switzerland appear welcoming of this new digital era.
Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach

Nowadays many companies and institutions are interested in learning what do people think and want. Many studies are conducted to answer these questions. That’s why, emotions of people are significant in terms of instructional design. However, processing and analysis of many people's ideas and emotions is a challenging task. That is where the 'sentiment analysis' through machine learning techniques steps in. Recently a fast digitalization process is witnessed. Anadolu university, that serves 1 million distant students, is trying to find its place in this digital era. A learning management system (LMS) that distant students of the Open Education Faculty (Açıköğretim Fakültesi) is developed at the Anadolu University. Interaction with students is the clear advantage of LMS's when compared to the hard copy materials. Book, audio book (mp3), video and interactive tests are examples of these materials. 6059 feedbacks for those online materials was scaled using the triple Likert method and using machine learning techniques sentiment analysis was performed in this study. 0.775 correctness ratio was achieved via the Logistic regression algorithm. The research concludes that machine learning techniques can be used to better understand learners and how they feel.
Edge AI and Blockchain for Privacy-Critical and Data-Sensitive Applications

The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.
Adaptive sampling using self-paced learning for imbalanced cancer data pre-diagnosis

The early diagnosis of cancer diseases is an indispensable part in the cancer research. It urges people to develop many new machine learning approaches to assist the diseases identification based on the gene expression data. However, the race occurrence of malignant tumors creates a challenge due to the potential over-fitting risk in the current model training. Typically, people use various sampling methods (e.g., random oversampling and undersampling) to address this challenge to provide a balanced data distribution. However, these methods might discard potentially useful samples. In this paper, we proposed an imbalanced sampling approach via self-paced learning (ISPL) to effectively select high-quality samples to improve the robustness. The experimental results showed that our proposed ISPL method increased the classification accuracy by approximately 16% compared with the average performance obtained by other sampling methods. In addition, the new method successfully selected some important genes for further investigation.
Threshold Schemes from Isogeny Assumptions

We initiate the study of threshold schemes based on the Hard Homogeneous Spaces (HHS) framework of Couveignes. Quantum-resistant HHS based on supersingular isogeny graphs have recently become usable thanks to the record class group precomputation performed for the signature scheme CSI-FiSh. Using the HHS equivalent of the technique of Shamir’s secret sharing in the exponents, we adapt isogeny based schemes to the threshold setting. In particular we present threshold versions of the CSIDH public key encryption, and the CSI-FiSh signature schemes. The main highlight is a threshold version of CSI-FiSh which runs almost as fast as the original scheme, for message sizes as low as 1880 B, public key sizes as low as 128 B, and thresholds up to 56; other speed-size-threshold compromises are possible.
Few-Shot Text Style Transfer via Deep Feature Similarity

Generating text to have a consistent style with only a few observed highly-stylized text samples is a difficult task for image processing. The text style involving the typography, i.e., font, stroke, color, decoration, effects, etc., should be considered for transfer. In this paper, we propose a novel approach to stylize target text by decoding weighted deep features from only a few referenced samples. The deep features, including content and style features of each referenced text, are extracted from a Convolutional Neural Network (CNN) that is optimized for character recognition. Then, we calculate the similarity scores of the target text and the referenced samples by measuring the distance along the corresponding channels from the content features of the CNN when considering only the content, and assign them as the weights for aggregating the deep features. To enforce the stylized text to be realistic, a discriminative network with adversarial loss is employed. We demonstrate the effectiveness of our network by conducting experiments on three different datasets which have various styles, fonts, languages, etc. Additionally, the coefficients for character style transfer, including the character content, the effect of similarity matrix, the number of referenced characters, the similarity between characters, and performance evaluation by a new protocol are analyzed for better understanding our proposed framework.
XCS classifier system with experience replay

XCS constitutes the most deeply investigated classifier system today. It offers strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and regression tasks, XCS also proved very effective in certain multi-step environments from the domain of reinforcement learning. Especially in the latter domain, recent advances have been mainly driven by algorithms which model their policies based on deep neural networks, among which the Deep-Q-Network (DQN) being a prominent representative. Experience Replay (ER) constitutes one of the crucial factors for the DQN's successes, since it facilitates stabilized training of the neural network-based Q-function approximators. Surprisingly, XCS barely takes advantage of similar mechanisms that leverage remembered raw experiences. To bridge this gap, this paper investigates the benefits of extending XCS with ER. We demonstrate that for single-step tasks ER yields strong improvements in terms of sample efficiency. On the downside, however, we reveal that ER might further aggravate well-studied issues not yet solved for XCS when applied to sequential decision problems demanding for long-action-chains.
An asynchronously deep reservoir computing for predicting chaotic time series

Chaotic time series prediction is a research topic in both theoretical and real-life area. Its aim is to predict the future of the time series based on past observations. Reservoir computing (RC) is a promising tool widely used in time series prediction. Short-term memory (STM) is very important to model time-dependent time series by the RC approach. However, traditional RC hardly achieves sufficient STM capacity required by a complicated time series prediction task. For this reason, this paper proposes an asynchronous deep RC (ADRC), which is composed of a number of sub-reservoirs that are connected one by one in sequence. Moreover, delayed modules are inserted between every two adjacent sub-reservoirs. The sub-reservoirs in the proposed ADRC preserve the input characteristics by a relay mode and deal with them asynchronously. This makes the reservoir achieve large STM capacity and rich dynamics. The experimental results demonstrate that the proposed ADRC is prominent in modeling chaotic time series signals with high performance.
How to Profit from Payments Channels

Payment channel networks like Bitcoin’s Lightning network are an auspicious approach for realizing high transaction throughput and almost-instant confirmations in blockchain networks. However, the ability to successfully conduct payments in such networks relies on the willingness of participants to lock collateral in the network. In Lightning, the key financial incentive to lock collateral are low fees for routing payments of other participants. While users can choose these fees, real-world data indicates that they mainly stick to default fees. By providing insights on beneficial choices for fees, we aim to incentivize users to lock more collateral and improve the effectiveness of the network. In this paper, we consider a node $$\mathbf {A}$$ that given the network topology and the channel details establishes channels and chooses fees to maximize its financial gain. Our contributions are i) formalization of the optimization problem, ii) proving that the problem is NP-hard, and iii) designing and evaluating a greedy algorithm to approximate the optimal solution. In each step, our greedy algorithm establishes a channel that maximizes the increase to $$\mathbf {A}$$ ’s total reward, which corresponds to maximizing the number of shortest paths passing through $$\mathbf {A}$$ . Our simulation study leveraged real-world data sets to quantify the impact of our gain optimization and indicates that our strategy is at least a factor two better than other strategies.
TKE: Mining Top-K Frequent Episodes

Frequent episode mining is a popular data mining task for analyzing a sequence of events. It consists of identifying all subsequences of events that appear at least minsup times. Though traditional episode mining algorithms have many applications, a major problem is that setting the minsup parameter is not intuitive. If set too low, algorithms can have long execution times and find too many episodes, while if set too high, algorithms may find few patterns, and hence miss important information. Choosing minsup to find enough but not too many episodes is typically done by trial and error, which is time-consuming. As a solution, this paper redefines the task of frequent episode mining as top-k frequent episode mining, where the user can directly set the number of episodes k to be found. A fast algorithm named TKE is presented to find the top-k episodes in an event sequence. Experiments on benchmark datasets shows that TKE performs well and that it is a valuable alternative to traditional frequent episode mining algorithms.
Pengembangan Media Pembelajaran Berbasis Android pada Masa Pandemi Covid-19 untuk Meningkatkan Literasi Sains Peserta Didik

This study aims to develop a feasible Android-based learning media to increase students' scientific literacy. This research uses the Four D model procedure. In addition to developing android-based media, this research also develops syllabus, lesson plans, and scientific literacy instruments. This research was conducted at one of the junior high schools in the city of Mataram. Validation of Android-based learning media was carried out by three validators of media experts and material experts. Expert validation data analysis was performed using the Pearson validation formula. The results of the feasibility study for Android-based learning products showed the validity of 84% with very valid criteria. The results of the media feasibility study obtained an average score of 88%, the syllabus obtained an average score of 83%, lesson plans obtained an average score of 82%, and the scientific literacy instrument obtained an average score of 83%. In conclusion, Android-based media products on natural science material can be implemented in learning, especially during the Covid-19 pandemic.
Initialization of quantum simulators by sympathetic cooling

We demonstrate the preparation of ground states of largely arbitrary Hamiltonians by sympathetic cooling with a single particle.
Pixelor

We present the first competitive drawing agent Pixelor that exhibits humanlevel performance at a Pictionary-like sketching game, where the participant whose sketch is recognized first is a winner. Our AI agent can autonomously sketch a given visual concept, and achieve a recognizable rendition as quickly or faster than a human competitor. The key to victory for the agent's goal is to learn the optimal stroke sequencing strategies that generate the most recognizable and distinguishable strokes first. Training Pixelor is done in two steps. First, we infer the stroke order that maximizes early recognizability of human training sketches. Second, this order is used to supervise the training of a sequence-to-sequence stroke generator. Our key technical contributions are a tractable search of the exponential space of orderings using neural sorting; and an improved Seq2Seq Wasserstein (S2S-WAE) generator that uses an optimal-transport loss to accommodate the multi-modal nature of the optimal stroke distribution. Our analysis shows that Pixelor is better than the human players of the Quick, Draw! game, under both AI and human judging of early recognition. To analyze the impact of human competitors' strategies, we conducted a further human study with participants being given unlimited thinking time and training in early recognizability by feedback from an AI judge. The study shows that humans do gradually improve their strategies with training, but overall Pixelor still matches human performance. The code and the dataset are available at http://sketchx.ai/pixelor.