Reviewerly/exHarmony huggingface dataset:
Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our approach generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweights irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare our approach with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.
The development of Android-based smartphone learning application on teaching reading comprehension

This research aims to determine the design of smartphone learning application-android based to the 10th grade of Senior High Schools in Solo, Central Java Indonesia for teaching reading comprehension. In this research, the researcher used ADDIE (Analysis-Design-Develop-Implement-Evaluate) as the instructional design. In this study, there were four experts who validated the android –based learning application. They were, two material experts and two digital learning media experts. The result showed that smartphone learning application-android based is feasible to use. This takes into consideration the assessment of the expert team, both material and media that provide an assessment in the excellent category. Thus, it can be concluded that the developed media is feasible for production and tested in the next stage.
An Anti-Drift Background-Aware Correlation Filter for Visual Tracking in Complex Scenes

The model drift problem is an inevitable problem for online visual tracking. Model drift can amplify the false tracking result over time and lead to failed tracking finally. Background-aware correlation filter (BACF) tracker obtains the training samples from negative background patches instead of shifted foreground patches and can mitigate the drifting to some extent. However, in complex scenes, such as occlusion, deformation, the inappropriate update of BACF may lead to model drift. We propose an anti-drift background-aware correlation filter via introducing the temporal consistency constraint into BACF. That is, we combine the excellent ability of BACF to distinguish the foreground and background with the ability of the temporal consistency constraint to stabilize model changes, and improve anti-drift performance of BACF. To improve computation efficient, we offer a fast algorithm via alternative Direction Method of Multipliers (ADMM) in the frequency domain. Besides, we design a simple yet effective adaptive feature channel selection method, which can further improve the success rate and precision of the tracker in complex scenes. Our proposed tracker with hand-crafted features achieves a gain of 2.2%, 3.3%, and 5.3% in AUC success rate on OTB-2013, OTB-2015, and Temple Color-128 dataset, respectively. Our proposed tracker with deep features achieves AUC success rate of 69.1% on OTB-2013 dataset. Besides, our proposed tracker with hand-crafted features achieves a gain of 3.94% in EAO on VOT-2018 dataset. Moreover, the proposed tracker performs favorably against several representative state-of-the-art methods regarding precision and success rate.
Structure and Implementation Principles of a Photonic Computer

A structure and implementation principles of a photonic computer are proposed. Its operation is based on the effects of interaction between coherent light wave systems generated by a laser source. The performance of photonic computers, their power consumption, and physical dimensions are estimated. These estimates indicate a great potential advantage of photonic computers over electronic ones. In particular, analysis carried out in this study and the obtained estimates demonstrate that a photonic computer working on the light with a wavelength of 1530 can operate 104 – 105 times faster than up-to-date electronic computers with the same power consumption. Light waves of different wavelengths do not interact with each other. With a proper OLG implementation, several computations represented by light waves of different wavelengths can be run on a single photonic computer.
Automatic Measurement System of Section Geometry Parameters for Composite Filter Rods Based on Machine Vision

This article aims to develop a reliable automatic measurement system to measure the geometry parameters of the cross section of cigarette filter rods. To achieve this purpose, an automatic measurement system based on machine vision is constructed. This measurement procedure is divided into three stages: initial stage, cutting stage, and measurement stage. In the measurement stage, a robust and accurate measurement approach is proposed to measure the depth, number of grooves, and area ratio of grooves to the section for different kinds of cigarette filter rods. First, a dynamic location algorithm is proposed to solve the position error of the mechanical system based on template matching. Subsequently, considering the uncertainty of the shape of the grooves, the iterative groove identification based on convex hull (IGICH) algorithm is constructed to detect correctly the grooves using three features: the area, width, and distance of groove distribution for the connected regions. Next, we have presented the section geometry parameter measurement method according to quality control requirements. Moreover, the system calibration algorithm is developed to improve the system accuracy based on the telecentric lens principle. Finally, based on the experimental setup, detailed characterizations including efficiency, precision, and robust analysis were performed. The results show that the proposed system can adapt to the automatic, precise, and robust measurement of geometry parameters for the section of different kinds of cigarette filter rods, and satisfy the measurement accuracy and speed required by quality control.
Polynomial rewritings from expressive Description Logics with closed predicates to variants of Datalog

In many scenarios, complete and incomplete information coexist. For this reason, the knowledge representation and database communities have long shown interest in simultaneously supporting the closed- and the open-world views when reasoning about logic theories. Here we consider the setting of querying possibly incomplete data using logic theories, formalized as the evaluation of an ontology-mediated query (OMQ) that pairs a query with a theory, sometimes called an ontology, expressing background knowledge. This can be further enriched by specifying a set of closed predicates from the theory that are to be interpreted under the closed-world assumption, while the rest are interpreted with the open-world view. In this way we can retrieve more precise answers to queries by leveraging the partial completeness of the data. The central goal of this paper is to understand the relative expressiveness of ontology-mediated query languages in which the ontology part is written in the expressive Description Logic (DL) ALCHOI and includes a set of closed predicates. We consider a restricted class of conjunctive queries. Our main result is to show that every query in this non-monotonic query language can be translated in polynomial time into Datalog with negation as failure under the stable model semantics. To overcome the challenge that Datalog has no direct means to express the existential quantification present in ALCHOI, we define a two-player game that characterizes the satisfaction of the ontology, and design a Datalog query that can decide the existence of a winning strategy for the game. If there are no closed predicates—in the case of querying an ALCHOI knowledge base—our translation yields a positive disjunctive Datalog program of polynomial size. To the best of our knowledge, unlike previous translations for related fragments with expressive (non-Horn) DLs, these are the first polynomial time translations.
Pruned Graph Scattering Transforms

Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks. However, theoretical analysis of such approaches is still at its infancy. Graph scattering transforms (GSTs) are non-trainable deep GCN models that are amenable to generalization and stability analyses. The present work addresses some limitations of GSTs by introducing a novel so-termed pruned (p)GST approach. The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs. It is further established that pGSTs are stable to perturbations of the input graph signals with bounded energy. Experiments showcase that i) pGST performs comparably to the baseline GST that uses all scattering features, while achieving significant computational savings; ii) pGST achieves comparable performance to state-of-the-art GCNs; and iii) Graph data from various domains lead to different scattering patterns, suggesting domain-adaptive pGST network architectures.
Reputation Protocol for the Internet of Trust

InternetInternet -based platform businesses outcompete traditional brick and mortar business on nearly all measures of comparison. Similarly, distributed ledger technology (DLT)Distributed Ledger Technology (DLT) businesses depend on the InternetInternet and show a lot of promise in multiple business verticals. Yet, InternetInternet -based platform businesses and DLTDistributed Ledger Technology (DLT) businesses have not reached their full potential. Multiple studies, dataData , and anecdotal evidence suggest that one core factor that undermines their evolution is the worldwide decreasing trustTrust in the InternetInternet and under-developed trustTrust in decentralized technology solutions. Semada and the Semada Research Institute (SRI)Semada Research Institute (SRI) believe that a decentralized reputationReputation network can reverse that trend to increase trustTrust in the InternetInternet and increase decentralized technology adoption. We call this solution the Semada InternetInternet of TrustTrust —a network that uniquely captures real world information, context, and value in cryptographic transactions generating transparently validated consensus on truth. Through the creation of unconscious and conscious trustTrust in decentralized networks adoption becomes a desirable outcome and increases. Semada is a unique blockchainBlockchain platform well suited for the decentralized web. Semada’s core architecture continually resolves the blockchainBlockchain trilemma by combining: (i) decentralization; (ii) scale, and; (iii) security. Semada’s version of proof-of-stake—a.k.a. the Anchor ProtocolProtocol —is a unique consensus algorithmAlgorithm that enables block propagation through reputationReputation staking and verification. Through its Sockpuppet ProtocolProtocol , Semada is resilient to deceptive fake InternetInternet identities, so-called sockpuppet accounts, and 51% attacks. Through its Persona ProtocolProtocol , Semada facilitates digital sovereignty, self-sovereign identity, privacyPrivacy , security and decentralized reputationReputation . TrustTrust enabling reputationReputation can only be earned over time on the Semada network. The Semada InternetInternet of TrustTrust creates a framework of trustTrust through reputationReputation and incentive optimization that enables market conditions for unprecedented business models and market capitalization while reducing adverse selection and moral hazards for transacting parties. The network will provide information symmetry while reducing transaction costs for market participants. Businesses will leverage the platform to achieve efficiencies across verticals.
Interference Suppression Based Gesture Recognition Method with FMCW Radar

In this paper, we propose a gesture recognition method based on interference suppression with Frequency Modulation Continuous Wave (FMCW)radar. We analyze the sampling point and the time needed for snapshot of radar signal, and estimate the range and doppler parameter of gesture. Afterward, we design a complete background and target interference suppression method for the characteristics of radar signal interference, then use Time Sequential Inflated 3 Dimensions(TS- I3D)network for gesture feature extraction and classification. The experimental results show that the suppression method has effectively solved the problems caused by non-gesture interference, and the average accuracy rate of gesture recognition is 96.67 %.
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations

We propose precision gating (PG), an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks. PG computes most features in a low precision and only a small proportion of important features in a higher precision to preserve accuracy. The proposed approach is applicable to a variety of DNN architectures and significantly reduces the computational cost of DNN execution with almost no accuracy loss. Our experiments indicate that PG achieves excellent results on CNNs, including statically compressed mobile-friendly networks such as ShuffleNet. Compared to the state-of-the-art prediction-based quantization schemes, PG achieves the same or higher accuracy with 2.4$\times$ less compute on ImageNet. PG furthermore applies to RNNs. Compared to 8-bit uniform quantization, PG obtains a 1.2% improvement in perplexity per word with 2.7$\times$ computational cost reduction on LSTM on the Penn Tree Bank dataset. Code is available at: https://github.com/cornell-zhang/dnn-gating
Using Android Media for Chemistry Learning Construction of Motivation and Metacognition Ability

The 21st century and industry revolution 4.0 has been technology used part of life.This research intended to understand the effect of android application for chemistry learning (reaction rate material) to construction of motivation and metacognition of high school students.Research aimed to determine the worthiness and effectiveness of chemistry learning using android with Research and Development (R&D) method of 4D by Thiagarajan and experiment method.The instruments used in this research consist of experts' validation sheets, chemistry teachers sheets, peer reviewer sheets and students response sheets, and also test with metacognition indicators.The data were analyzed by using descriptive quantitative method with empirical analysis and MANOVA, involving two experts on topic material and media aspects, chemistry teachers and peer reviewer: the results show that on worthy grade.The result on the questions test metacognition ability and motivation sheets of trial analysis test was fit and valid questions.Therefore, the response sheets the students using android media for chemistry learning had a great influence on transfer chemistry material content of high school students.The application android was significantly effective for students metacognition ability on effect-size interpreted as large effect, but in students study motivation was not significantly effective on effect size as small effect.
Modular Multimodal Architecture for Document Classification

Page classification is a crucial component to any document analysis system, allowing for complex branching control flows for different components of a given document. Utilizing both the visual and textual content of a page, the proposed method exceeds the current state-of-the-art performance on the RVL-CDIP benchmark at 93.03% test accuracy.
Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable to reliably sort large numbers of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.
A Tabu Search Approach With Dynamical Neighborhood Size for Solving the Maximum Min-Sum Dispersion Problem

The maximum min-sum dispersion problem (Max-Minsum DP for short) is a representative binary optimization problem that is proved to be NP-hard and has a number of real-world or potential applications.In this paper, to solve efficiently this computationally challenging problem, we propose a tabu search algorithm with a dynamical neighborhood size (TSDNS) by integrating a solution-based tabu strategy, three new hash functions, and a mechanism of adjusting adaptively the size of neighborhood exploited by the algorithm.The performance of the proposed TSDNS algorithm is assessed through extensive experiments on 160 benchmark instances widely used in the literature, and the experimental results show that the proposed algorithm is very competitive compared with the state-of-the-art algorithms in the literature especially for the large scale instances, and that the best known results are improved for a number of benchmark instances.Analysis experiments show that the new hash functions used in the tabu strategy are more efficient than those from the literature for the large scale instances, and that the mechanism of controlling adaptively neighborhood size plays a key role for the high performance of proposed algorithm.
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation

Combinatorial optimization problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor in practice. The models typically consist of hard and soft constraints, or combine hard constraints with a preference function. We introduce a novel setting for learning combinatorial optimisation problems from contextual examples. These positive and negative examples show – in a particular context – whether the solutions are good enough or not. We develop our framework using the MAX-SAT formalism. We provide learnability results within the realizable and agnostic settings, as well as hassle, an implementation based on syntax-guided synthesis and showcase its promise on recovering synthetic and benchmark instances from examples.
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets

Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks. While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs remain unclear. In this paper, we aim at bridging this gap from both theoretical and empirical perspectives. First, we analyze a variant of Optimistic Stochastic Gradient (OSG) proposed in~\citep{daskalakis2017training} for solving a class of non-convex non-concave min-max problem and establish $O(ε^{-4})$ complexity for finding $ε$-first-order stationary point, in which the algorithm only requires invoking one stochastic first-order oracle while enjoying state-of-the-art iteration complexity achieved by stochastic extragradient method by~\citep{iusem2017extragradient}. Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $O\left(ε^{-\frac{2}{1-α}}\right)$, where $α$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq α\leq 1/2$. To the best of our knowledge, this is the first work for establishing adaptive complexity in non-convex non-concave min-max optimization. Empirically, our experiments show that indeed adaptive gradient algorithms outperform their non-adaptive counterparts in GAN training. Moreover, this observation can be explained by the slow growth rate of the cumulative stochastic gradient, as observed empirically.
Unsupervised Anomaly Alerting for IoT-Gateway Monitoring using Adaptive Thresholds and Half-Space Trees

Through increasingly powerful hardware, the computational resources of IoT-Gateways are growing, enabling more sophisticated data analytics, service deployments and virtualisation capabilities. However, the difficulty of ensuring high reliability of IoT-Gateways and its deployed services increases along with the complexity of digital systems. Reliable operation of such gateways and devices therefore depends on automated methods for detecting abnormal behaviour as early as possible, in order to remediate the situation before the complete failure of a system component. We propose an unsupervised anomaly detection algorithm by extending the concept of Half-Space Trees through online adaptive thresholds for real-time computation. The anomaly detector consumes multidimensional time series data from an external monitoring component and is fixed in computational complexity to support real-time operation. In order to determine valuable hyperparameters, we propose a data stream and anomaly simulation system, providing options for pattern induced load simulations and different types of anomalies. Further, we evaluate the anomaly detection method on monitoring data originating from a deployment of the open source software Project Clearwater, a lightweight implementation of the IP-Multimedia Subsystem capable of running on VMs with limited resources. Results show the applicability of Half-Space Tree based anomaly detection with high detection rates (99.4%) and low number of false alarms (<; 3%).
Overview of CCKS 2018 Task 1: Named Entity Recognition in Chinese Electronic Medical Records

The CCKS 2018 presented a named entity recognition (NER) task focusing on Chinese electronic medical records (EMR). The Knowledge Engineering Group of Tsinghua University and Yidu Cloud Beijing Technology Co., Ltd. provided an annotated dataset for this task, which is the only publicly available dataset in the field of Chinese EMR. Using this dataset, 69 systems were developed for the task. The performance of the systems showed that the traditional CRF and Bi-LSTM model were the most popular models for the task. The system achieved the highest performance by combining CRF or Bi-LSTM model with complex feature engineering, indicating that feature engineering is still indispensable. These results also showed that the performance of the task could be augmented with rule-based systems to determine clinical named entities.