Reviewerly/exHarmony huggingface dataset:
Cooperative Multi-Agent Reinforcement Learning with Hierarchical Relation Graph under Partial Observability

Cooperation among agents with partial observation is an important task in multi-agent reinforcement learning (MARL), aiming to maximize a common reward. Most existing cooperative MARL approaches focus on building different model frameworks, such as centralized, decentralized, and centralized training with decentralized execution. These methods employ partial observation of agents as input directly, but rarely consider the local relationship between agents. The local relationship can help agents integrate observation information among different agents in a local range, and then adopt a more effective cooperation policy. In this paper, we propose a MARL method based on spatial relationship called hierarchical relation graph soft actor-critic (HRG-SAC). The method first uses a hierarchical relation graph generation module to represent the spatial relationship between agents in local space. Second, it integrates feature information of the relation graph through the graph convolution network (GCN). Finally, the soft actor-critic (SAC) is used to optimize agents' actions in training for compliance control. We conduct experiments on the Food Collector task and compare HRG-SAC with three baseline methods. The results demonstrate that the hierarchical relation graph can significantly improve MARL performance in the cooperative task.
Vietnamese Antonyms Detection Based on Specialized Word Embeddings using Semantic Knowledge and Distributional Information

Antonymy is one of the fundamental relations shaping the organization of the semantic lexicon. Therefore, automatic detection of antonymy can be leveraged to make contributions to different NLP tasks, such as Machine Translation, Sentiment Analysis, and Information Retrieval. Currently, most prior studies just focus on discriminating between antonyms and synonyms. However, not only synonymy but other semantic relations, such as hypernymy, co-hyponyms, which also get high similarities thereby making it hard to discriminate. Therefore, it is necessary to make a thorough research on identifying antonyms from a wide variety of other semantic relations. In this paper, we aim to identify Vietnamese antonyms pairs according to the vector semantics approach. Specifically, we build up specialized word embedding models by incorporating lexical-semantic resource and distributional information. In addition, we propose specialized Vietnamese features and utilize mutual information between words in order to integrate with word embedding vectors. This aims to generate more meaningful feature vectors for supervised classifiers solving antonym detection problems. Furthermore, we construct three reliable Vietnamese testing datasets consisting of AntSynlOOO, AntHyplOOO, and AntMixlOOO, for this task. Experimental results conducted on the datasets demonstrated that our model performs effectively.
Variable batch size across layers for efficient prediction on CNNs

CNNs are used extensively for computer vision tasks like activity recognition, image classification, segmentation etc. The large compute memory required in these applications restricts the use of high batch size during inference, thereby increasing the overall prediction time. Prior work addresses this issue through various model compression mechanisms like weight/filter pruning, quantizing the parameters/intermediate outputs, etc. We propose a complementary technique where we improve inference time by using variable batch sizes (VBS) across the layers of a CNN. This optimises the memory-time trade-off for each layer and leads to better network throughput. Our approach does not make any modifications to the existing network (unlike pruning or quantization techniques) and thus there is no impact on the model accuracy. We develop a dynamic program (DP) based algorithm that takes inference time and memory required by different layers of the network as input, and computes the optimal batch sizes for each layer depending on the available resources (RAM, storage space etc.). We demonstrate our findings in two different settings: video inference on K80 GPUs and image inference on Edge devices. On video networks like C3D, our VBS algorithm gives up to 61% higher throughput compared to a fixed batch size baseline. On image networks like GoogleNet, ResNet50 etc., we achieve up to 60% higher throughput compared to a fixed batch size baseline.
Fast Learning and Prediction of Event Sequences in a Robotic System

The most important requirements for a video surveillance system are efficiency and effectiveness. In fact, it has to be fast in detecting a potentially dangerous event in real time, but it has also not to miss any of them. However, it would be even better if a system could detect dangerous events even before they actually occur. For that reason, in this paper we propose a very fast approach for learning and predicting event sequences in a surveillance context, that can also be applied to a robotic platform for improving the whole monitoring process. Preliminary experiments confirm that the proposed approach is very promising.
Initial Study on Classification of Japanese Paper by Kozo Name using EfficientNet with Digital Camera

Nondestructive analysis of historical materials, such as paper, is vital in the elucidation of history and culture. This is because it is possible to elucidate the background of a book's origin using information obtained from other sources, such as paper making methods and paper's fiber composition. Herein, we propose a patch classification method for Japanese paper kozo using EfficientNet-B0. We integrate the results classified using patch images to estimate the origin of the kozo fiber in the entire paper. Consequently, the average success rate of our method was 93.1 % for two classifications and 87.5 % for four classifications of the production areas of kozo. In addition, the classification accuracy for the entire paper was evaluated from the patch image obtained after classification using the reconstructed image in which the position was rearranged on the original image. As a result, the classification of four production areas of kozo, from which the entire macro image was made, could be classified in all macro images, although the number of experiments was limited. In the future, we will investigate a multi-scale technique to consider the fiber continuity between patches in macro images reconstructed from patch images.
Towards Designing an Explainable-AI based Solution for Livestock Mart Industry

A model capable of explaining the different factors that impact the price point is essential for the needs of the market. It can also inspire confidence in buyers and sellers about the price point offered. To achieve these objectives, we have been working with the team at MartEye, a startup based in Portershed in Galway City, Ireland. Through this paper, we report our work-in-progress research towards building a smart video analytic platform, leveraging Explainable AI techniques.
On Calibration of Scene-Text Recognition Models

The topics of confidence and trust in modern scene-text recognition (STR) models have been rarely investigated in spite of their prevalent use within critical user-facing applications. We analyze confidence estimation for STR models and find that they tend towards overconfidence thus leading to overestimation of trust in the predicted outcome by users. To overcome this phenomenon we propose a word-level confidence calibration approach. Initially, we adapt existing single-output T-scaling calibration methodologies to suit the case of sequential decoding. Interestingly, extensive experimentation reveals that character-level calibration underperforms word-level calibration and it may even be harmful when employing conditional decoding. In addition, we propose a novel calibration metric better suited for sequential outputs as well as a variant of T-scaling specifically designed for sequential prediction. Finally, we demonstrate that our calibration approach consistently improves prediction accuracy relative to the non-calibrated baseline when employing a beam-search strategy.
Authenticating Drivers Using Automotive Batteries

Automakers have been improving, or even trying to replace, key-based driver authentication solutions, owing to their vulnerability to cyber attacks and single-point-of-failures, as well as their inability of driver identification. In line with this effort, we design a novel driver authentication system using automotive batteries, called Batteries-as-Authenticators (BAuth), to mitigate the limitations of key-based solutions by providing a second-factor authentication. BAuth is an add-on module installed between vehicles and their batteries, which uses the batteries as sensors to validate drivers' identities and actuators to enable/disable the cranking of vehicle's engine. We have prototyped and evaluated BAuth on 6 regular/hybrid/electric vehicles. Our evaluation shows BAuth to authenticate the drivers with a 98.17 (2.84)% averaged true (false) positive rates and tolerate the dynamics caused by the aging/temperature/state-of-charge of batteries. Our user study corroborates BAuth's attractiveness to car owners.
Proportional Differential Privacy (PDP): A new Approach for Differentially Private Histogram Release based on Buckets Densities

Differential privacy is an effective model for releasing the results of statistical queries on sensitive data. It can be used to release different types of data, in particular, histograms, which provide a useful summary of a dataset. Several differentially private histogram releasing schemes have recently been proposed. However, these methods impose the same amount of privacy control over all data, by using one private budgeting value. In this paper, we introduce a new approach called Proportional Differential Privacy (PDP), based on the density of buckets. The aim is to use different levels of privacy in order to publish Differential Privacy histograms having a reduced queries' error. Carried out experiments on real-life datasets highlights very encouraging results in terms of information loss decrease.
LSH-based Collaborative Recommendation Method with Privacy-Preservation

With the rapid development of cloud computing technology, massive services and online information cause information overload. Collaborative Filtering (CF) is one of the most successful and widely used technologies in personalized recommendation system to deal with information overload. However, traditional CF recommendation algorithms go through high time cost and poor real-time performance when dealing with the large-scale behavior data. Moreover, most collaborative recommendation methods mainly focus on improving recommendation accuracy, while ignore privacy preservation. In addition, the recommendation results of traditional CF recommendation algorithms are often too single, which could not meet user's diverse requirements. To solve these problems, this paper proposes a privacy-aware collaborative recommendation algorithm based on local sensitive hash (LSH) and factorization techniques. First, LSH is adopted to determine nearest neighbor set of the target users, where a neighbor matrix for the target user can be generated. The matrix factorization technique is applied in the neighbor matrix to predict the missing ratings. Then the nearest neighbors can be determined based on the predicted ratings. Finally, predictions for the target user are made based on the neighborhood-based CF recommendation model and diversified recommendations are made for the target user. Experimental results show that the proposed algorithm can effectively improve the efficiency of recommendation on the premise of protecting the privacy of users.
Using Surveys to Build-up Empirical Evidence on Test-Related Technical Debt

Background: Technical Debt (TD) refers to the problem of pending or incomplete tasks and artifacts that brings short term gain to a software project, but may have to be paid with interest later on in its life-cycle. We use the term test-related debt to refer to TD items faced by software testing practitioners. Goal: To investigate, from the point of view of software testing practitioners, TD types associated with test-related debt, the causes that lead to it, and the effects of its existence. Method: We perform a replication of a well-known TD survey with software testing practitioners and combine the obtained responses with the ones collected in the original study. In total, the study analyses, quantitatively and qualitatively, 54 responses from Brazilian testing practitioners. Results: Test-related debt items are associated with TD types such as documentation, code, and test debt itself. The survey identified 61 TD causes that are organized into eight categories. The main causes are deadline, outdated / incomplete documentation, and inappropriate planning. The survey identified 45 TD effects that are grouped into six categories. The main effects are delivery delay, rework, and low external quality. Conclusion: This paper organizes the identified causes and effects in probabilistic cause-effect diagrams, which provide a broad view of the state of the practice concerning test-related debt causes and effects.
Oracles of Bad Smells

A bad smell is an evidence of a design problem that may be harmful to the software maintenance. Several studies have been carried out to aid the identification of bad smells, by defining approaches or tools. Usually, the evaluation of these studies' results relies on data of oracles bad smells. An oracle is a set of data of bad smells found in a given software system. Such data serves as a referential template or a benchmark to evaluate the proposals on detecting bad smells. The availability and the quality of bad smell oracles are crucial to assert the quality of detection strategies of bad smells. This study aims to compile the bad smell oracles proposed in the literature. To achieve this, we conducted a Systematic Literature Review (SLR) to identify bad smell oracles and their characteristics. The main result of this study is a catalog of bad smell oracles that may be useful for research on bad smells, especially the studies that propose tools or detection strategies for bad smells.
On the Differences Between Human Agents and Logic-Based Software Agents Discourse Understanding

We are interested in the differences between how a human agent and a logic-based software agent interpret a text in natural language. When reading a narrative, the human agent has a single interpretation model. That is the preferred model among the models consistent with the available information. The model is gradually adjusted as the story proceeds. Differently, a logic-based software agent works with a finite set of many models, in the same time. Of most interest is that the number of these models is huge, even for simple narratives. We compare here the reduction strategies of humans and software agents to keep the discourse more intelligible and tractable. One the one hand, the human agent extensively uses common knowledge, contextual reasoning and closes the world as much as possible. On the other hand, the logical agent adds domain knowledge (such as ontologies) and applied reduction strategies (such as identifying isomorphisms). The differences are analyse with puzzles in First order logic, Description logic and Dynamic epistemic logic.
Anomaly Detection Using Spatio-Temporal Correlation and Information Entropy in Wireless Sensor Networks

Wireless Sensor Network (WSN) has already been deployed widely in industrial, agriculture and many other fields. Meaningful information extracted from the data collected by WSNs can assist people in making control and decisions. Anomaly detection of sensor data is critical to ensure the data quality and the reliability of WSNs. In recent years, distance-based anomaly detection methods have attracted the attention of many experts. Though these methods can achieve a good detection effect, the exploitation of the spatio-temporal correlation of sensor data is not sufficient. Therefore, we propose an anomaly detection method (STCIE) using the spatio-temporal correlation and information entropy. By exploiting the spatio-temporal relationship of sensor data, the maximum correlation and weighted variation coefficient are introduced. The maximum correlation and weighted variation coefficient are used to analyze the state of the sensor nodes, and the confidence interval method is adopted to realize the adaptive update of the threshold. To improve the detection accuracy, the abnormal sensor node is confirmed by the linear least square estimation method using information entropy (IE-LLSE). The information entropy is adopted to analyze the data fluctuation to achieve the best prediction accuracy. Experimental results showed that the false positive rate and false negative rate of the proposed method were significantly reduced, and the accuracy was stable at more than 97%.
The Best Classification Algorithm for Identification Beef Quality Based on Marbling

Indonesian populace demands animal protein high enough to fulfill nutrition, one of the most sought-after sources of protein comes from beef. Due to high market needs, some traders are defrauding to get higher profits. This has caused discomfort for most beef consumers because people generally rely on the ability of vision to find out the quality of meat. In order to make it easier for the public to recognize the quality of meat to be consumed, this study classifies the quality of meat marbling based on the size of marbling. The classification in this study used the SVM algorithm, LDA, and Decision Tree. Furthermore, the result came out with the best algorithm in this case was the Decision Tree.
Signal-Adapted Analytic Wavelet Packets in Arbitrary Dimensions

The analytic wavelet packet transform, based on the dual-tree approach, represents a complex-valued extension of the wavelet packet transform. A generalization to multiple dimensions can be realized using fully separable wavelet trees, but this restricts the possible subband combinations. To overcome these limitations, we present a flexible framework to calculate N-D analytic wavelet packets with configurable decomposition structures and filter types. By introducing a new subband notation for the nodes of the N-D wavelet binary tree, both anisotropic and isotropic decomposition structures can be realized. Based on this subband notation, a full frame in N dimensions is defined and combined with an optimal basis selection, which we generalized to arbitrary dimensions, to find signal-adapted decomposition structures. As a multi-dimensional example, the framework is applied to the compression and denoising of a 4D light field. The results are evaluated in terms of the PSNR and SSIM and compared with the discrete cosine transform.
A non-alternating graph hashing algorithm for large-scale image search

In the era of big data, methods for improving memory and computational efficiency have become crucial for the successful deployment of technology. Hashing is one of the most effective approaches to deal with the computational limitations associated with big data. One natural way to formulate this problem is spectral hashing, which directly incorporates an affinity to learning binary codes. However, owing to the binary constraints, the optimization becomes intractable. To mitigate this challenge, different relaxation approaches have been proposed to reduce the computational load required to obtain binary codes and still attain a good solution. The problem with all existing relaxation methods involves the use of one or more additional auxiliary variables to attain high-quality binary codes while relaxing the problem. The existence of auxiliary variables leads to the coordinate descent approach, which increases the computational complexity. We argue that the introduction of these variables is unnecessary. To this end, we propose a novel relaxed formulation for spectral hashing that adds no additional variables to the problem. Furthermore, instead of solving the problem in the original space where the number of variables is equal to the data points, we solve the problem in a much smaller space and retrieve the binary codes from this solution. This technique reduces both the memory and computational complexity simultaneously. We apply two optimization techniques, namely, the projected gradient and optimization on the manifold, to obtain the solution. Using comprehensive experiments on four public datasets, we show that the proposed efficient spectral hashing (ESH) algorithm achieves a highly competitive retrieval performance compared with the state-of-the-art algorithms at low complexity.
A New Method of Interval Type-2 Fuzzy-Based CNN for Image Classification

Last two decades, neural networks and fuzzy logic have been successfully implemented in intelligent systems. The fuzzy neural network system framework infers the union of fuzzy logic and neural system framework thoughts, which consolidates the advantages of fuzzy logic and neural network system framework. This FNN is applied in many scientific and engineering areas. Wherever there is an uncertainty associated with data fuzzy logic place a vital rule, and the fuzzy set can represent and handle uncertain information effectively. The main objective of the FNN system is to achieve a high level of accuracy by including the fuzzy logic in either neural network structure, activation function, or learning algorithms. In computer vision and intelligent systems such as convolutional neural network has more popular architectures, and their performance is excellent in many applications. In this article, fuzzy-based CNN image classification methods are analyzed, and also interval type-2 fuzzy-based CNN is proposed. From the experiment, it is identified that the proposed method performance is well.
Secure Multi-party Sorting Protocol Based on Distributed Oblivious Transfer

In the present paper, a secure multi-party protocol for sorting the private values, without disclosing them, is presented. We extend the Yao's millionaire problem to n parties. Secure comparison and sorting the wealth of n parties using the Yao's protocol requires running it n×(n-2)/2 times. For a wealth interval of N, the complexity the Yao's protocol execution for these n parties has an order of N × n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The Secure Multi-party Computation method is utilized to develop the protocol of secure multi-party sorting. We also employ an easy knapsack problem to distinguish the corresponding indices of different participants. Moreover, a modified version of the distributed oblivious transfer protocol is proposed to improve our proposed protocol and reduce its overall compexity. The computational and communication complexities of our protocol for secure multiparty sorting based on the distributed oblivious transfer, are both equal to n. Finally, the security of the proposed protocol against adversaries of semi-honest type is proved.