Reviewerly/exHarmony huggingface dataset:
Comprehensible counterfactual explanation on Kolmogorov-Smirnov test

The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test. In this paper, we tackle the problem of producing counterfactual explanations for test data failing the KS test. Concept-wise, we propose the notion of most comprehensible counterfactual explanations, which accommodates both the KS test data and the user domain knowledge in producing explanations. Computation-wise, we develop an efficient algorithm MOCHE (for &lt;u&gt;MO&lt;/u&gt;st &lt;u&gt;C&lt;/u&gt;ompre&lt;u&gt;H&lt;/u&gt;ensible &lt;u&gt;E&lt;/u&gt;xplanation) that avoids enumerating and checking an exponential number of subsets of the test set failing the KS test. MOCHE not only guarantees to produce the most comprehensible counterfactual explanations, but also is orders of magnitudes faster than the baselines. Experiment-wise, we present a systematic empirical study on a series of benchmark real datasets to verify the effectiveness, efficiency and scalability of most comprehensible counterfactual explanations and MOCHE.
Towards More Robust GNN Training with Graph Normalization for GraphSAINT

Graph Neural Networks (GNNs) field has a dramatically development nowadays due to the strong representation ability for data in non-Euclidean space, such as graphs. However, with the larger graph datasets and the trend of more complex algorithms, the stability problem appears during model training. For example, GraphSAINT algorithm will not converge in training with a probability range from 0.1 to 0.4. In order to solve this problem, this paper proposes an improved GraphSAINT method. Firstly, a proper graph normalization strategy is introduced into the model as a neural network layer. Secondly, the structure of the model is modified based on the normalization strategy to normalize the original input data and the input data of the middle layer. Thirdly, the training process and the inference process of the model are adjusted to fit this normalization strategy. The improved GraphSAINT method successfully eliminates the instability and improves the robustness during training. Besides, it accelerates the training procedure convergence of the GraphSAINT algorithm and reduces the training time by about a quarter. Furthermore, it also achieves an improvement in the pre-diction accuracy. The effectiveness of the improved method is verified by using the citation dataset of Open Graph Benchmark (OGB).
Average number is an insufficient metric for interferometry

We argue that analysing schemes for metrology solely in terms of the average particle number can obscure the number of particles effectively used in informative events. For a number of states we demonstrate that, in both frequentist and Bayesian frameworks, the average number of a state can essentially be decoupled from the aspects of the total number distribution associated with any metrological advantage.
Multiclass Classification Using Dilute Bandit Feedback

This paper introduces a new online learning framework for multiclass classification called learning with diluted bandit feedback. At every time step, the algorithm predicts a candidate label set instead of a single label for the observed example. It then receives a feedback from the environment whether the actual label lies in this candidate label set or not. This feedback is called “diluted bandit feedback". Learning in this setting is even more challenging than the bandit feedback setting, as there is more uncertainty in the supervision. We propose an algorithm for multiclass classification using dilute bandit feedback (MC-DBF), which uses the exploration-exploitation strategy to predict the candidate set in each trial. We show that the proposed algorithm achieves \(\mathcal {O}(T^{1-\frac{1}{m+2}})\) mistake bound if candidate label set size (in each step) is m. We demonstrate the effectiveness of the proposed approach with extensive simulations.
A Conditional Cascade Model for Relational Triple Extraction

Tagging based methods are one of the mainstream methods in relational triple extraction. However, most of them suffer from the class imbalance issue greatly. Here we propose a novel tagging based model that addresses this issue from following two aspects. First, at the model level, we propose a three-step extraction framework that can reduce the total number of samples greatly, which implicitly decreases the severity of the mentioned issue. Second, at the intra-model level, we propose a confidence threshold based cross entropy loss that can directly neglect some samples in the major classes. We evaluate the proposed model on NYT and WebNLG. Extensive experiments show that it can address the mentioned issue effectively and achieves state-of-the-art results on both datasets. The source code of our model is available at: https://github.com/neukg/ConCasRTE.
Long-Term Exploration in Persistent MDPs

Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy. Hard-exploration environments are defined by huge state space and sparse rewards. In such conditions, an exhaustive exploration of the environment is often impossible, and the successful training of an agent requires a lot of interaction steps. In this paper, we propose an exploration method called Rollback-Explore (RbExplore), which utilizes the concept of the persistent Markov decision process, in which agents during training can roll back to visited states. We test our algorithm in the hard-exploration Prince of Persia game, without rewards and domain knowledge. At all used levels of the game, our agent outperforms or shows comparable results with state-of-the-art curiosity methods with knowledge-based intrinsic motivation: ICM and RND. An implementation of RbExplore can be found at https://github.com/cds-mipt/RbExplore.
Real-time rain severity detection for autonomous driving applications

Rain produces a complex set of visual effects on an image captured by a camera sensor. Hence, the performance of any vision-based system gets adversely affected due to rain. Severity of impact is generally proportional to severity of rain. Such an effect is particularly relevant for advanced driver assistance systems (ADAS) and autonomous driving (AD) scenarios given the criticality of environment perception in such scenarios. In order to mitigate the adverse impact of such interference, detection and severity classification of rainy conditions is an essential part of the sensor support system. In this context, we present a novel rain detection and severity classification algorithm based on a neural network with inputs such as localized DCT and image-based features. The proposed approach is tested on real data set collected on the road and subjectively quantified with respect to rain severity. The method performs well in terms of both detection and classification accuracy. The proposed algorithm also achieves real-time operational capability owing to its low complexity.
Image to Image Translation Networks using Perceptual Adversarial Loss Function

Image to image translation based on deep learning models is a subject of immense importance in the disciplines of Artificial Intelligence (AI) and Computer Vision (CV). A variety of traditional tasks such as image colorization, image denoising and image inpainting, are categorized as typical paired image translation tasks. In computer vision, super-resolution regeneration is particularly important field. We proposed an improved algorithm to mitigate the issues that arises during the reconstruction using super resolution based on generative adversarial network. It is difficult to train in reconstruction of results. The generated images and the corresponding ground-truth images should share the same fundamental structure in order to output the required resultant images. The shared basic structure between the input and the corresponding output image is not as optimal as assumed for paired image translation tasks, which can greatly impact the generating model performance. The traditional GAN based model used in image-to-image translation tasks used a pre-trained classification network. The pre-trained networks perform well on the classification tasks compared to image translation tasks because they were trained on features that contribute to better classification. We proposed the perceptual loss based efficient net Generative Adversarial Network (PL-E-GAN) for super resolution tasks. Unlike other state of the art image translation models, the PL-E-GAN offers a generic architecture for image super-resolution tasks. PL-E-GAN is constituted of two convolutional neural networks (CNNs) that are the Generative network and Discriminator network Gn and Dn, respectively. PL-E-GAN employed both the generative adversarial loss and perceptual adversarial loss as objective function to the network. The integration of these loss function undergoes an adversarial training and both the networks Gn and Dn trains alternatively. The feasibility and benefits of the PL-E-GAN over several image translation models are shown in studies and tested on many image-to-image translation tasks
Research on Face Detection Methods

In today's society, face recognition [1] has become a main method of identity recognition. Face recognition is easy to operate and fast to detect, it is widely used in various identification occasions and plays a huge role because of many advantages of face recognition.
A Cooperative Learning Method for Multi-Agent System with Different Input Resolutions

Multi-Agent Reinforcement Learning controls some agents to learn group action with cooperation each other. For example, AGVs in warehouse as the agents cooperate with others and put on and off the supplies to organize them. Though Multi-Agent Reinforcement Learning seems to make advantage to apply multi-robot and more domains, this method has some problems, in particular, it cannot consider the sensor resolution in real world problem. This paper addresses this problem as hetero informational problem, and discuss how to solve the problem by the topology and learning of the neural network of the deep reinforcement learning. Concretely, This paper employed Asynchronous Advantageous Actor-Critic (A3C) with some kinds of neural networks to discuss through two experimental cases, single and multi agent domains. This paper compared performance of agents with different number of hidden layers of neural networks in the single agent domain, and investigate the performance on the environment whose agents have different resolution each other in the multi-agent domain.
Potentiality of E-recruitment System for Schools and Hospitals in Moshi Catholic Diocese

The adaptation of e-recruitment is a very complex phenomena as its tends to address numerous organization obstacles and overcoming operative cost. In this digital era, many organizations' human resources prefer to use a set of digitalized appliances to identify new employees and examine their credentials, certifications, and characteristics. This study was conducted to examine the utilization of e-recruitment system in Moshi Catholic Diocese. The study was guided by three specific research objectives stated as: Firstly, to find out the extent to which e-recruitment system has been successful in MCD. Secondly, to eximine the benefits of e-recruitment system in MCD. Lastly, to asses the challengers of utilizing e-recruitment system in MCD. Additionally, the study useda cross-sectional design where by70 participants were sampled through stratified, purposive, and simple random sampling employed to sample Catholic Diocesan institutions. Furthermore, a questionnaire and structured interview used to collect data from the respondents, and the validity and reliability of the instrument was considered. Data collected analyzed through descriptive (mean, standard deviation, and percentages) and inferential statistics (chi-square test). The findings revealed that the Catholic Diocese of Moshi is using e-recruitment system in the whole process of hiring employees in their respective Schools and Hospitals higher than traditional approaches. Moreover, the method brought huge advantages to their institutions as most of the employees hired were found to be extremely reliable for the work assigned and effectively in the task implementation.
A Probabilistic Precision Information Retrieval Model for Personalized Clinical Trial Recommendation based on Heterogeneous Data

In modern healthcare practices, diagnosis and treatment for certain complex illnesses require specific information on the patients' background, genealogy, heredity, demographic data etc. Even with a similar diagnosis, treatments may need to designed specifically to adapt well to the patients' genetic, cultural, and lifestyle aspects. Precision medicine mainly deals with enabling personalized care based on a given patient's conditions in a scientifically rigorous way. Because this entails recommending personalized therapies to patients and has the potential to affect the health of other people, the performance of a designed system must be accurate and exact. In this paper, a precision information retrieval system is proposed that leverages structured and unstructured data to retrieve relevant knowledge for enabling personalized recommendations. The proposed pipeline is validated with the clinical trial dataset of the Precision medicine track of TREC 2017. A set of relevant ranked clinical trials for a given condition/disease that could not be cured using any of the traditional treatments suggested are retrieved using structured and unstructured patient data. We employ multiple IR techniques like Best Match 25, query reformulation and re-ranking facilitated through deep neural networks, focusing on extracting highly accurate and relevant trials. The proposed pipeline achieved a high score of 0.58 in terms of Normalized Discounted Cumulative Gain (NDCG) score for ranking the relevant clinical trials, outperforming the state-of-the-art approaches.
Project Management Mechanism Based on Burndown Chart to Reduce the Risk of Software Project Failure

The failure rate of software projects has always been high. One of the key factors is the inability to detect and effectively solve the progress delay problem in time. Progress delay problems will continuous to propagate and extand in software development process to increase software project failure risk greatly. Burn down chart is a simple and useful progress monitor tool in agile software development. In this paper, we propose the project management mechanism based on burndown chart (PMM/BDC) for detecting the situations of progress delay. And use quantified measurement model to identify the problem of progress delays in iterative and incremental development. Project Management Mechanism based on burn down chart can identify the problems of progress delay timely. And the quantified measurement model can recommend improvement manner of project management defects. PMM/BDC combined with quantified measurement model can effectively reduce the risk of software project failure.
Enhanced Named Entity Recognition with Semantic Dependency

Dependency-based models for the named entity recognition (NER) task have shown promising results by capturing long-distance relationships between words in a sentence. However, while existing models focus on the syntactic dependency between entities, we are unaware of any work that considers semantic dependency. In this work, we study the usefulness of semantic dependency information for NER. We propose a NER model that is guided by semantic dependency graphs instead of syntactic dependency trees. The extensive experiments illustrate the effectiveness of the proposed model and the advantages of semantic dependency over syntactic dependency for NER. Also, it shows correlations between the NER performance and the semantic dependency annotations qualities.
Online food court payment processing using blockchain

It is a web-based application designed commonly for use in Food & beverage based enterprises. These applications tackle the issues of Manual Labour cost in the food court bringing about a rapid expansion in the Overall business Model. It also provides the proprietor of the restaurant to administer and make modifications as per the stip-ulations in their restaurant. The consumer can order their food by accessing the application through their mobile browser in an optimized manner. The retailer can easily complete the order in time, as there will be a streamlined check-in for the availability of ingredients. In this paper, let's exemplify the overall description and generalized soft-ware requirements specification of the entire systematic structure designed for use in different food courts. Our appli-cation will impart the restaurant employees to manage their online cuisines and keep track of all the orders placed by their consumers. Furthermore, the Customer can make Payment using the Existing Payment Gateways as well as they can go for blockchain-based Payment Methodologies. Eventually, this idea is projected to reduce the overheads of Payment gateways and improvise Data security in a precise manner.
Synthesizing waveform sequence-to-sequence to augment training data for sequence-to-sequence speech recognition

Sequence-to-sequence (seq2seq) automatic speech recognition (ASR) recently achieves state-of-the-art performance with fast decoding and a simple architecture. On the other hand, it requires a large amount of training data and cannot use text-only data for training. In our previous work, we proposed a method for applying text data to seq2seq ASR training by leveraging text-to-speech (TTS). However, we observe the log Mel-scale filterbank (lmfb) features produced by Tacotron 2-based model are blurry, particularly on the time dimension. This problem is mitigated by introducing the WaveNet vocoder to generate speech of better quality or spectrogram of better time-resolution. This makes it possible to train waveform-input end-to-end ASR. Here we use CNN filters and apply a masking method similar to SpecAugment. We compare the waveform-input model with two kinds of lmfb-input models: (1) lmfb features are directly generated by TTS, and (2) lmfb features are converted from the waveform generated by TTS. Experimental evaluations show the combination of waveform-output TTS and the waveform-input end-to-end ASR model outperforms the lmfb-input models in two domain adaptation settings.
FiShNet: Fine-Grained Filter Sharing for Resource-Efficient Multi-Task Learning

Multi-task learning has attracted much attention in recent years, where the goal is to learn multiple tasks by exploiting the similarities and differences between the tasks. Previous researches on multi-task learning mainly focus on flexible methods for feature sharing (e.g., soft sharing) under resource-sufficient settings (e.g., on GPU servers). However, in many real-world applications, we often need to deploy multi-task learning models on resource-constrained platforms (e.g., mobile devices). The high resource requirement of soft-sharing methods can make them hard to deploy on mobile devices. In this paper, we study the problem of Resource-efficient Multi-Task Learning (MTL), where the goal is to design a resource-friendly model that suits resource-constrained inference environment, e.g., security camera or mobile devices. We formulate the Resource-efficient MTL problem as a fine-grained filter sharing problem, i.e., learning how to share filters at any given convolutional layers among multiple tasks. We proposed a novel solution for parameter sharing, called FiShNet. Different from soft-sharing approaches, where the computational cost per task is growing w.r.t. the number of other tasks, FiShNet can achieve high accuracy comparable to soft-sharing approaches, while only consuming a constant computational cost per task. Different from hard-sharing approaches, where the parameter sharing structures are hand-picked, FiShNet can learn how to share parameters directly on the training data with finer-grained sharing. We evaluate FiShNet on a number of problem settings and datasets for multi-task learning. We show that FiShNet achieves high accuracy when compared with state-of-the-art methods in multi-task learning, while only requiring a fraction of the computational resource.
An RE’21 Workshop on Environment-Driven Requirements Engineering (EnviRE’21)

We organize a one-day workshop on Environment-Driven Requirements Engineering(EnviRE’21) in conjunction with the 29th IEEE International Requirements Engineering Conference. With the rising influence of AI, IoT, and cyber-physical systems, we realize that the environment, in which the software operates, becomes more open and evolves rapidly with stakeholders’ changing needs. EnviRE’21 features one keynote, four accepted papers, and one accepted presentation. Overall, the workshop is aimed at bringing the interested researchers and practitioners together, exchanging ideas and visions, and exploring a set of open problems to pursue in the years to come.
A Study of English-Indonesian Neural Machine Translation with Attention (Seq2Seq, ConvSeq2Seq, RNN, and MHA)

In recent years, Neural Machine Translation (NMT) with attention mechanisms has emerged in research and industry. This study discusses the essentials of NMT (Seq2Seq, Convolutional Seq2Seq (ConvSeq2Seq), Recurrent Neural Networks (RNN), and Multi-Head Attention (MHA)) while implemented in formal passages in English-Indonesian and Indonesian-English. The experimental results for ConvSeq2Seq achieve up to 38.99 BLEU sentence scores, 43.23 BLEU corpus scores, and 39.48 GLEU corpus scores over the Seq2Seq English-Indonesian. For Indonesian-English, the results for ConvSeq2Seq achieved as follows: up to 42.59 BLEU sentence scores, 42.91 BLEU corpus scores, 41.05 GLEU corpus scores, and 1356.65 WER scores over RNN and MHA. Thus, while ConvSeq2Seq tends to be the supremacy, this literature also describes the combination of architectures and specific fine-tuning strategies as a discussion.
Attention Template Update Model for Siamese Tracker

Visual tracking is defined as a template-matching task in current Siamese approaches. The tracker needs to locate the target by matching the template with the search area in each frame. Most current Siamese methods either do not use an update strategy or use a linear update method with a fixed learning rate. Neither of the above two strategies allows the target template to dynamically adapt to frequent and dramatic changes in appearance. To solve this problem, we propose a template update model based on the attention mechanism. Our model updates the template in a nonlinear manner. It can fully explore the weight relationship of various features in the template, so that the template can pay more attention to features that are more beneficial to determine the target in different situations. In addition, by adding an adjustment block, the error and invalid information in the old template can be removed before updating. Extensive experiments on several datasets demonstrated the effectiveness of our update model. We used SiamFC++ as our basic tracker and achieved state-of-the-art performance by adding our model. Moreover, our model has lightweight structure, and thus it can be easily applied to most Siamese trackers with minimal computational cost.