Reviewerly/exHarmony huggingface dataset:
Deep Ensemble Approach for Question Answer System

ResearchesMoholkar, K. P. Patil, S. H. on question answering systems has been attracting significant research attention in recent years with the explosive data growth and breakthroughs in machine learning paradigm. Answer selection in question answering segment is always considered as a challenging task in natural language processing domain. The major difficulty detected here is that it not only needs the consideration of semantic matching between question answer pairs but also requires a serious modeling of contextual factors. The system aims to use deep learning technique to generate the expected answer. Sequential ensemble approach is deployed in the proposed model, where it categorically boosts the prediction of LSTM and memory network to increase the system accuracy. The proposed model shows a 50% increase in accuracy when compared to individual systems with a few number of epochs. The proposed system reduces the training time and boosts the system-level accuracy.
Multi-task Learning by Leveraging the Semantic Information

One crucial objective of multi-task learning is to align distributions across tasks so that the information between them can be transferred and shared. However, existing approaches only focused on matching the marginal feature distribution while ignoring the semantic information, which may hinder the learning performance. To address this issue, we propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. We first theoretically analyze the generalization bound of multi-task learning based on the notion of Jensen-Shannon divergence, which provides new insights into the value of label information in multi-task learning. Our analysis also leads to a concrete algorithm that jointly matches the semantic distribution and controls label distribution divergence. To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions. Empirical results demonstrate that the proposed method could outperform most baselines and achieve state-of-the-art performance, particularly showing the benefits under the label shift conditions.
Ethics for AI in Business

Digital transformation and the fourth industrial revolution are increasingly impacting all aspect of everyday life, including business management. The use of AI comes with substantial advantages in the short, medium and long term, but also creates substantial new risks that must be appropriately addressed. These risks can hinder innovation and progress if they constitute an obstacle for the adoption of AI technologies. An ethical adoption of AI can encourage progress and innovation and therefore benefit both business and society at large. In this work we aim to provide a clear and understandable managerial framework for stakeholders to adopt AI safely within their activities, an ethical risk identification and mitigation set of strategies, and emphasise the leadership role that firms can have in this process.
A Real-Time Face Recognition System by Efficient Hardware-Software Co-Design on FPGA SoCs

With the development of deep learning, the accuracy of face recognition has been significantly improved. Current face recognition systems are mostly designed for CPU or GPU platforms, and faces significant latency and power constraints when migrated to embedded devices. In this live demonstration, a real-time face recognition system based on FPGA System-on-Chip (SoC) platforms is presented. To achieve real-time processing, the face recognition algorithm based on convolutional neural network is optimized first to a hardware-friendly network model and is accelerated on FPGA, while the face detection and face alignment are implemented on ARM. The latency of the entire system is 52 ms, and the face recognition accuracy on the LWF data set reaches 99.05%.
Learning and Exploiting Shaped Reward Models for Large Scale Multiagent RL

Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of interactions among agents, a feature commonly present in large scale multiagent applications; (b) a shaped reward model analytically derived from the learned reward model to address the key challenge of credit assignment; (c) a model-based multiagent RL approach that integrates shaped rewards into well known RL algorithms such as policy gradient, soft-actor critic. Compared to previous methods, our learned reward models are more accurate, and our approaches achieve better solution quality on synthetic and real world instances of air traffic control, and cooperative navigation with large agent population.
Argument Mining in Tweets: Comparing Crowd and Expert Annotations for Automated Claim and Evidence Detection

One of the main challenges in the development of argument mining tools is the availability of annotated data of adequate size and quality. However, generating data sets using experts is expensive from both organizational and financial perspectives, which is also the case for tools developed for identifying argumentative content in informal social media texts like tweets. As a solution, we propose using crowdsourcing as a fast, scalable, and cost-effective alternative to linguistic experts. To investigate the crowd workers’ performance, we compare crowd and expert annotations of argumentative content, dividing it into claim and evidence, for 300 German tweet pairs from the domain of climate change. As being the first work comparing crowd and expert annotations for argument mining in tweets, we show that crowd workers can achieve similar results to experts when annotating claims; however, identifying evidence is a more challenging task both for naive crowds and experts. Further, we train supervised classification and sequence labeling models for claim and evidence detection, showing that crowdsourced data delivers promising results when comparing to experts.
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation

A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary.This potentially weakens the effect when applying pretrained models into natural language generation (NLG) tasks, especially for the subword distributions between upstream and downstream tasks with significant discrepancy.Towards approaching this problem, we extend the vanilla pretrain-finetune pipeline with an extra embedding transfer step.Specifically, a plug-and-play embedding generator is introduced to produce the representation of any input token, according to pre-trained embeddings of its morphologically similar ones.Thus, embeddings of mismatch tokens in downstream tasks can also be efficiently initialized.We conduct experiments on a variety of NLG tasks under the pretrain-finetune fashion.Experimental results and extensive analyses show that the proposed strategy offers us opportunities to feel free to transfer the vocabulary, leading to more efficient and better performed downstream NLG models.
Hierarchical Width-Based Planning and Learning

Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of the size of the state-space, but exponentially in the problem width. In practice, running the algorithm with a width larger than 1 is computationally intractable, prohibiting IW from solving higher width problems. In this paper, we present a hierarchical algorithm that plans at two levels of abstraction. A high-level planner uses abstract features that are incrementally discovered from low-level pruning decisions. We illustrate this algorithm in classical planning PDDL domains as well as in pixel-based simulator domains. In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2. For pixel-based domains, we show how in combination with a learned policy and a learned value function, the proposed hierarchical IW can outperform current flat IW-based planners in Atari games with sparse rewards.
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification

Ordinal Classification (OC) is an important classification task where the classes are ordinal.For example, an OC task for sentiment analysis could have the following classes: highly positive, positive, neutral, negative, highly negative.Clearly, evaluation measures for an OC task should penalise misclassifications by considering the ordinal nature of the classes (e.g., highly positive misclassified as positive vs. misclassifed as highly negative).Ordinal Quantification (OQ) is a related task where the gold data is a distribution over ordinal classes, and the system is required to estimate this distribution.Evaluation measures for an OQ task should also take the ordinal nature of the classes into account.However, for both OC and OQ, there are only a small number of known evaluation measures that meet this basic requirement.In the present study, we utilise data from the SemEval and NTCIR communities to clarify the properties of nine evaluation measures in the context of OC tasks, and six measures in the context of OQ tasks.
Computational Complexity of Multi-player Evolutionarily Stable Strategies

In this paper we study the computational complexity of computing an evolutionary stable strategy (ESS) in multi-player symmetric games. For two-player games, deciding existence of an ESS is complete for $$\mathrm {\Sigma }^\mathrm {p}_2$$ , the second level of the polynomial time hierarchy. We show that deciding existence of an ESS of a multi-player game is closely connected to the second level of the real polynomial time hierarchy. Namely, we show that the problem is hard for a complexity class we denote as $$\exists ^\mathrm {D}\cdot \forall \mathbb {R}$$ and is a member of $$\exists \forall \mathbb {R}$$ , where the former class restrict the latter by having the existentially quantified variables be Boolean rather then real-valued. As a special case of our results it follows that deciding whether a given strategy is an ESS is complete for $$\forall \mathbb {R}$$ .
Live through of the Students in the Conditions of Pandemic of Covid - 19

Using the methodological approach of qualitative research to conduct empirical research in the social sciences, the impact of the COVID-19 pandemic on students' experiences of the present and their projections for the future is revealed. The results show that many of them find positives from social isolation in the opportunity to pay more attention to the people important to them and to work more purposefully on their own development. On the other hand, serious fears are revealed, the main of which is for the health and life of their loved ones, as well as for the future, for their career development and realization. They find it difficult to tolerate social isolation and most of them experience their mental state as shaky, as depressed. In general, students have a negative attitude towards distance learning – online, considering it inferior to face-to-face training and assess this training as a risk to their professional development and subsequent realization in the labor market.
On Explainable Features for Translatorship Attribution: Unveiling the Translator’s Style With Causality

Translatorship attribution deals with accurately attributing a translation to its translator. The task is challenging because several factors can confound the attribution such as the original author’s style, genre, and topic of the text. The attribution and the identification of the translator’s style could contribute to fields including translation studies and forensic linguistics. In this paper, we pose translatorship attribution as a multiclass classification problem and employ machine learning algorithms. To address the problem of confounding, we use corpora of English translations of the same source material (parallel corpora) to identify the translators’ personal style. We propose two novel feature sets in this task: i) a list of cohesive markers with and without their surrounding punctuation and ii) syntactic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -grams to capture real syntactic information. We employ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula> feature selection and, using 10-fold cross-validation, assess the accuracy of several classifiers trained with our proposed features and with word, punctuation, POS, and POS-punctuation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -grams. The results show that the proposed features yield comparable and even higher accuracy results than the reported in the literature on the same corpora and prove that POS-punctuation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -grams are an effective feature set for this task. We also recover the most distinctive features and provide examples of stylistic interpretations of them for each translator. Finally, using insights from causal inference, where confounding is well-defined and studied, we provide a novel explanation for the accepted need of using parallel and contemporaneous corpora on this task and for the different results among types of features.
Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort

In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging.Automatic metrics are not reliable when it comes to high performing systems.In addition, resorting to human evaluators can be expensive, especially when evaluating multiple systems.To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available.Our experiments on WMT'19 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered, despite the lack of human feedback for many translations.
Enhanced Speech Recognition for Indonesian Geographic Dictionary Using Deep Learning

Speech recognition technology has been developing very fast lately. One of its application is to know the meaning of some terms included in a geographic dictionary. When a subject speaks a word to the system, it will output the word and its meaning and explanation. There are many methods that are applied to speech recognition. One of the methods that can be applied and improve the accuracy of speech recognition is the use of a deep learning method, i.e. Convolutional Neural Network (CNN). In this research, CNN's speech recognition accuracy for the Indonesian geographic dictionary is analyzed to show that CNN can improve the accuracy of speech recognition compared to speech recognition with Gaussian mixture model and hidden Markov model (GMM-HMM). CNN is one of deep learning methods that analyzes and finds similarity in Mel-frequency cepstral coefficients (MFCC) from sound waves. This research is performed by making models of the spoken words using CNN under Python and TensorFlow. CNN is trained with these models from speech data collected and prepared from 20 students, consists of 19 men and a woman of different ages from 19 to 23 years. The vocabulary of the database consists of 50 words. The result of this research is a desktop application with the trained models implemented. Our application can recognize well the spoken words from subjects. Testing of the trained models was performed to examine the accuracy of the build speech recognition system. The result of the CNN speech recognition method from the Indonesian geographic dictionary is 80% accuracy for isolated words and 72.67% for continuous words in our research.
Pengenalan Pola Tulisan Tangan Aksara Bima menggunakan Ciri Tekstur dan KNN

As the fact, that is Bima script did not familiar to bimanese, Bima script as a cultural heritage needs to be preserved. Pattern recognition has been used to recognize several of ancient script. Gray Level Co-occurence Matrix (GLCM) as features exctraction and K Nearest Neighbour (KNN) as a classifier show the good performance to recognize of an ancient script. so, in this research we use GLCM and KNN to recognize Bima script. we use 2640 images of handwritting bima Script that is collected from 10 volunter. Each volunter write 22 of Bima script twelve times each script. The experimental result show that the performance of our model is good enough, with 60.86% of accuracy that is obtained by manhattan distances.
Seeing like a dataset from the global south

This is a forum for perspectives on designing for marginalized communities worldwide. Articles will discuss design methods, theoretical/conceptual contributions, and participatory interventions with underserved communities. --- Nithya Sambasivan, Editor
Network Security Monitoring Method Based on Deep Learning

Absrtact With the rise and rapid development of mobile communication, big data and artificial intelligence technology, we are entering the era of mobile Internet. With the continuous intellectualization of network security and infrastructure, information technology has been widely used in the field of industrial control, making network security more and more open, which brings a new network security control system to the traditional relatively closed industrial control system. This paper mainly introduces the network security monitoring method based on deep learning. In this paper, the network security monitoring method based on deep learning is studied, and the network security monitoring planning is designed by using image analysis, and the feasibility of detection model and deep learning is analyzed reasonably. A network security monitoring method based on deep learning is designed. Through data collection, feature extraction and neural network model training of network security power consumption information, non-invasive network security monitoring is realized. This method can detect network security information attacks that cannot be found at the network level, and improve the security performance of network security. The experimental results show that the network security monitoring method for deep learning increases the network security efficiency by 24%. The limitations of the research on network security monitoring method for deep learning and the methods and paths to provide good network security monitoring planning for image analysis application are analyzed, discussed and summarized, so as to enrich the academic research results.
An Empirical Study of Heterogeneous Cross-Project Defect Prediction Using Various Statistical Techniques

Cross-project defect prediction (CPDP) forecasts flaws in a target project through defect prediction models (DPM) trained by defect data of another project. However, CPDP has a prevalent problem (i.e., distinct projects must have identical features to describe themselves). This article emphasizes on heterogeneous CPDP (HCPDP) modeling that does not require same metric set between two applications and builds DPM based on metrics showing comparable distribution in their values for a given pair of datasets. This paper evaluates empirically and theoretically HCPDP modeling, which comprises of three main phases: feature ranking and feature selection, metric matching, and finally, predicting defects in the target application. The research work has been experimented on 13 benchmarked datasets of three open source projects. Results show that performance of HCPDP is very much comparable to baseline within project defect prediction (WPDP) and XG boosting classification model gives best results when used in conjunction with Kendall's method of correlation as compared to other set of classifiers.
Hybrid Crypto Techniques for Secured Multimedia Big Data Content Protection System (SMBDCPS)

Most of the existing work does not ensure the optimal and secured storage maintenance of big data. This is resolved in the proposed research method name called secured multimedia big data content protection system (SMBDCPS). The proposed scheme has using AES and SHA-256 hybrid mechanism for securing the keys of encryption and decryption. This method has integrated the key value of AES and SHA-256 together to generate the new key value for increasing the security level. The main advantage of the proposed scheme has required only small storage which is computation efficient. The overall performance of the proposed scheme has carried out on MATLAB simulation environment over the medical health care datasets. The performance proves the proposed scheme has yield better results than the existing scheme.
Detecting Faults During Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving

Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application.
Machine learning assisted quantum adiabatic algorithm design

Quantum computing has made dramatic progress in the last decade. The quantum platforms including superconducting qubits, photonic devices, and atomic ensembles, have all reached a new era, with unprecedented quantum control capability developed. Quantum computation advantage over classical computers has been reported on certain computation tasks. A promising computing protocol of using the computation power in these controllable quantum devices is implemented through quantum adiabatic computing, where quantum algorithm design plays an essential role in fully using the quantum advantage. Here in this paper, we review recent developments in using machine learning approach to design the quantum adiabatic algorithm. Its applications to 3-SAT problems, and also the Grover search problems are discussed.
Analysis of smart contracts balances

We define a technique for analyzing updates of smart contracts balances due to transfers of digital assets. The analysis addresses a lightweight smart contract language and consists of a two-step translation. First, we define the input-output behaviors of smart contract functions by means of a simple functional language with static dispatch. Then we associate the terms of this intermediate language with cost equations that compute the loss or gain of digital assets. The resulting equations can be fed to an off-the-shelf cost analyzer to provide upper bounds to the loss or gain. Our analysis has been prototyped and we report its assessments and discuss extensions with additional features.