Reviewerly/exHarmony huggingface dataset:
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?

Developing robust NLP models that perform well on many, even small, slices of data is a significant but important challenge, with implications from fairness to general reliability.To this end, recent research has explored how models rely on spurious correlations, and how counterfactual data augmentation (CDA) can mitigate such issues.In this paper we study how and why modeling counterfactuals over multiple attributes can go significantly further in improving model performance.We propose RDI, a context-aware methodology which takes into account the impact of secondary attributes on the model's predictions and increases sensitivity for secondary attributes over reweighted counterfactually augmented data.By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods.We also demonstrate that RDI generalizes to the coreference resolution task and provide guidelines to extend this to other tasks.
Data Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage?

This survey/position paper discusses ways to improve coverage of resources such as Word-Net.Rapp estimated correlations, ρ, between corpus statistics and psycholinguistic norms.ρ improves with quantity (corpus size) and quality (balance).1M words are enough for simple estimates (unigram frequencies), but at least 100M are required for pairs of words (word associations, edges).Knowledge Graph Completion (KGC) attempts to learn missing links in WN18.Unfortunately, WN18 is flawed with information leaking from train to test.More seriously, WN18 is based on SemCor (just 200k words) and dated (collected in 1960s).KGC cannot learn anything that happened since the 1960s, or associations requiring 100M words.
A study on the relationship among Servant Leadership, Authentic Leadership, Perceived Organizational Support(POS), and Agile Culture using PLS-SEM: Mediating Effect of POS

Our study intended to review the characteristics of Agile Culture, which has attract ed significant attention as an organizational culture suitable to deal with the recent changes i n the management environment of enterprises and to reveal the role and interrelationships of leaderships, Agile Culture, and Perceived Organizational Support (POS).First, through previous studies, leaderships that influence Agile Culture were viewed as Servan t Leadership and Authentic Leadership, and POS as an organizational characteristic that media tes the relationship between these two leaderships and Agile Culture. in addition, the sub-dim ensions of Agile Culture were recognized as Empowerment, Continuous Learning, and Collectiv e Intelligence.Based on these findings, a research model was developed to analyze the effect relationship among these variables.Our study was conducted mainly on the National Agricult ural Cooperative Federation and its affiliated organizations in Korea and the SmartPLS 3.3.2was used for statistical analysis of collected data.Both Servant Leadership and Authentic Leadership had positive effect on POS and POS had st atistically significant effect on Empowerment, Collective Intelligence and Continuous Learning, a ll sub-dimensions of Agile Culture.The mediating effect of POS in the relationship between le aderships and Agile Culture was tested and POS mediated the positive effect of Servant Lead ership and Authentic Leadership on two of the sub-dimensions of Agile Culture: Collective Int elligence and Empowerment.The samples of this study were restricted to specific organization, limiting the generalization to all industries.The future studies should include various industries, and the scope of resear ch needs to be expanded to additional factors affecting Agile Culture.
Cross Attention Augmented Transducer Networks for Simultaneous Translation

This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation.The framework aims to jointly optimize the policy and translation models.To effectively consider all possible READ-WRITE simultaneous translation action paths, we adapt the online automatic speech recognition (ASR) model, RNN-T, but remove the strong monotonic constraint, which is critical for the translation task to consider reordering.To make CAAT work, we introduce a novel latency loss whose expectation can be optimized by a forward-backward algorithm.We implement CAAT with Transformer while the general CAAT architecture can also be implemented with other attention-based encoder-decoder frameworks.Experiments on both speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks show that CAAT achieves significantly better latency-quality trade-offs compared to the state-of-the-art simultaneous translation approaches. 1
Radar Signal Modulation Recognition Based on Sep-ResNet

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time-frequency (T-F) analysis and a deep neural network to identify radar modulation signals. The T-F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T-F images. Adaptive filtering and morphological processing are used in T-F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T-F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is -10 dB, the probability of successful recognition (PSR) is 93.44%.
Evaluation Criteria for Quality Education of Physical Education Lessons Based on Logical Analysis

To cultivate high-quality talents, it is important to develop a set of scientific and complete evaluation criteria for comprehensive quality. The effective evaluation could promote and guide teaching and learning, and improve the overall quality of students. Considering the features of PE lessons, this paper resorts to multiple methods, e.g., questionnaire survey, Delphi’s method, mathematical statistics, and logical analysis, to establish a system of evaluation criteria for the quality education of PE lessons, the proposed system includes 4 primary indices, 10 secondary indices, and 24 tertiary indices. The weight of each index was determined through analytic hierarchy process (AHP), making the criteria more scientific. Taking the PE major of a college for example, the proposed criteria were investigated empirically. The results show that experts, teachers, and students made consistent evaluations. Thus, the proposed system and evaluation criteria are feasible, and capable of evaluating the overall quality of students objectively and fairly. The research results provide a tool and idea for improving the quality education of PE lessons in China, and promoting the evaluation of students’ overall quality.
Transferring Knowledge from Vision to Language: How to Achieve it and how to Measure it?

Large language models are known to suffer from the hallucination problem in that they are prone to output statements that are false or inconsistent, indicating a lack of knowledge.A proposed solution to this is to provide the model with additional data modalities that complements the knowledge obtained through text.We investigate the use of visual data to complement the knowledge of large language models by proposing a method for evaluating visual knowledge transfer to text for uni-or multimodal language models.The method is based on two steps, 1) a novel task querying for knowledge of memory colors, i.e. typical colors of well-known objects, and 2) filtering of model training data to clearly separate knowledge contributions.Additionally, we introduce a model architecture that involves a visual imagination step and evaluate it with our proposed method.We find that our method can successfully be used to measure visual knowledge transfer capabilities in models and that our novel model architecture shows promising results for leveraging multimodal knowledge in a unimodal setting.
MELOPPR: Software/Hardware Co-design for Memory-efficient Low-latency Personalized PageRank

Personalized PageRank (PPR) is a graph algorithm that evaluates the importance of the surrounding nodes from a source node. Widely used in social network related applications such as recommender systems, PPR requires real-time responses (latency) for a better user experience. Existing works either focus on algorithmic optimization for improving precision while neglecting hardware implementations or focus on distributed global graph processing on large-scale systems for improving throughput rather than response time. Optimizing low-latency local PPR algorithm with a tight memory budget on edge devices remains unexplored. In this work, we propose a memory-efficient, low-latency PPR solution, namely MeLoPPR, with largely reduced memory requirement and a flexible trade-off between latency and precision. MeLoPPR is composed of stage decomposition and linear decomposition and exploits the node score sparsity: Through stage and linear decomposition, MeLoPPR breaks the computation on a large graph into a set of smaller sub-graphs, that significantly saves the computation memory; Through sparsity exploitation, MeLoPPR selectively chooses the sub-graphs that contribute the most to the precision to reduce the required computation. In addition, through software/hardware co-design, we propose a hardware implementation on a hybrid CPU and FPGA accelerating platform, that further speeds up the sub-graph computation. We evaluate the proposed MeLoPPR on memory-constrained devices including a personal laptop and Xilinx Kintex-7 KC705 FPGA using six real-world graphs. First, MeLoPPR demonstrates significant memory saving by $1. 5 \times \sim 13. 4 \times$ on CPU and $73 \times \sim 8699 \times$ on FPGA. Second, MeLoPPR allows flexible trade-offs between precision and execution time: when the precision is 80%, the speedup on CPU is up to $15\times$ and up to $707\times$ on FPGA; when the precision is around 90%, the speedup is up to $70\times$ on FPGA.
Multiple Instance Classification for Gastric Cancer Pathological Images Based on Implicit Spatial Topological Structure Representation

Gastric cancer is a malignant tumor with high incidence. Computer-aided screening systems for gastric cancer pathological images can contribute to reducing the workload of specialists and improve the efficiency of disease diagnosis. Due to the high resolution of images, it is common to divide the whole slide image (WSI) into a set of image patches with overlap before utilizing deep neural networks for further analysis. However, not all patches split from the same cancerous WSI contain information of cancerous issues. This restriction naturally satisfies the assumptions of multiple instance learning (MIL). Moreover, the spatial topological structure relationships between local areas in a WSI are destroyed in the process of patch partitioning. Most existing multiple instance classification (MIC) methods fail to take into account the topological relationships between instances. In this paper, we propose a novel multiple instance classification framework based on graph convolutional networks (GCNs) for gastric microscope image classification. Firstly, patch embeddings were generated by feature extraction. Then, a graph structure was introduced to model the spatial topological structure relationships between instances. Additionally, a graph classification model with hierarchical pooling was constructed to achieve this multiple instance classification task. To certify the effectiveness and generalization of our method, we conducted comparative experiments on two different modes of gastric cancer pathological image datasets. The proposed method achieved average fivefold cross-validation precisions of 91.16% and 98.26% for gastric cancer classification on the two datasets, respectively.
A Noninvasive Model to Detect Malaria Based on Symptoms Using Machine Learning

The impact of Artificial Intelligence in the domain of Healthcare has been growing, day by day. These applications bring a drastic change in the healthcare system and affects our lives based in the change it brings to the Patientcare system, transforming the traditional way of handling sicknesses and diseases. Machine Learning algorithms that use data, have a big role in the AI based applications that are used in the Healthcare. Hence the Data source and the nature of Data holds an important role in developing effective AI based solutions for many health issues in the society. Data is available in all the hospitals and medical care facilities for many years now. However, without transforming them into a format where Machine Learning algorithms work, it is impossible to use them to develop an AI based application. In this research paper, we briefly discuss the process of developing an AI based application to predict Malaria, which is one of the most common vector borne diseases in the coastal districts of Karnataka. This pioneer work was done over the data collected from the clinical notes of a 1500 bed hospital situated in Mangalore. Few machine learning algorithms like Logistic regression, Support vector machine XGB Booster classifier, CAT Booster Classifier and Random forest classifier were used over the dataset. Our experimental study revealed that, Random Forest classifier works efficiently for this data set, compared with the other algorithms that we used. It gave the best accuracy of 90.92.
Effect of ease of doing business dimensions on foreign direct investment in Nigeria

In Sub-Saharan Africa countries, there is growing interest regarding attraction of foreign direct investments in order to sustainably grow economy.Despite the fact that Nigeria is the economic hotspot in Sub-Saharan Africa, recent declining foreign direct investment statistics is increasingly becoming a serious concern to government.Regrettably, constraining factors have been under-researched leading to little understanding among policy-makers.This paper examines dimensions of ease of doing business on foreign direct investment in Nigerian economy.Survey research was adopted as the design of the study.The population of the study comprises registered small and medium enterprises in Southeastern Nigeria.A sample of 300 respondents where statistically determined.The study used primary data which were generated through the administration of structured questionnaire on the representative sample of the population.Descriptive statistics such as percentages was used to describe the demographic characteristics of respondents while hypotheses of the study were tested using
Effects of Communication on the Evolution of Squad Behaviours

As the non-playable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviours that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective team behaviours for shooter games. GP has been used to evolve teams capable of defeating a single powerful enemy agent in a number of environments without the use of any explicit team communication. The aim of this paper is to explore the effects of communication on the evolution of effective squad behaviours. Thus, NPCs are given the ability to communicate their perceived information during evolution. The results show that communication between team members enables an improvement in average team effectiveness.
A gradual type system for Elixir

Elixir is a functional programming language with dynamic typing. We propose a gradual type system that makes it possible to perform type-checking on a significant fragment of the language. An important feature of the type system is that it does not require any syntactic change to Elixir. Type information is provided by means of function signatures which are declared in terms of Elixir typespec directives. The proposed type system is based on subtyping and is backward compatible, as it allows the presence of untyped code fragments. We have a prototype implementation of the type-checker in Elixir itself and a formalization in Idris of the fragment considered.
Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Common electric powered wheelchairs cannot safely negotiate architectural barriers (i.e., curbs) which could injure the user and damage the wheelchair. Robotic wheelchairs have been developed to address this issue; however, proper alignment performed by the user is needed prior to negotiating curbs. Users with physical and/or sensory impairments may find it challenging to negotiate such barriers. Hence, a Curb Recognition and Negotiation (CRN) system was developed to increase user's speed and safety when negotiating a curb. This article describes the CRN system which combines an existing curb negotiation application of a mobility enhancement robot (MEBot) and a plane extraction algorithm called Polylidar3D to recognize curb characteristics and automatically approach and negotiate curbs. The accuracy and reliability of the CRN system were evaluated to detect an engineered curb with known height and 15 starting positions in controlled conditions. The CRN system successfully recognized curbs at 14 out of 15 starting positions and correctly determined the height and distance for the MEBot to travel towards the curb. While the MEBot curb alignment was 1.5 ± 4.4°, the curb ascending was executed safely. The findings provide support for the implementation of a robotic wheelchair to increase speed and reduce human error when negotiating curbs and improve accessibility.
U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images

Tables are an important element in a document and can express more information with fewer words. Due to the different arrangements of tables and texts, as well as the variety of layouts, table detection is a challenge in the field of document analysis. Nowadays, as Optical Character Recognition technology has gradually matured, it can help us to obtain text information quickly, and the ability to accurately detect table structures can improve the efficiency of obtaining text content. The process of document digitization is influenced by the editor’s style on the table layout. In addition, many industries rely on a large number of people to process data, which has high expense, thus, the industry imports artificial intelligence and Robotic Process Automation to handle simple and complicated routine text digitization work. Therefore, this paper proposes an end-to-end table detection model, U-SSD, as based on the object detection method of deep learning, takes the Single Shot MultiBox Detector (SSD) as the basic model architecture, improves it by U-Net, and adds dilated convolution to enhance the feature learning capability of the network. The experiment in this study uses the dataset of accident claim documents, as provided by a Taiwanese Law Firm, and conducts table detection. The experimental results show that the proposed method is effective. In addition, the results of the evaluation on open dataset of TableBank, Github, and ICDAR13 show that the SSD-based network architectures can achieve good performance.
Analysis of 32-bit Fixed Point Quantizer in the Wide Variance Range for the Laplacian Source

The main goal of this paper is to examine the possibility of using the 32-bit fixed-point format to represent the weights of neural networks (NN) instead of the standardly used 32-bit floating-point format in order to reduce the complexity of NN implementation. To this end, the performance of the 32-bit fixed-point format is analyzed, using an analogy between the fixed-point format and the uniform quantization that allows for the performance of the 32-bit fixed-point format to be expressed by an objective measure SQNR (Signal-to-Quantization Noise Ratio). In doing so, SQNR analysis is performed in a wide range of variance of NN weights, looking for a solution that maximizes the average SQNR in that range of variance. Also, an experiment is performed, applying the 32-bit fixed-point format to represent the weights of an MLP (Multilayer Perceptron) neural network trained for classification purposes. It is shown that the application of the 32-bit fixed-point representation of MLP weights achieves the same classification accuracy as in the case of the 32-bit floating-point representation of MLP weights, proving that the application of the 32-bit fixed-point representation of weights reduces the implementation complexity of neural networks without compromising the accuracy of classification.
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning

Trading off exploration and exploitation in an unknown environment is key to maximising expected online return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but also on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn approximately Bayes-optimal policies for complex tasks. VariBAD simultaneously meta-learns a variational auto-encoder to perform approximate inference, and a policy that incorporates task uncertainty directly during action selection by conditioning on both the environment state and the approximate belief. In two toy domains, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We further evaluate variBAD on MuJoCo tasks widely used in meta-RL and show that it achieves higher online return than existing methods. On the recently proposed Meta-World ML1 benchmark, variBAD achieves state of the art results by a large margin, fully solving two out of the three ML1 tasks for the first time.
Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment

Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications.