Reviewerly/exHarmony huggingface dataset:
Determinants of Adoption Rate of Rice Production Technologies Introduced by Agricultural Research Outreach Centres (AROCs) by Farmers in Niger State, Nigeria

The study assessed the determinants of adoption rate of rice production technologies introduced by Agricultural Research Outreach Centres in Nigeria. Data were collected using a multi-sampling technique. Data were analysed using simple descriptive statistics and multiple linear regression. Results revealed that respondents’ mean age was 50 years; level of formal education of farmers was low and farm size was 2.5ha on the average. Age, farming experience, years of schooling and number of extension visits were the socioeconomic determinants affecting rate of adoption. It was recommended that more villages should be selected with partnership between government and the private sector in order to cover more grounds and increase the rate of adoption of new technologies. Also, government and relevant stakeholders should prioritize establishment of the best extension teaching methods and systems as well as administration to help increase adoption rate of innovations and sustainability of the use of these technologies over time.
An improved TSVD-GCV inversion algorithm of pore size distribution in time-domain induced polarization using migration Hankel matrix

Reservoir porosity obtained from time-domain induced polarization (TDIP) well logging plays a vital role in estimating the hydraulic properties and obtain the reservoir parameters in a water-flood oil-field. Improving the inversion accuracy of the reservoir porosity can enhance the oil recovery in the water-flood oil-filed. Evaluating reservoir pore size distribution through induced polarization decay curve is confronted with the problems of poor applicability of data pre-processing, low accuracy and lacking of evaluation criteria for inversion results of pore size distribution. The basic principles of TDIP are introduced and the relationship between pore relaxation time and pore diameter is given. Combining the mathematical characteristics of polarization decay curve data, the performance and the limitations of existing pre-processing algorithms are analyzed and pointed out, respectively. An improved data pre-processing algorithm using the spatial characteristics of linear transformation based on migration Hankel matrix is proposed, and this method improves the inversion accuracy of pore size distribution greatly. In the engineering application, 2-logarithmic sampling method is proposed to sample the polarization decay data for more efficient petroleum exploration with less sample points. The different regularization methods, regularization matrix and regularization parameter determination methods are compared and analyzed for the inversion of the pore size distribution. The numerical simulation experimental results show that the stability and accuracy of Truncated Singular Value Decomposition - Generalized Cross Validation (TSVD-GCV), Truncated Singular Value Decomposition - L Curve (TSVD-L) and Tikhonov-I-L are appropriate for the inversion of pore size distribution. Because of the truth that the pore size distribution of rock is unknown, Backus-Gilbert (BG) theory is introduced to evaluate the inversion results of rock polarization decay curve data of a mining area in Jilin Province. The rock sample experiment shows that the TSVD-GCV inversion algorithm has the best performance.
Robust Graph Neural Network Against Poisoning Attacks via Transfer Learning.

Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce the performances of GNNs. It is very challenging to design robust graph neural networks against poisoning attack and several efforts have been taken. Existing work aims at reducing the negative impact from adversarial edges only with the poisoned graph, which is sub-optimal since they fail to discriminate adversarial edges from normal ones. On the other hand, clean graphs from similar domains as the target poisoned graph are usually available in the real world. By perturbing these clean graphs, we create supervised knowledge to train the ability to detect adversarial edges so that the robustness of GNNs is elevated. However, such potential for clean graphs is neglected by existing work. To this end, we investigate a novel problem of improving the robustness of GNNs against poisoning attacks by exploring clean graphs. Specifically, we propose PA-GNN, which relies on a penalized aggregation mechanism that directly restrict the negative impact of adversarial edges by assigning them lower attention coefficients. To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph. Experimental results on four real-world datasets demonstrate the robustness of PA-GNN against poisoning attacks on graphs. Code and data are available here: this https URL.
Siamese Network Using Adaptive Background Superposition Initialization for Real-Time Object Tracking

Object tracking has become widespread in many fields, such as autonomous vehicles, video surveillance and robotics. However, it is far from the requirements for real-world applications. Recently, Siamese network based trackers have attracted high attention by balancing accuracy and speed. Because these trackers only learn a similarity measurement model via off-line training, the exemplar branch has insufficient discriminant information to adapt to the constantly changing appearance of the target in subsequent frames. We propose a Siamese network based tracker that improves upon tracking performance as follows. First, an adaptive background superposition initialization is proposed and used in the exemplar branch to make full use of the limited prior information in the first frame. Second, a light-weight convolutional neural network is proposed and applied as the tracker's backbone; it compresses the dimensions of the feature to ensure speed and accuracy. Third, the channel attention module is introduced into our tracker and integrated with adaptive background superposition initialization. The feature map of the original exemplar image and its background changed image are adjusted by a channel attention model and fused to enhance the representation of the exemplar image. The GOT-10k dataset is applied to train our tracker. Finally, experiments on the object tracking benchmark (OTB) and visual object tracking (VOT) demonstrate the effectiveness of our proposed approach compared with state-of-the-art trackers.
Same Same but Different: Exploring the Effects of the Stroop Color Word Test in Virtual Reality

Virtual reality (VR) is used for different trainings e.g. for pilots, athletes, and surgeons. Dangerous and difficult situations are often focused in such simulations in VR, targeting to learn how to perform well under stress. However, there has been little work on understanding stress perception in VR compared to the real-world situation. In this paper we present an investigation of how users experience a stressful task in VR compared to in a classic office environment. Specifically, we investigate the subjective stress experience and physiological arousal with 15 participants performing the Stroop color word test either on a regular desktop screen, in VR, or in VR requiring head movements. Our findings suggest that stressful tasks are perceived less stressful when being performed in VR compared to the real environment as long as there is no additional stress factor, such as head movement involved. Our work indicates that it may be valuable to transfer stressful tasks, currently done in traditional office environments into VR.
A Neural User Preference Modeling Framework for Recommendation Based on Knowledge Graph

To address the data sparsity and cold start problems in the traditional recommender systems, lots of researchers aim at incorporating knowledge graphs (KG) into recommender systems to enhance the recommendation performance. However, existing efforts mainly rely on hand-engineered features from KG (e.g., meta paths), which requires domain knowledge. What’s more, as relations are usually excluded from meta paths, they hardly specify the holistic semantics of paths. To address the limitations of existing methods, we propose an end-to-end neural user preference modeling framework (UPM) to incorporate features of entity and relation of KG into the representations of users and items, so as to learn user latent interests precisely. Specifically, UPM first propagate user’s interests along links between entities in KG iteratively to learn user’s potential preferences for the item. Furthermore, these preference features are dynamically during the preference propagation process. That is to say, the importance of these preference features to characterize user is different. Therefore, an attention network is used in UPM to calculate the influence of preference features at different propagating stages, then the final preference vector of the user is calculated from the preference features and the corresponding weights. Lastly, the final prediction probability of user-item interaction is obtained by inner product operation between the embedding of item and user. To evaluate our framework, extensive experiments on two real-world datasets demonstrate significant performance improvements over state-of-the-art methods.
The Nexus between Financial Development and Economic Growth: Evidence from European Countries

The subprime crisis brought new challenges for the European countries that's why this study examines the relationship between banking sector development, stock market development and economic growth, using annual data, for the period 1990-2015, in twelve European Countries (Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal and Spain).The principal component analysis was used to construct two new measures (banking sector development and stock market development).The Panel Vector Auto-Regressive model developed by Love and Zicchino (2006) and Granger causalities test was used.Results show that the model is endogenous, stable and that the shocks caused by the introduction of the euro and the subprime crisis are significant.By using dummies tools to control the crisis effects, the banking sector development and the stock market development show a bidirectional relationship.The results suggest that governments should implement stability policies of the banking sector development to attract foreign direct investment that impulses economic growth.Future research that evolves the nexus between financial development and economic growth should take into consideration the impact of the economic crisis in the countries.
Residual objectness for imbalance reduction

As most object detectors rely on dense candidate samples to cover objects, they have always suffered from the extreme imbalance between very few foreground samples and numerous background samples during training, i.e., the foreground-background imbalance. Although several resampling and reweighting schemes (e.g., OHEM, Focal Loss, GHM) have been proposed to alleviate the imbalance, they are usually heuristic with multiple hyper-parameters, which is difficult to generalize on different object detectors and datasets. In this paper, we propose a novel Residual Objectness (ResObj) mechanism that adaptively learns how to address the foreground-background imbalance problem in object detection. Specifically, we first formulate the imbalance problems on all object classes as an imbalance problem on an “objectness” class. Then, we design multiple cascaded objectness estimators with residual connections for that objectness class to progressively distinguish the foreground samples from background samples. With our residual objectness mechanism, object detectors can learn how to address the foreground-background problem in an end-to-end way, rather than rely on hand-crafted resampling or reweighting schemes. Extensive experiments on the COCO benchmark demonstrate the effectiveness and compatibility of our method for various object detectors: the RetinaNet-ResObj, YOLOv3-ResObj and FasterRCNN-ResObj achieve relative 3%∼4% Average Precision (AP) improvements compared with their vanilla models, respectively.
Quotidian use of new media and sociocultural change in contemporary Kenya

This article examines how ordinary people in rural and urban areas in Kenya ascribe meaning to their daily use of information and communication technologies (ICTs) including media in ever changing communication ecologies and how the use of new ICT interrelates with processes of sociocultural change in everyday life. Taking as point of departure a semi-ethnographic social constructivist approach based on observations and semi-structured life world interviews the study presents an exploratory analytical bricolage around themes like power, knowledge and gender. The findings identify a simultaneous and seamless integration of the different new media that lead to significant open-ended processes of sociocultural change characterized by complexities, ambiguities, continuities, ruptures and inertia rather than dramatic irreversible changes. These findings call for a new paradigm in media studies focusing on communication ecologies and everyday uses of ICT where continuity and inertia are more seriously taken into accounts in processes of sociocultural change.
Social Security and Privacy for Social IoT Polymorphic Value Set: A Solution to Inference Attacks on Social Networks

Social Internet of Things (SIoT) integrates social network schemes into Internet of Things (IoT), which provides opportunities for IoT objects to form social communities. Existing social network models have been adopted by SIoT paradigm. The wide distribution of IoT objects and openness of social networks, however, make it more challenging to preserve privacy of IoT users. In this paper, we present a novel framework that preserves privacy against inference attacks on social network data through ranked retrieval models. We propose PVS, a privacy-preserving framework that involves the design of polymorphic value sets and ranking functions. PVS enables polymorphism of private attributes by allowing them to respond to different queries in different ways. We begin this work by identifying two classes of adversaries, authenticity-ignorant adversary, and authenticity-knowledgeable adversary, based on their knowledge of the distribution of private attributes. Next, we define the measurement functions of utility loss and propose PVSV and PVST that preserve privacy against authenticity-ignorant and authenticity-knowledgeable adversaries, respectively. We take into account the utility loss of query results in the design of PVSV and PVST. Finally, we show that PVSV and PVST meet the privacy guarantee with acceptable utility loss in extensive experiments over real-world datasets.
Saliency Detection via Topological Feature Modulated Deep Learning

The topological feature in an image, such as connectivity and adjacency, plays an important role in eye fixation detection. However, due to the scalar and additive nature of neurons to aggregate the node values in a local neighborhood, it is hard for convolutional neural networks (CNNs) to directly obtain and model the topological feature. Thus we adopt the topological feature which is pre-trained in accordance with the relationship between the figure and ground. Then we add a topological feature modulated convolutional layer into CNNs. By this way, the topological feature is automatically modulated by the deep features and well modeled by the CNNs. Experimental results show the proposed method surpasses the state-of-the-art by a big margin.
Professional Training of Teachers in the United States as an Example for Improving the Professionalism and Competence of Pedagogues in Ukraine

The process of reforming of the system of compulsory secondary education in Ukraine challenges teachers and determine new requirements for them. The study and analysis of teachersâ€™ professional education and training in other countries are one of the main ways to settle strategic directions of pedagogical education modernisation and thus to improve the quality of education in Ukraine. The national education systems of Ukraine and the USA are functionally common despite some differences in cultural and social aspects. This makes it possible to use the experience of the USA in reforming the system of professional preparation of teachers in Ukraine and improving their educational and professional qualifications. The study aims to make a comparative analysis of professional education and training of teachers in the USA from professionalism and competence and propose a way to adjust the most successful American experience to Ukrainian conditions. The study applies a comparative-historical method to compare socio-pedagogical phenomena and views. The structural method makes it possible to consider the pedagogical process based on system components of theory and practice of professional education of teachers. The prognostic method is used to ascertain the successful experience of the USA and outline the prospects of its implementation in Ukraine. Our research concludes that the system of professional preparation of teachers should be improved in order to increase the educational potential of the country. The study reveals a significant contrast between academic competence and professional qualification of teachers. It is determined that the objectives for professional preparation of secondary school teachers are the following: improving teaching competence, the combination of teaching practice and pedagogical theory and integration of pedagogical subjects and subjects of specialisation.
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder

Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP).Given an observed event, it is trivial for human to infer its intents and effects, while this type of If-Then reasoning still remains challenging for NLP systems.To facilitate this, a If-Then commonsense reasoning dataset Atomic is proposed, together with an RNN-based Seq2Seq model to conduct such reasoning.However, two fundamental problems still need to be addressed: first, the intents of an event may be multiple, while the generations of RNN-based Seq2Seq models are always semantically close; second, external knowledge of the event background may be necessary for understanding events and conducting the If-Then reasoning.To address these issues, we propose a novel context-aware variational autoencoder effectively learning event background information to guide the If-Then reasoning.Experimental results show that our approach improves the accuracy and diversity of inferences compared with state-of-the-art baseline methods.
PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

Automatic digital pressure gauge calibration is challenging due to various unconstrained conditions.Although existing CNN-RNN based methods have been almost perfect on scene text recognition, they fail to perform well on digital pressure gauge calibration that requires to be extremely computationefficient and accurate.In this paper, we propose a light weight fully convolutional sequence recognition network for fast and accurate digital Pressure Gauge Calibration (PGC-Net).PGC-Net integrates feature extraction, sequence modelling and transcription into a unified framework.Experimental results show that PGC-Net runs 28 fps on CPU with 97.41% accuracy.Compared with previous methods, PGC-Net achieves better or comparable performance at lower inference time.Without bells and whistles, PGC-Net is capable of recognizing decimal points that usually appear in pressure gauge images, which evidently verifies the feasibility of PGC-Net.We collected a dataset that contains 17, 240 gauge images with annotated labels for automatic digital pressure gauge calibration.The dataset has been public for future research.
Improving Precision of Grammatical Error Correction with a Cheat Sheet

In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction for the restricted track of BEA 2019 Shared Task on GEC.Our results show that phrases directly extracted from GEC corpora outperform phrases from a statistical machine translation phrase table by a large margin.Appending er-ror+context phrases to the original GEC corpora yields comparably higher precision.We also explore the generation of artificial syntactic error sentences using error+context phrases for the unrestricted track.The additional training data greatly facilitates syntactic error correction (e.g., verb form) and contributes to better overall performance.
Multi-View Reinforcement Learning

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments.
Evaluating a Spoken Dialogue System for Recording Systems of Nursing Care

Integrating speech recondition technology into an electronic health record (EHR) has been studied in recent years. However, the full adoption of the system still faces challenges such as handling speech errors, transforming raw data into an understandable format and controlling the transition from one field to the next field with speech commands. To reduce errors, cost, and documentation time, we propose a dialogue system care record (DSCR) based on a smartphone for nursing documentation. We describe the effects of DSCR on (1) documentation speed, (2) document accuracy and (3) user satisfaction. We tested the application with 12 participants to examine the usability and feasibility of DSCR. The evaluation shows that DSCR can collect data efficiently by achieving 96% of documentation accuracy. Average documentation speed was increased by 15% (P = 0.012) compared to traditional electronic forms (e-forms). The participants' average satisfaction rating was 4.8 using DSCR compared to 3.6 using e-forms on a scale of 1-5 (P = 0.032).
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates

We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation. The two methods are known to achieve complementary bias-variance trade-off properties, with TD tending to achieve lower variance but potentially higher bias. In this paper, we argue that the larger bias of TD can be a result of the amplification of local approximation errors. We address this by proposing an algorithm that adaptively switches between TD and MC in each state, thus mitigating the propagation of errors. Our method is based on learned confidence intervals that detect biases of TD estimates. We demonstrate in a variety of policy evaluation tasks that this simple adaptive algorithm performs competitively with the best approach in hindsight, suggesting that learned confidence intervals are a powerful technique for adapting policy evaluation to use TD or MC returns in a data-driven way.