Reviewerly/exHarmony huggingface dataset:
Efficient Temporal Piecewise-Linear Numeric Planning With Lazy Consistency Checking

Temporal planning often involves numeric effects that are directly proportional to their action's duration.These include continuous effects, where a numeric variable is subjected to a rate of change while the action is being executed, and discrete duration-dependent effects, where the variable is updated instantaneously but the magnitude of such change is computed from the action's duration.When these effects are linear, state-of-theart temporal planners often make use of Linear Programming to ensure that these numeric updates are consistent with the chosen start times and durations of the plan's actions.This is typically done for each evaluated state as part of the search process.This exhaustive approach is not scalable to solve realworld problems that require long plans, because the linear program's size becomes larger and slower to solve.In this work we propose techniques that minimise this overhead by computing these checks more selectively and formulating linear programs that have a smaller footprint.The effectiveness of these techniques is demonstrated on domains that use a mix of discrete and continuous effects, which is typical of real-world planning problems.The resultant planner also outperforms most state-ofthe-art temporal-numeric and hybrid planners, in terms of both coverage and scalability.Impact Statement-While current temporal planners perform well on benchmark domains that feature continuous or durationdependent numeric effects, real-world problems are often more challenging to solve.This is mostly due to the complex interactions between the temporal and numeric trajectory dynamics, together with the size of the plans that such planning problems typically require.The techniques proposed in this work take advantage of the structure of such domains to find a plan more efficiently.Empirical testing shows a significant improvement on the time spent on solving linear programs during the search for a plan, with an overall improvement on the total planning time for problems that exhibit such characteristics.Furthermore, the proposed planner outperforms all other state-of-the-art temporalnumeric planners in terms of both coverage and scalability.This is a significant achievement that enables A.I. Planning technology to solve a wider range of problems for industry applications.
A simulated annealing-based algorithm for selecting balanced samples

Abstract Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.
A structured light image quality evaluation method based on no-reference quality assessment

Abstract Affected by noise, color, and light blocking, there are some large error data contained in the results of structured light 3D measurement. In the absence of effective criterion, the data can only be recognized artificially. In this paper, a criterion based on Human Vision System (HVS), which can be applied to recognize the large error data by evaluating the noise variance level of the stripe images, and a method used to improve the level of measurement accuracy are proposed. According to the proposed method, the no-reference quality assessment is used to evaluate the quality of stripe images. With the noise evaluation coefficient, the large error data can be identified automatically. The experimental results show that this method is efficient in detecting large error data in the stripe images.
Privacy Protection and Secure Transmission of Smart Meter Data

Energy companies can collect users' energy consumption information through smart meters, and provide users with various services based on these data information. However, the data collected includes sensitive personal information, which may lead to privacy leakage. Currently, existing schemes still do not solve the security, utility and computational efficiency of data privacy information well. Therefore, this paper proposes a system to protect the privacy and transmission of smart meter data, that is PSSM, to protect the privacy of user data and ensure the integrity of data transmission. In PSSM, we combine wavelet transform with Gaussian Distributed Differential Privacy (GDDP) to protect the confidentiality of data, and for the first time use Schnorr signature technology to verify the correctness of data. At the same time, the data aggregation module is used to collect the data of all users in a specific area, and then randomly send each user's data to the Data Processing Units (DPUs) for data processing. Experimental simulation show that the scheme is superior to the existing scheme in terms of utility, security and computational efficiency.
Incoherent Sentence Detection in Scientific Articles in Russian and English

Text coherence is an important factor that often gets overlooked by novice writers. Incoherence in academic writing directly affects both the reading experience and the comprehensibility of the articles. This paper introduces and describes a method for detecting incoherence in academic writing. The method utilized a fine-tuned BERT model in conjunction with a graph clustering algorithm. We benchmarked the method against baseline models on Discordant Sentence Detection using Time-travel dataset, and the results showed that the proposed method outperformed baseline models in terms of F1-score. Afterwards, the method was tested on corpora of Russian and English scientific articles in order to assess its proficiency in Narrative Incoherence Detection when applied to the paper's main research subject: academic writing. The paper's proposed method achieved a decent F1 of over 0.65 in Discordant Sentence Detection. For future work, our biggest goal is to further refine the method and be able to effectively deploy it on existing systems for reviewing academic corpora.
Development of Domain-Specific Lexicon for Aspect-Based Sentiment Analysis

Aspect term extraction is one of the main subtasks in aspect-based sentiment analysis. An aspect extraction method based on the Sequential Covering algorithm successfully used a list of aspects and opinion words to improve the performance of aspect extraction. The aspect and opinion list used in the method is crafted manually, costing a significant amount of time and effort. To ease the effort, we proposed a way to automatically build the aspect and opinion list. We also modified the scope of the word list to a larger domain. The resulting word list is therefore called domain-specific lexicon. In this paper, we used word embedding technique to develop domain-specific lexicons of size 1000. The data used in the development of domain-specific lexicon were collected from review websites using a focused web crawler. The resulting domain-specific lexicons were used in the modified Sequential Covering method. We used a total of 3,124 review sentences from the digital camera, handphone, and restaurant domain to evaluate the performance of the modified Sequential Covering method. Experimental results showed that this method managed to yield better F1 scores than the baseline Sequential Covering method. The best F1 scores for each dataset were as follows: 0.645 for Nikon Coolpix 4300, 0.581 for Canon G3, 0.629 for Nokia 6610, and 0.705 for ABSA16_Restaurants_Train_SB1.
Artificial Intelligence at Present and Tomorrow

Abstract Artificial Intelligence, part of Information Technology, is currently the hottest spot in Information technology the main research subject of which is knowledge and the main research directions are methodologies, methods, and algorithms for knowledge acquisition, knowledge representations, and knowledge usage. The paper describes the core scientific content of these directions, outlines their state-of-the-arts, and analyzes briefly new trends and prospects for research and development in Artificial Intelligence for the forthcoming years.
Region Specific Automatic Quality Assurance For MRI-Derived Cortical Segmentations

Quality control (QC) is a vital step for all scientific data analyses and is critically important in the biomedical sciences. Image segmentation is a common task in medical image analysis, and automated tools to segment many regions from human brain MRIs are now well established. However, these methods do not always give anatomically correct labels. Traditional methods for QC tend to reject statistical outliers, which may not necessarily be inaccurate. Here, we make use of a large database of over 12,000 brain images that contain 68 parcellations of the human cortex, each of which was assessed for anatomical accuracy by a human rater. We trained three machine learning models to determine if a region was anatomically accurate (as 'pass', or 'fail') and tested the performance on an independent dataset. We found good performance for the majority of labeled regions. This work will facilitate more anatomically accurate large-scale multi-site research.
Preservations and rescue of the Omagua language through the use of a mobile application in homes in the San Joaquin de Omaguas community in Loreto, Peru

This paper proposes the design and development of a mobile application aimed at preserving and rescuing the Omagua language, one of the 44 native languages of the Peruvian Amazon that is facing extinction problems due to the few speakers it has. This work was conducted in the town of San Joaquin de Omaguas, located 64 km from the city of Iquitos. The validation tests about the usefulness and usability of the application were conducted through a control group, which applied the traditional teaching methodology, and an experimental group, who made use of the mobile application. The results of this work allowed us to contrast that the mobile application does have the utility of serving as a learning tool, allowing to rescue and preserve the Omagua language.
Mining online review for predicting sales performance

Information technology field getting raised day-by-day and due to the growth of internet associated services the online shopping and social media is become more popular as well as attain a great development. The involvement of users in social media as well as e-commerce sites are increasing a lot and all are willing to share their opinions as post in the social medium as well as the increasing volume of reviews raising several ideologies over both commercial and non-commercial firms. The commercial vendors think like for obtaining the reviews from customers to identify the sales level of the product as well as the vendors think like based on these reviews identify the best product as well as provide the best service to customers accordingly. So, these reviews are most important to deal with as well as through this review mining logic sales and its associated performance will be improved in drastic level. These kinds of reviews estimation factor is called as Sentiment Analysis, in this paper a new sentimental analysis logic is introduced called as Sales and Performance of Products Analysis based Sentimental PLSA (S-PLSA). By using this formulation, the review of the product is considered and the sentimental vectors are measured by using complex sentimental estimation logic based on the reviews generated by the user. The logic is estimated with the help of training association and model creation principles, these models are cross-validated with the help of real-time live data review reports. The experimental results prove the proposed approach of SPPA provides good response in results as well as the Quality-of-Service to customers to prove the effectiveness of the proposed algorithm called SPPA.
Integrating Multiple Policies for Person-Following Robot Training Using Deep Reinforcement Learning

Given a training environment which follows Markov decision process for a specific task, a deep reinforcement learning (DRL) agent is able to find possible optimal policies which map states of the environment to appropriate actions by repeatedly trying various actions to maximize training rewards. However, the learned policies cannot be reused directly in the training process for other new tasks resulting wasted precious time and resources. To solve this problem, we propose a DRL-based method for training an agent capable of selecting the appropriate policy for current state of the environment from a set of previously trained optimal policies for a given task which can be decomposed into other sub tasks. We implement our proposed method to a person-following robot task training that can be broken down into three sub tasks, namely: navigation, left attending, and right attending. Using the proposed method, the previously learned optimal navigation policy obtained from our previous work is integrated to attending policies which are trained in this study. We also introduce the use of weight-scheduled action smoothing which is able to stabilize actions generated by the agent in the attending task training. Our experiment results show that the proposed method is able to integrate all sub policies using the action smoothing method even though the navigation and the attending policies have dissimilar input structures, unalike output ranges, and are trained in different ways. Moreover, our proposed method shows better results compared to training from scratch and training using transfer learning strategy.
Face hallucination based on cluster consistent dictionary learning

Face hallucination is a super-resolution technique specially designed to reconstruct high-resolution faces from low-resolution faces. Most state-of-the-art algorithms leverage position-patch prior knowledge of human faces to better super-resolve face images. However, most of them assume the training face dataset is sufficiently large, well cropped or aligned. This paper, proposes a novel example-based face hallucination method, based on cluster consistent dictionary learning with the assumption that human faces have similar facial structures. In this method, the paired face image patches are firstly labelled as face areas including eyes, nose, mouth and other parts, as well as non-face areas without requiring the training face images cropped and aligned. Then, the training patches are clustered according their labels and textures. The cluster consistent dictionary is learned to represent the low-resolution patches and the high-resolution patches. Finally, the high-resolution patches of the input low-resolution face image can be efficiently generated by using the adjusted anchored neighbourhood regression. As utilizing the labelled facial parts prior knowledge, the proposed method represents more details in the reconstruction. Experimental results demonstrate that the authors' algorithm outperforms many state-of-the-art techniques for face hallucination under different datasets.
Towards efficient full 8-bit integer DNN online training on resource-limited devices without batch normalization

Huge computational costs brought by convolution and batch normalization (BN) have caused great challenges for the online training and corresponding applications of deep neural networks (DNNs), especially in resource-limited devices. Existing works only focus on the convolution or BN acceleration and no solution can alleviate both problems with satisfactory performance. Online training has gradually become a trend in resource-limited devices like mobile phones while there is still no complete technical scheme with acceptable model performance, processing speed, and computational cost. In this research, an efficient online-training quantization framework termed EOQ for abbreviation is proposed by combining Fixup initialization and a novel quantization scheme for the online training in resource-limited devices. Based on the proposed framework, we have successfully realized full 8-bit integer network training and removed BN in large-scale DNNs. Especially, weight updates are quantized to 8-bit integers for the first time. Theoretical analyses of EOQ utilizing Fixup initialization for removing BN have been further given using a novel Block Dynamical Isometry theory with weaker assumptions. Benefiting from rational quantization strategies and the absence of BN, the full 8-bit networks based on EOQ can achieve state-of-the-art accuracy and immense advantages in computational cost and processing speed. Experiments show that the 8-bit EOQ networks achieve 2.78%, 3.85%, and 4.31% accuracy improvements compared with existing full 8-bit integer networks in ResNet-18/34/50. At the same time, the 8-bit EOQ networks can improve the computing speed greatly, and decrease the power consumption and circuit area by about an order of magnitude compared with 32-bit floating-point vanilla networks. In addition to the huge advantages brought by quantization in convolution operations, 8-bit networks based on EOQ without BN can realize >66× lower in power, >13× faster in the processing speed compared with the traditional 32-bit floating-point BN in the inference process. What’s more, the design of deep learning chips can be profoundly simplified in the absence of unfriendly square root operations in BN. Beyond this, EOQ has been evidenced to be more advantageous in small-batch online training with fewer batch samples. In summary, the EOQ framework is specially designed for reducing the high cost of convolution and BN in network training, demonstrating a broad application prospect of online training in resource-limited devices.
Chinese investment and corruption in Africa

Chinese investment in Africa has rapidly expanded in recent years and garnered significant attention. There has been considerable concern that this investment will increase corruption in African states. However, there has been little academic scrutiny or examination of these claims. This paper proposes and tests the theory that the effect of FDI on corruption is dependent on the source country, specifically proposing and testing the hypothesis that Chinese FDI has a more detrimental effect on corruption than FDI from developed economies. By analyzing a random effects model with pooled cross-sectional, time-series data on corruption and foreign direct investment from 52 African countries from 2002 to 2012 I show that, contrary to the theoretical prediction, investment from Chinese sources does not have a significantly different effect on corruption than foreign investment from developed countries. Though Chinese investors are less deterred by high levels of corruption, their investment in more corrupt countries does not increase overall corruption levels.
Multi-frame Super-Resolution from Noisy Data

Obtaining high resolution images from low resolution data with clipped noise is algorithmically challenging due to the ill-posed nature of the problem. So far such problems have hardly been tackled, and the few existing approaches use simplistic regularisers. We show the usefulness of two adaptive regularisers based on anisotropic diffusion ideas: Apart from evaluating the classical edge-enhancing anisotropic diffusion regulariser, we introduce a novel non-local one with one-sided differences and superior performance. It is termed sector diffusion. We combine it with all six variants of the classical super-resolution observational model that arise from permutations of its three operators for warping, blurring, and downsampling. Surprisingly, the evaluation in a practically relevant noisy scenario produces a different ranking than the one in the noise-free setting in our previous work (SSVM 2017).
ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION

Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process.
Gravitational effects of scene information in object localization

Abstract We effortlessly interact with objects in our environment, but how do we know where something is? An object’s apparent position does not simply correspond to its retinotopic location but is influenced by its surrounding context. In the natural environment, this context is highly complex, and little is known about how visual information in a scene influences the apparent location of the objects within it. We measured the influence of local image statistics (luminance, edges, object boundaries, and saliency) on the reported location of a brief target superimposed on images of natural scenes. For each image statistic, we calculated the difference between the image value at the physical center of the target and the value at its reported center, using observers’ cursor responses, and averaged the resulting values across all trials. To isolate image-specific effects, difference scores were compared to a randomly-permuted null distribution that accounted for any response biases. The observed difference scores indicated that responses were significantly biased toward darker regions, luminance edges, object boundaries, and areas of high saliency, with relatively low shared variance among these measures. In addition, we show that the same image statistics were associated with observers’ saccade errors, despite large differences in response time, and that some effects persisted when high-level scene processing was disrupted by 180° rotations and color negatives of the originals. Together, these results provide evidence for landmark effects within natural images, in which feature location reports are pulled toward low- and high-level informative content in the scene.
Impact of Syntactical and Statistical Pattern Recognition on Prognostic Reasoning

This chapter provides insight on pattern recognition by illustrating various approaches and frameworks which aid in the prognostic reasoning facilitated by feature selection and feature extraction. The chapter focuses on analyzing syntactical and statistical approaches of pattern recognition. Typically, a large set of features have an impact on the performance of the predictive model. Hence, there is a need to eliminate redundant and noisy pieces of data before developing any predictive model. The selection of features is independent of any machine learning algorithms. The content-rich information obtained after the elimination of noisy patterns such as stop words and missing values is then used for further prediction. The refinement and extraction of relevant features yields in performance enhancements of future prediction and analysis.