Reviewerly/exHarmony huggingface dataset:
Impact of Development Perspective of HRM and Lesson Learned System of Knowledge Management on Project Success Through Project Management Competency Retention

This study was conducted to find out the impact of development perspective of Human Resource Management (HRM), and lesson learned system of knowledge management (KM) on project success with the mediation of project management competency retention (PMCR). The data was collected from 310 employees of the telecommunication industry. The results confirmed that the development perspective of HRM and lesson learned through the system of KM has a significant relationship with PMCR and project success. The intervening role of PMCR is also proved between lesson learned the system of KM and project success. The study presents new evidence to organizations for enhancing competency retention of employees and achieve project success.
Analysis of Braille translation software

Assistive computer programs are one of the most invaluable technologies for people who are blind or visually impaired -this category of inclusive software can be effectively utilized for both educational purposes and everyday use, enabling its users to work with standard windowed graphical user interfaces.In this article, research publications related to software designed to assist people with visual impairments are reviewed and analyzed.Particular note is given to software that converts plaintext or rich text documents into Braille files that can be used by tactile printers or displays; otherwise known as 'Braille translators'.Additionally, the feature sets both commercial and non commercial or opensource Braille translators are analyzed with an emphasis on their support of a Ukrainian Braille alphabet, ability to modify translation tables or internal translation logic and other functional or nonfunctional requirements.
Spatial and Semantic Relations for Pedestrian Attribute Recognition

This paper addresses the problem of pedestrian attribute recognition. Previous works typically treat the different attributes independently with each other, without considering possible dependencies between them, or just take semantic relations or spatial relations into consideration. In our work, we propose an end-to-end learning framework combining with a subnet for multi-task classification to take both spatial and semantic relations into account, which proves to be more accurate and effective. Our work can not only deal with binary attributes, but also multi-class attributes in a single network, overcoming the drawbacks in many methods that can only recognize the binary attributes in joint-training. Experiments have been carried out on several benchmarks and positively demonstrated the superiority of our method.
INVESTIGATION OF HEAVY-MINERAL DEPOSITS USING MULTISPECTRAL SATELLITE IMAGERY IN THE EASTERN COASTAL MARGIN OF BANGLADESH

Sea beaches are always a good source of heavy minerals around the world.Cox's Bazar has 120 km of unbroken sandy sea beach.The study includes the sea beaches of 5 upazilas (Moheshkhali, Kolatoli, Ramu, Ukhia, Teknaf) in Cox's bazaar district for studying the concentration, identification and investigation of heavy mineral deposits along the sandy beaches.This study has concentrated on the physical properties, identification, and abundance of the heavy minerals fraction in sediments collected from the study area.Satellite imagery Landsat-8 OLI was used for remote sensing verifications.The image processing and crucial analysis carried out using Environment for Visualizing Images (ENVI), Arc GIS and Erdas Imagine software.Coastal areas adjacent to Teknaf upazila has a very significant amount of heavy mineral reserves, almost 16%.The study has identified around nineteen variety of heavy minerals from the collected samples in the study area.Ilmenite, Kyanite, Garnet, Rutile, Zircon, Magnetite, Augite, Hornblende, Enstatite, Epidote, Andalusite, Hypersthene, Diopside and Cassiterite have been found the most abundant in the entire study area.The Coastline of Bangladesh was surveyed completely to map the heavy minerals which are potential resources for our national economy.
Verification in theory and in the sciences

In this work, we undertake the task of laying out some basic considerations towards straightening out the foundations of an abstract logical system. We venture to explain what theory is as well as what is not theory, to discriminate between the roles of truth in theory and in reality, as well as to open the road towards clarifying the relationship between theory and the real world. Etymological, cultural and conceptual analyses of truth are brought forth in order to reveal problems in modern approaches and to set the stage for more consistent solutions. One such problem addressed here is related to negation per se, to its asymmetry towards affirmative statements and to the essential ramifications of this duality with respect to the common perceptual and linguistic aspects of words indicating concepts akin to truth in various languages and to attitudes reflected and perpetuated in them and to their consequent use in attempted informal or formal logic and its understanding. Finally, a case study invoking the causes or “causes” of gravity both clarifies and reinforces the points made in this paper.
Dynamic Obstacle Avoidance Planning for Manipulators of Home

With the maturity of robotics and serious aging situation, home service robots have been prevailing around the world, which requires the robotic arm of home service robots to be flexible for obstacle avoidance to work in complex and changing home environment. Based on the actual requirements of home service robots, this paper proposes an obstacle avoidance planning algorithm based on improved artificial potential field method for dynamic obstacle avoidance of redundant manipulators. The velocity of the target object and obstacle were introduced into the end-planned field structure to plan a collision-free path for the end of the arm and track the dynamic target point. Meanwhile, the deviation speed of the arm member in the obstacle repulsive field was added to ensure obstacle avoidance. Finally, the effectiveness of the algorithm was verified in the simulation environment.
Progressive Semantic Image Synthesis via Generative Adversarial Network

Semantic image synthesis via text description is a desirable and challenging task, which requires more protection of the text irrelevant content in the original image. Existing methods directly modify the original image, which become more difficult when encountering high resolution image, and the generated images are also blurred and lack in detail. This paper presents a novel network architecture to progressively manipulate an image starting from low-resolution, while introducing the original image of corresponding size at different stages with our proposed union module to avoid losing of detail. And the progressive design of the network allows us to modify the image from coarse into fine. Compared with the previous methods, our new method can successfully manipulate a high resolution image and generate a new image with background protection and fine details. The experimental results on CUB-200-2011 dataset show that the proposed approach outperforms existing methods in terms of image detail, background protection and high resolution generation.
Challenges of Collection Management: Analysis, Staffing, and Space

ABSTRACT Academic libraries face limitations of space, staffing, budget, and other factors, but still must maintain their collections responsibly. Collection maintenance with the goal of reclaiming space requires both solid data analysis and sufficient staff to execute projects. Presenters with perspectives from Siena College, Rockhurst University, and Sam Houston State University discuss their experiences in facing the challenges of analyzing data and managing workflows for current and potential removal projects. An OCLC Regional Library Services Manager describes their product Green Glass and its involvement with the project at Sam Houston State University.
WALLACE: Weakly Supervised Learning of Deep Convolutional Neural Networks With Multiscale Evidence

This paper presents WALLACE, a new framework of deep convolutional neural networks, which perform ConvNet's pyramidal feature hierarchy for weakly supervised learning.Most prior works rely on the image pyramid or network ensemble, which is both complicated and usually expensive.Instead, WALLACE is a more simple single-stage network that can predict objects present and location in an image without multiple rescale.Our model is trained efficiently using only global image-level labels, and it could generate meaningful multi-scale semantic feature maps by only one evaluation.Furthermore, a novel constrain-to-highlight loss is proposed to balances region selection among hierarchical feature maps, which additional improve model performance.Extensive experiments on object classification and weakly supervised pointwise object localization show that WALLACE achieves state-of-the-art results on the VOC 2007 and VOC 2012 benchmark without bells and whistles.
Video Alignment Using Bi-Directional Attention Flow in a Multi-Stage Learning Model

Recently, deep learning techniques have contributed to solving a multitude of computer vision tasks. In this paper, we propose a deep-learning approach for video alignment, which involves finding the best correspondences between two overlapping videos. We formulate the video alignment task as a variant of the well-known machine comprehension (MC) task in natural language processing. While MC answers a question about a given paragraph, our technique determines the most relevant frame sequence in the context video to the query video. This is done by representing the individual frames of the two videos by highly discriminative and compact descriptors. Next, the descriptors are fed into a multi-stage network that is able, with the help of the bidirectional attention flow mechanism, to represent the context video at various granularity levels besides estimating the query-aware context part. The proposed model was trained on 10k video-pairs collected from “YouTube”. The obtained results show that our model outperforms all known state of the art techniques by a considerable margin, confirming its efficacy.
A Range-Independent Disparity-Based Calibration Model for Structured Light Pattern-Based RGBD Sensor

Consumer-grade RGBD sensors that provide both colour and depth information have many potential applications, such as robotics control, localization, and mapping, due to their low cost and simple operation. However, the depth measurement provided by consumer-grade RGBD sensors is still inadequate for many high-precision applications, such as rich 3D reconstruction, accurate object recognition and precise localization, due to the fact that the systematic errors of RGB sensors increase exponentially with the ranging distance. Most existing calibration models for depth measurement must be carried out with different distances. In this paper, we reveal the mechanism of how an infrared (IR) camera and IR projector contribute to the overall non-centrosymmetric distortion of a structured light pattern-based RGBD sensor. Then, a new two-step calibration method for RGBD sensors based on the disparity measurement is proposed, which is range-independent and has full frame coverage. Three independent calibration models are used for the calibration for the three main components of the RGBD sensor errors: the infrared camera distortion, the infrared projection distortion, and the infrared cone-caused bias. Experiments show the proposed calibration method can provide precise calibration results in full-range and full-frame coverage of depth measurement. The offset in the edge area of long-range depth (8 m) is reduced from 86 cm to 30 cm, and the relative error is reduced from 11% to 3% of the range distance. Overall, at far range the proposed calibration method can improve the depth accuracy by 70% in the central region of depth frame and 65% in the edge region.
Identification of Bifacial Components in Middle Paleolithic Techno-Complexes (Based on the Chagyrskaya Cave Assemblages)

Previously, occasional bifacial tools found in different industrial variants of the Altai Middle Paleolithic were not considered as cultural markers that could be used to differentiate the technological/cultural variants. They rather were a bright but situational manifestation of the typological variability, especially in the case of the Sibiryachikha assemblages. Purpose. The article discusses various research approaches used to determine and evaluate the bifacial component in the Middle Paleolithic lithic assemblages, namely attributive analysis with a set of specific attributes, scar-pattern analysis and experimental modelling. Results. As a result of recent studies at the site Chagyrskaya Cave, the key-site of Sibiryachikha, we found out that all the bifaces were made using plano-convex technology. In the Chagyrskaya Cave assemblage all stages of bifacial production have been noticed? including pre-forms, bifacial tools and tools made on bifacial thinning flakes, accompanied by numerous bifacial thinning flakes and bifacial thinning chips. Re-investigation of the Okladnikov assemblage should bring a new, previously unknown series of technical spalls related to the bifacial plano-convex technology. A similar situation is with Karabom complexes, where all bifacial tools are made using bi-convex bifacial technology. Thus, criteria for technological distinction of bifacial production are of special importance. Conclusion. Our experiments have shown that the proportion of chips associated with bifacial production is much higher than it can be determined while analyzing archaeological assemblages. Taking in account the new data on bifacial technologies in the region, we conclude that variability of Middle Paleolithic complexes has become more complex. To evaluate the bifacial component in Paleolithic assemblages, all stages of bifacial flaking should be documented, including bifacial pre-forms, technical spalls related to bifacial reduction sequence, chips, blanks which demonstrate bifacial flaking errors and tools made on bifacial thinning flakes and bifacial tools. A complete set of bifacial production is present at the Chagyrskaya Cave assemblage due to the fact that the cave was constantly visited and had a sufficiently long habitation cycle as a source of raw materials. In the assemblage, a complete sequence of lithic raw material exploitation was processed. Taking into account the fact that Chagyrskaya Cave and Okladnikov Cave are associated only with Neanderthal remains, it can be assumed that bifacial plano-convex technology in the Middle Paleolithic of Altai is linked to Neanderthal population in the region.
Mobile Learning in Turkey: Trends, Potentials and Challenges

Several components of learning including learners, teachers, schools, learning methodologies, learning content and evaluation criteria are affected by changing technologies. Like other technology-based learning methods, mobile learning is also a product of a period in which new technologies and education are intertwined. As new learning and technologies become more personalized, learner-centered, connected, portable and ubiquitous, skills such as cooperation, communication, critical thinking and creativity start to stand out. Mobile learning has a potential to address these skills and better cater for today’s digital native learners thanks to the aforementioned affordances. This potential of mobile learning is the starting point for this study. The aim of this study is to see the trends of mobile learning over the years and to discuss its potentials and challenges. In line with this purpose, the graduate theses published in Turkish Higher Education Council Thesis Database between 2010 and 2019 were reviewed by content analysis method. The results of the study revealed that mobile learning in graduate studies offer the potentials to positively effect on academic achievement, facilitate positive attitude towards mobile learning, increase motivation and develop positive attitude towards the course. In addition, the results also demonstrated that mobile learning is time and place independent, easy, fun and helpful for vocabulary learning and increasing interaction. However, there were also challenges reported associated with mobile learning such as technical and infrastructural problems and lack of hardware and software used in mobile leaning tools.
MACHINE AS ONE PLAYER IN INDIAN COWRY BOARD GAME: BASIC PLAYING STRATEGIES

Cowry game is an ancient board game from India, also known as Chowka Bhara.It is a race game of chance and strategy for 2-4 players, in which playing pieces are moved around a square board according to the throw of special dice (cowry shells).This game involves decision-making under uncertainty and imprecision with multiple players, and therefore can be considered as an appropriate model for real-life problems that contain stochastic components.In this research, we propose and analyze a few basic playing strategies for Cowry game, and describe the framework for the implementation of these strategies.We also provide an experimental comparison of the proposed strategies to evaluate their performances.The comprehensive study of Cowry game presented in this work can be used to gain a better understanding of the game, and may result in the formulation and implementation of more advanced strategies.It can also serve as a basis for producing better artificial players in similar strategic race games.
Improving Small-Scale Pedestrian Detection Using Informed Context

Finding small objects is fundamentally challenging because there is little signal on the object to exploit. For the small-scale pedestrian detection, one must use image evidence beyond the pedestrian extent, which is often formulated as context. Unlike existing object detection methods that use adjacent regions or whole image as the context simply, we focus on more informed contexts exploiting and utilizing to improve small-scale pedestrian detection: firstly, one relationship network is developed to utilize the correlation among pedestrian instances in one image; secondly, two spatial regions, overhead area and feet bottom area, are taken as spatial context to exploit the relevance between pedestrian and scenes; at last, GRU [7] (Gated Recurrent Units) modules are introduced to take encoded contexts as input to guide the feature selection and fusion of each proposal. Instead of getting all of the outputs at once, we also iterate twice to refine the detection incrementally. Comprehensive experiments on Caltech Pedestrian [8] and SJTU-SPID [9] datasets, indicate that, with more informed context, the detection performance can be improved significantly, especially for the small-scale pedestrians.
IMPROVING THE VOCABULARY MASTERY THROUGH WORD-CONNECTION GAME

Many students face difficulties to express their mind, ideas and feelings because lack of vocabulary. This fact makes them loose the point of information and lead them to the thought that English is a difficult subject, till even frustration. This is why every student must master vocabulary, because when they do, it will be easy for them to master the four language skills. This reasearch aimed to find (1) whether the use of Word-Connection game significantly improved the students vocabulary mastery of the first grade students of English Education in Universitas Muslim Maros on academic year 2018/2019, and (2) their attitudes toward the use of this game. Method of this research was experiment with 2 classes as the subjects. It employed 30 numbers of vocabulary test then analyzed by using inferential statistic SPSS program 25.0 and 20 numbers of close-ended questions questionnaire to find out their attitude towards the use of Word-Connection game. Result of the calculation data said that the mean score improved significantly at the experiment group from 3.476 to 8.356, but at the control group only from 3.950 to 4.550. Standard deviation at experiment group decreased from 1.2444 to 0.9238 and at control group from 1.5472 to 1.7604. Range on normalized gain score at the experiment group was 0.37, smaller than control group which was 0.43. Standard deviation on normalized gain score at experiment group was 0.115511 whereas at control group was 0.11075. Furthermore, data which were collected from questionnaire indicated that the students had positive attitude toward the use of Word-Connection game with mean score 82.68. Findings above concludes that the use of Word-Connection game can improve the students vocabulary mastery at the experiment group.
A Sequence-to-Sequence Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation

The prevalent approaches for the task of Chinese word segmentation almost rely on the Bi-LSTM neural network. However, the methods based the Bi-LSTM have an inherent drawback: the Vanishing Gradients, which cause the little efficient in capturing the faraway character information of a long sentence for the task of word segmentation. In this work, we propose a novel sequence-to-sequence transformer model for Chinese word segmentation, which is premised a type of convolutional neural network named temporal convolutional network. The model uses the temporal convolutional network to construct an encoder, and uses a fully-connected neural network to build a decoder, and applies the Viterbi algorithm to build an inference layer to infer the final result of the Chinese word segmentation. Meanwhile, the model captures the faraway character information of a long sentence by adding the layers of the encoder. For achieving a superior result of word segmentation, the model binds the Conditional Random Fields model to train parameters. The experiments on the Chinese corpus show that the performance of Chinese word segmentation of the model is better than the Bi-LSTM model, and the model has a better ability to process a long sentence than the Bi-LSTM.
Orientation Robust Scene Text Recognition in Natural Scene

In recent years, scene text recognition has achieved significant improvement and various state-of-the-art recognition approaches have been proposed. This paper focused on recognizing text in natural photos of equipment nameplates, which has wide applications in industrial automations. This task only receives little attentions in previous works. The challenge of this problem comes from multi-orientation, curved, noisy and blurry text patches in equipment nameplates. To address this problem, we propose a deep model for text recognition in multi-oriented nameplates, namely, Orientation Robust Scene Text Recognition (ORSTR). Specifically, our model employs a rectification module to transform curved, distorted or multi-orientation text to near-horizontal text with a carefully designed rectification module. Once the near-horizontal text has been generated, recognition network will output the predictions of text patches. Our scene text recognition model achieves 90 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</inf> 8% recognition accuracy on equipment nameplate dataset which outperforms previous scene text recognition model (CRNN) about 0 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</inf> 8%. Several extensive experiments have been conducted to verify the effectiveness of our model.