PRINCIPLES OF ORGANIZATIONAL CO-EVOLUTION OF BUSINESS AND IT: A COMPLEXITY PERSPECTIVE

The Business and IT Co-evolution (BITC) is a growing concern for researchers and practitioners alike. Extant literature on implementation and management of BITC is still in infancy and lacks especially empirical guidelines. This paper makes two contributions to the study of BITC. First, we summarize and systematically organize 10 BITC principles from prior literature to guide management efforts. Second, we build a system dynamics model based on the 10 principles to apply these principles as a means to improve the BITC management. The model embraces the emergent behaviors driven by the interactions of business and IT, and guides the BITC governance shaped by the principles. The development of this model forms a necessary step towards suggesting guidance how to implement BITC in companies. The paper also shows the capability of a system dynamic method to capture some of the holistic behaviors that emerge from implementing the 10 principles

Quality problems and countermeasures in construction process

Quality is the life of architecture. Without quality there is nothing. Engineering projects have the characteristics of large investment and long construction period, so the quality of construction projects must be strictly controlled. The control of construction quality of engineering projects is the quality control of the whole process and the participation of all employees.It is the implementation of construction engineering quality regulations and mandatory standards, the correct configuration of construction production management elements and the use of scientific management methods to achieve the expected use function of engineering projects And quality standards, deliver the owner a satisfactory quality project. Most of the quality problems of construction projects appear in the construction stage. Therefore, we must strictly control the quality in project construction, strengthen the whole process management from the organization and management, find a project quality management system suitable for China's national conditions, and be able to eliminate the hidden quality hazards in the project in time to ensure the project The construction project can meet the target requirements, and the project quality can be effectively controlled.This article mainly analyzes the current problems affecting the construction quality, combined with the actual analysis, and then find some countermeasures to solve the problem

Joint Neural Collaborative Filtering for Recommender Systems

We propose a J-NCF method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization, which takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-word datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.Comment: 30 page

Multiresolution Feature Guidance Based Transformer for Anomaly Detection

Anomaly detection is represented as an unsupervised learning to identify deviated images from normal images. In general, there are two main challenges of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of anomalies. In this paper, we propose a multiresolution feature guidance method based on Transformer named GTrans for unsupervised anomaly detection and localization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on ImageNet is developed to provide surrogate labels for features and tokens. Under the tacit knowledge guidance of the AGN, the anomaly detection network named Trans utilizes Transformer to effectively establish a relationship between features with multiresolution, enhancing the ability of the Trans in fitting the normal data manifold. Due to the strong generalization ability of AGN, GTrans locates anomalies by comparing the differences in spatial distance and direction of multi-scale features extracted from the AGN and the Trans. Our experiments demonstrate that the proposed GTrans achieves state-of-the-art performance in both detection and localization on the MVTec AD dataset. GTrans achieves image-level and pixel-level anomaly detection AUROC scores of 99.0% and 97.9% on the MVTec AD dataset, respectively

Describing coevolution of business and IS alignment via agent-based modeling

The coevolution of business and IS alignment is a growing concern for researchers and practitioners alike. Extant literature on describing and modeling the coevolution is still in infancy, which makes it hard to capture the complexity and to offer reasonable decisions in the evolution of organizations. This paper focuses on the actors’ behaviors, and explores their emergent effects on the holistic alignment. We build an agent-based model to describe the complex alignment landscape and to improve the coevolution governance. The model embraces the emergent behaviors shaped by the interactions of business and IS agents, and guides the coevolution of alignment driven by the external changes. The development of this model forms a necessary step towards suggesting guidance how to analyze and implement coevolution in companies. The paper also shows the capability of an agent-based model to capture some of the emergent behaviors that emerge from bottom-level behaviors

A Co-evolutionary Perspective on Business and IT Alignment: A Review and Research Agenda

Business and IT Alignment (BITA) has received a growing attention during the last decades. Due to increasing environmental and organizational complexities, a co-evolutionary perspective has emerged recently to understand and to control the dynamics in BITA. The Business and IT Co-evolution (BITC) aims to coordinate business and IT through continuous adaptation and learning. A series of BITC studies have been conducted since the 2000s. This study provides an organized review of the current knowledge of this area. Its contribution is threefold: 1) organizing extant literature on BITC, 2) revealing knowledge gaps, and 3) proposing a research agenda

Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification

Text representation can aid machines in understanding text. Previous work on text representation often focuses on the so-called forward implication, i.e., preceding words are taken as the context of later words for creating representations, thus ignoring the fact that the semantics of a text segment is a product of the mutual implication of words in the text: later words contribute to the meaning of preceding words. We introduce the concept of interaction and propose a two-perspective interaction representation, that encapsulates a local and a global interaction representation. Here, a local interaction representation is one that interacts among words with parent-children relationships on the syntactic trees and a global interaction interpretation is one that interacts among all the words in a sentence. We combine the two interaction representations to develop a Hybrid Interaction Representation (HIR). Inspired by existing feature-based and fine-tuning-based pretrain-finetuning approaches to language models, we integrate the advantages of feature-based and fine-tuning-based methods to propose the Pre-train, Interact, Fine-tune (PIF) architecture. We evaluate our proposed models on five widely-used datasets for text classification tasks. Our ensemble method, outperforms state-of-the-art baselines with improvements ranging from 2.03% to 3.15% in terms of error rate. In addition, we find that, the improvements of PIF against most state-of-the-art methods is not affected by increasing of the length of the text.Comment: 32 pages, 5 figure

COMPARISON OF SOME BIOMECHANICS PARAMETERS OF BREASTSTROKE SWIMMERS IN FLUME AND SWIMMING POOL

The purpose of this study was to compare some parameters of breaststroke swimmers in a swimming pool with those for breaststroke swimming in the flume, to search whether there is some difference between two test circumstances of swimming pool and flume in technical parameters. Four male breaststroke swimmers aged between16 and 18 years were studied. Subjects were required to swim in a 25m pool for best or familiar stroke length and tried to decrease stroke rate, and performed at three minute intervals at speeds ranging from 70% to 100% of the best performance of individuals. Subjects were familiarized to flume swimming on the day prior to be tested, then swam at the same speed based upon conversion from pool in swimming flume. According to testing we found that stroke rate, stroke length and efficiency index for pool and swimming flume at corresponding speeds were similar. Of course, there was as expected significant difference in the stroke rate and stroke length used between subjects to swim at the various speeds