The Organizational Change Dilemma Of ERP Implementation In A Small Manufacturing Company

ERP implementation in small and medium-sized businesses is especially challenging not only because of their shortage of budget and talents but also of the strong organizational and individual resistance to the overall organizational changes caused by the new technology.   This paper presents an original case of ERP implementation failure in a small manufacturing company and how we used the case to teach about organizational changes in a management class. Rather than leading a retrospective analysis of the case as many instructors would do with this type of change failure case study, we employed the four-step problem-solving case study approach to guide the students to identify the case problem, analyze its causes, prescribe and evaluate alternatives, and make a decision and develop detailed action plan to eventually solve the problem. Our purpose with such a thorough, quasi-experiential learning case study was to develop the students’ problem solving and decision making skills, particularly in understanding and leading organizational changes. Students received methodological, conceptual, practical, and technical learning benefits from the forward-looking, solution-focused case study of the small company’s ERP implementation failure

Rhizosphere microbe populations but not root traits induced by drought in Populus euphratica males

Funding Information: This work was supported by the National Natural Science Foundation of China (Grant No. U1803231). Publisher Copyright: © 2023, Higher Education Press.How sex-related root traits and soil microbes and their interactions respond to drought remains unclear. Here, we investigated how fine root traits and the composition of rhizosphere microbial communities in Populus euphratica females and males respond to drought in concert in 17-year-old plantations. Females increased specific root length (SRL) in response to drought. However, males showed no changes in their roots but significant increases in arbuscular mycorrhizal hyphal biomass and population of Gram-negative bacteria in the rhizosphere. Also, fungal symbiotroph communities associated with root systems in males differed from those in females under drought. We further demonstrated that the Gram-positive to Gram-negative bacteria ratios positively correlated with the SRL, while fungi to bacteria ratios were negatively correlated. Meanwhile, the relative abundance of symbiotrophs was negatively correlated with the SRL, while saprotroph abundance was positively correlated. Nevertheless, the relative abundance of symbiotrophs was positively correlated with the root carbon content (RCC). These findings indicate that microbial responses to drought depend highly upon the sex of the plant and microbial group and are related to root trait adjustments to drought. This discovery also highlights the role of plant-microbial interactions in the ecosystems of P. euphratica forest plantations.Peer reviewe

More Balanced Polynomials: Cube Attacks on 810- and 825-Round Trivium with Practical Complexities

The key step of the cube attack is to recover the special polynomial, the superpoly, of the target cipher. In particular, the balanced superpoly, in which there exists at least one secret variable as a single monomial and none of the other monomials contain this variable, can be exploited to reveal one-bit information about the key bits. However, as the number of rounds grows, it becomes increasingly difficult to find such balanced superpolies. Consequently, traditional methods of searching for balanced superpolies soon hit a bottleneck. Aiming at performing a cube attack on more rounds of Trivium with a practical complexity, in this paper, we present three techniques to obtain sufficient balanced polynomials. 1. Based on the structure of Trivium, we propose a variable substitution technique to simplify the superpoly. 2. Obtaining the additional balanced polynomial by combining two superpolies to cancel the two-degree terms. 3. We propose an experimental approach to construct high-quality large cubes which may contain more subcubes with balanced superpolies and a heuristic search strategy for their subcubes whose superpolies are balanced. To illustrate the power of our techniques, we search for balanced polynomials for 810- and 825-round Trivium. As a result, we can mount cube attacks against 810- and 825-round Trivium with the time complexity of $2^{44.17}$ and $2^{53.17}$ round-reduced Trivium initializations, respectively, which can be verified in 48 minutes and 18 days on a PC with one A100 GPU. For the same level of time complexity, this improves the previous best results by $2$ and $5$ rounds, respectively

ThumbNet: One Thumbnail Image Contains All You Need for Recognition

Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of computational resources. Current works mostly seek to compress the network by reducing its parameters or parameter-incurred computation, neglecting the influence of the input image on the system complexity. Based on the fact that input images of a CNN contain substantial redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet, to simultaneously accelerate and compress CNN models by enabling them to infer on one thumbnail image. We provide three effective strategies to train ThumbNet. In doing so, ThumbNet learns an inference network that performs equally well on small images as the original-input network on large images. With ThumbNet, not only do we obtain the thumbnail-input inference network that can drastically reduce computation and memory requirements, but also we obtain an image downscaler that can generate thumbnail images for generic classification tasks. Extensive experiments show the effectiveness of ThumbNet, and demonstrate that the thumbnail-input inference network learned by ThumbNet can adequately retain the accuracy of the original-input network even when the input images are downscaled 16 times

Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system

Background: For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. Methods: In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Results: Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and the two patients that emerged from a minimally conscious state achieved accuracies significantly greater than the chance level. Furthermore, P300 potentials and steady state visual evoked potentials were observed in the electroencephalography signals from the five patients. Conclusions: Number processing and arithmetic abilities as well as command following were demonstrated in the five patients. Furthermore, our results suggested that through brain-computer interface systems, many cognitive experiments may be conducted in patients with disorders of consciousness, although they cannot provide sufficient behavioral responses. © 2015 Li et al

One-for-All: Towards Universal Domain Translation with a Single StyleGAN

In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space. To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P mapper. Utilizing CLIP embeddings, this module is tailored to approximate the latent distribution in the P space, effectively acting as a connector between these two spaces. The proposed UniTranslator is versatile and capable of performing various tasks, including style mixing, stylization, and translations, even in visually challenging scenarios across different visual domains. Notably, UniTranslator generates high-quality translations that showcase domain relevance, diversity, and improved image quality. UniTranslator surpasses the performance of existing general-purpose models and performs well against specialized models in representative tasks. The source code and trained models will be released to the public