A Mobile Learning Support System for Ubiquitous Learning Environments

AbstractThis paper proposes a Mobile Learning Support System (MLSS) which enables students to access learning materials by utilizing 2D barcodes and GPS technology. As the pilot system of ubiquitous learning, we used camera-equipped mobile phones and 2D barcode tags to obtain learning information from online websites. By installing the MLSS on to their mobile phones, students can scan the tag attached to the corresponding object to display related multimedia materials on the screen of mobile phones. Furthermore, MLSS also applies GPS technology to develop a location-aware environment for students. GPS technology is used to detect the students’ location and identify which 2D barcode tags are in their proximity. Therefore, this paper provides the opportunity to develop for developers create ubiquitous learning environments that combine real-world and digital world resources

DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. These event markers may not be readily available or could be challenging to acquire during real-time inference, and the sequence of eye fixations may not align with the order of spoken words. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it alleviates the order mismatch between eye fixations and spoken words by introducing text-EEG contrastive alignment training, and 2) it minimizes the interference caused by individual differences in EEG waves through an invariant discrete codex. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. Furthermore, this work is the first to facilitate the translation of entire EEG signal periods without needing word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset, respectively. Codes and the final paper will be public soon

Automatic Learning of A Supervised Classifier for Patent Prior Art Retrieval

Prior art retrieval is the process of determining a set of possibly relevant prior arts for a specific patent or patent application. Such process is essential for various patent practices, e.g. patentability search, validity search, and infringement search. To support the automatic retrieval of prior arts, existing studies generally adopt the traditional information retrieval (IR) approach or extend the IR approach by incorporating additional information such as citations, classes of patents. Those approaches only exploit partial information of patents and thus may limit the performance of prior art retrieval. In response, we propose a novel approach which employs comprehensive information of patents and performs a supervised approach for prior art retrieval. Unlike traditional supervised learning approach which requires manual preparation of a set of positive and negative training examples, the proposed supervised technique includes a simple but effective mechanism for automatic generation of training examples. Our empirical evaluation on a large dataset consisted of 52,311 semiconductor-related patents indicates that the proposed supervised technique significantly outperforms the traditional full-text-based IR approach

Contrastive learning-based agent modeling for deep reinforcement learning

Multi-agent systems often require agents to collaborate with or compete against other agents with diverse goals, behaviors, or strategies. Agent modeling is essential when designing adaptive policies for intelligent machine agents in multiagent systems, as this is the means by which the ego agent understands other agents' behavior and extracts their meaningful policy representations. These representations can be used to enhance the ego agent's adaptive policy which is trained by reinforcement learning. However, existing agent modeling approaches typically assume the availability of local observations from other agents (modeled agents) during training or a long observation trajectory for policy adaption. To remove these constrictive assumptions and improve agent modeling performance, we devised a Contrastive Learning-based Agent Modeling (CLAM) method that relies only on the local observations from the ego agent during training and execution. With these observations, CLAM is capable of generating consistent high-quality policy representations in real-time right from the beginning of each episode. We evaluated the efficacy of our approach in both cooperative and competitive multi-agent environments. Our experiments demonstrate that our approach achieves state-of-the-art on both cooperative and competitive tasks, highlighting the potential of contrastive learning-based agent modeling for enhancing reinforcement learning.Comment: 8 pages, 6 figure

Influence of the inlet air temperature on the microencapsulation of kenaf (Hibiscus cannabinus L.) seed oil.

The aim of this study was to evaluate the influence of different inlet air temperatures on the physicochemical properties and oxidative stability of microencapsulated kenaf seed oil (MKSO). Kenaf seed oil was homogenised with the wall materials at a total solid content of 30% and was spray-dried at 160, 180 or 200°C inlet air temperature. The microstructure and morphology of the microencapsulated kenaf seed oil were observed using a scanning electron microscope. The physicochemical properties, such as moisture content, water activity and particle size, of MKSO produced at different inlet air temperatures showed a significant difference (p<0.05). MKSO produced with an inlet air temperature of 160°C exhibited the highest microencapsulation efficiency (MEE, 96.46%) compared to 180°C (78.42%) and the efficiency was lowest at 200°C (58.96%). Increasing the inlet air temperature also resulted in significantly increased (p<0.05) lipid oxidation of MKSO and decreased total intrinsic phenolic content upon accelerated storage. However, all MKSO had lower lipid oxidation and higher total phenolic content than bulk (unencapsulated) oil. This study indicates that increased inlet air temperature results in larger particle size, higher lipid oxidation and lower MEE. The process of microencapsulation could protect oil from the external environment that causes lipid oxidation. Practical applications: Kenaf seed oil contains PUFA and phytosterols, which are beneficial to human health. However, the PUFA in kenaf seed oil is susceptible to lipid oxidation, which degrades its nutritional value. Microencapsulation is used to protect the kenaf seed oil from being oxidised. By knowing the influence of the inlet air temperature on the physical properties and oxidative stability of the microencapsulated kenaf seed oil, the ideal inlet air temperature can be used to produce microencapsulated kenaf seed oil, which may be incorporated into food products to supplement the bioactive compounds that are beneficial to human health

The effect of adding a home program to weekly institutional-based therapy for children with undefined developmental delay: A pilot randomized clinical trial

AbstractBackgroundEarly rehabilitation for children with developmental delay without a defined etiology have included home and clinic programs, but no comparisons have been made and efficacy is uncertain. We compared a weekly visit for institutional-based therapy (IT) to IT plus a structured home activity program (HAP).MethodsSeventy children who were diagnosed with motor or global developmental delay (ages 6-48 months and mean developmental age 12.5 months) without defined etiology were recruited (including 45 males and 23 females). The outcomes included the comprehensive developmental inventory for infants and toddlers test and the pediatric evaluation of disability inventory.ResultsChildren who received only IT improved in developmental level by 2.11 months compared with 3.11 months for those who received a combination of IT and HAP (p = 0.000). On all domains of the comprehensive developmental inventory for infants and toddlers test, except for self-help, children who participated in HAP showed greater improvements, including in cognition (p = 0.015), language (p = 0.010), motor (p = 0.000), and social (p = 0.038) domains. Except on the subdomain of self-care with caregiver assistance, the HAP group showed greater improvement in all the pediatric evaluation of disability inventory subdomains (p < 0.05).ConclusionEarly intervention programs are helpful for these children, and the addition of structured home activity programs may augment the effects on developmental progression

Overexpression of Nuclear Protein Kinase CK2 α Catalytic Subunit (CK2α) as a Poor Prognosticator in Human Colorectal Cancer

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignancies but the current therapeutic approaches for advanced CRC are less efficient. Thus, novel therapeutic approaches are badly needed. The purpose of this study is to investigate the involvement of nuclear protein kinase CK2 α subunit (CK2α) in tumor progression, and in the prognosis of human CRC. METHODOLOGY/PRINCIPAL FINDINGS: Expression levels of nuclear CK2α were analyzed in 245 colorectal tissues from patients with CRC by immunohistochemistry, quantitative real-time PCR and Western blot. We correlated the expression levels with clinicopathologic parameters and prognosis in human CRC patients. Overexpression of nuclear CK2α was significantly correlated with depth of invasion, nodal status, American Joint Committee on Cancer (AJCC) staging, degree of differentiation, and perineural invasion. Patients with high expression levels of nuclear CK2α had a significantly poorer overall survival rate compared with patients with low expression levels of nuclear CK2α. In multi-variate Cox regression analysis, overexpression of nuclear CK2α was proven to be an independent prognostic marker for CRC. In addition, DLD-1 human colon cancer cells were employed as a cellular model to study the role of CK2α on cell growth, and the expression of CK2α in DLD-1 cells was inhibited by using siRNA technology. The data indicated that CK2α-specific siRNA treatment resulted in growth inhibition. CONCLUSIONS/SIGNIFICANCE: Taken together, overexpression of nuclear CK2α can be a useful marker for predicting the outcome of patients with CRC

Set-up for an optically induced dielectrophoresis platform and its application to micro- and nanoscale material manipulation

In this study, we set-up an optically induced dielectrophoresis platform, which can be used to manipulate micro- and nanoscale particles. A commercially available liquid-crystal-display–based projector was used as a light source to produce a variety of optical patterns and project them onto a photoconductive material. The optical patterns illuminating the photoconductive material can be used as configurable virtual electrodes, which will induce dielectrophoretic forces on particles. Thus, the particles can be manipulated by dynamic optical patterns. Manipulation of silver nanowires was demonstrated using this platform by forming a specific pattern of nanowires through illumination of the photoconductive chip with an optical pattern. In addition, polystyrene beads with diameters of 10 and 20 μm were also successfully manipulated using this system. By combining the optically induced dielectrophoretic force and the hydrodynamic force, particles of two different sizes can be continuously separated into two different microchannels. Furthermore, the microparticles were collected and concentrated by virtual electrode traps. We believe that this flexible platform can be applied to investigations of a variety of fields

Integrative model for the selection of a new product launch strategy, based on ANP, TOPSIS and MCGP: a case study

New product launch strategy is a key competitive advantage for a new product development. A new product launch is a multiple criteria decision-making problem, which involves evaluating different criteria or attributes in a strategy selection process. The purpose of this paper is to develop a qualitative and quantitative approach for the selection of a new product launch strategy. The current study proposes an integrated approach, integrating analytic network process, the technique for order preference by similarity to an ideal solution and multi-choice goal programming, which can be used to determine the best launch strategy for marketing problems. The advantage of this integrated method is that it enables the consideration of both tangible (qualitative) and intangible (quantitative) criteria as well as both “more/higher is better” (e.g., benefit criteria) and “less/lower is better” (e.g., cost criteria) in the launch strategy of a new product selection problem. To show the practicality and usefulness of this method, an empirical example of a watch company is demonstrated. First published online: 03 Nov 201