The influence of X fuzzy mathematical method on basketball tactics scoring

In the selection of basketball players, the determination of the selection index system and the weight of each index is an important prerequisite for whether the selection is scientific or not. Only when the index system is determined and the importance of each index is sorted reasonably can it be guaranteed that the basketball tactics scoring work went smoothly. This research introduces a method of X fuzzy mathematics, called analytic hierarchy process, or AHP for short. The AHP method can be used in regional planning, resource allocation, program selection, policy analysis, conflict analysis, forecast estimation, decision research, etc. The AHP method schematises the thinking process of the human brain analysis program, which can simply, comprehensively, effectively and clearly deal with complex problems restricted by many factors; it is also a quantitative tool that can be used for the measurement of the sports evaluation system.

Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN

Scalable and sustainable AI-driven analytics are necessary to enable large-scale and heterogeneous service deployment in sixth-generation (6G) ultra-dense networks. This implies that the exchange of raw monitoring data should be minimized across the network by bringing the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying traffic patterns and channel conditions, making thereby the data collected in different points non independent and identically distributed (non-IID), which is challenging for FL. To sidestep this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This a priori entropy is calculated using a multi-dimensional spectral clustering scheme over both the features and the supervised output spaces, and is suitable for classification as well as regression tasks. The FL aggregation operations support system (OSS) server then uses the reported dataset entropies to devise 1) an entropy-based federated averaging scheme, and 2) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to show the superiority of these novel approaches.

Combating the Challenges of Intellectual Freedom: The Perspective of Librarians in Academic Libraries in Kwara State, Nigeria

Background: Censorship of library materials denies people's right to access, use, retrieve, and store materials of their desire. Intellectual freedom is critical to eliminating the constraint of censorship. Issues such as illiteracy, societal standards, and selection policy hadbeen identified as challenges to intellectual freedom. The existing body of literature revealed that library materials are subjected to censorship and this denies readers’ rights to access desirable information at any point in time. Aim: This research examined the perception of librarians on combating the challenges of intellectual freedom. Methodology: The target population for this study included professional librarians working in eight (8) selected academic libraries in Kwara State, Nigeria. A stratified random sampling technique was used to select 60 respondents from the 8 academic libraries involved in the study. A questionnaire of 6 research questions was developed for the collection of data. Findings: The results indicated: the majority of the respondents agree that there are equal opportunities for library users to access library materials, obscene and controversial materials are subjected to censorship, library selection policy restricts library users’ access to desired materials. Lifting restriction of access to library materials was identified as the way to combat the challenges of intellectual freedom. Recommendations: The authors recommends that government should ensure that the Freedom of Information Bill (FOI) is passed to enable library patrons’ have access to all information materials and that library stakeholders should create awareness, publicity, or enlightenment on intellectual freedom to inform the users of their rights to hold, use, and access information materials of their choice.

A pseudo-softmax function for hardware-based high speed image classification

In this work a novel architecture, named pseudo-softmax, to compute an approximated form of the softmax function is presented. This architecture can be fruitfully used in the last layer of Neural Networks and Convolutional Neural Networks for classification tasks, and in Reinforcement Learning hardware accelerators to compute the Boltzmann action-selection policy. The proposed pseudo-softmax design, intended for efficient hardware implementation, exploits the typical integer quantization of hardware-based Neural Networks obtaining an accurate approximation of the result. In the paper, a detailed description of the architecture is given and an extensive analysis of the approximation error is performed by using both custom stimuli and real-world Convolutional Neural Networks inputs. The implementation results, based on CMOS standard-cell technology, compared to state-of-the-art architectures show reduced approximation errors.

Thread-Aware Mechanism to Enhance Inter-Node Load Balancing for Multithreaded Applications on NUMA Systems

NUMA multi-core systems divide system resources into several nodes. When an imbalance in the load between cores occurs, the kernel scheduler’s load balancing mechanism then migrates threads between cores or across NUMA nodes. Remote memory access is required for a thread to access memory on the previous node, which degrades performance. Threads to be migrated must be selected effectively and efficiently since the related operations run in the critical path of the kernel scheduler. This study focuses on improving inter-node load balancing for multithreaded applications. We propose a thread-aware selection policy that considers the distribution of threads on nodes for each thread group while migrating one thread for inter-node load balancing. The thread is selected for which its thread group has the least exclusive thread distribution, and thread members are distributed more evenly on nodes. This has less influence on data mapping and thread mapping for the thread group. We further devise several enhancements to eliminate superfluous evaluations for multithreaded processes, so the selection procedure is more efficient. The experimental results for the commonly used PARSEC 3.0 benchmark suite show that the modified Linux kernel with the proposed selection policy increases performance by 10.7% compared with the unmodified Linux kernel.

KEBIJAKAN SELEKSI DALAM MENDUKUNG STANDAR KOLEKSI DI PERPUSTAKAAN SEKOLAH

The school library is a library in a school to support the teaching and learning activities and objectives of the parent school. In terms of the collection, the library has a standard, both from the number of collections as well as the depth of the collection. There needs to be the selection policy as part of a collection development policy so that these standards are fulfilled. This paper uses literature study method. This paper provides guidance to the school librarian about the things that need to be considered in making the selection policy. Selection policy must consider the amount of collection, type of collection, the suitability of the curriculum, language, and so forth.

Backtracking Restarts for Deep Reinforcement Learning

Manipulating the starting states of a Markov Decision Process to accelerate the learning of a deep reinforcement learning agent is an idea that has been proposed in several ways in the literature. Examples include starting from random states to improve exploration, taking random walks from desired goal states, and using performance-based metrics for starting states selection policy. In this paper, we explore the idea of exploiting the RL agent's trajectories generated during training for use as starting states. The main intuition behind this proposal is to focus the training of the RL agent to overcome its current weaknesses by practicing overcoming failure states by resetting the environment to a state in its recent past. We shall call the idea of starting from a fixed (or variable) number of steps back from recent terminal or failure states `backtracking restarts'. Our empirical findings show that this modification yields tangible speedups in the learning process.