Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay-aware User Association for Caching Enabled Small Cell Networks

Capable of reducing the content-user distance, content caching is shown to increase the hit ratio, relieve the backhaul pressure and improve the overall system throughput in caching enabled small cell networks (CSCN). In this work, however, we show that the performance of caching will be significantly degraded in terms of the local delay in dense CSCN. Especially, local delay, which is defined as the number of time slots for one successful transmission, first decreases and then exponentially increases with the growing deployment density of small cell base stations (SBSs) when the number of users is large. The reason is that transmission failures are more likely to occur supposing that content-oriented user association is applied and over-deployed SBSs are located closer to the intended user than the associated SBSs, which results in overwhelming near-field interference. To reduce the local delay, we design and optimize a delay-aware user association strategy, in which a distance threshold is used to determine whether to obtain content through content caching or backhaul resources. Through the optimization of distance threshold, the near-field interference could be effectively alleviated. Numerical results show that the local delay performance is improved under delay-aware user association strategy, especially for high signal-to-interference ratio (SIR) threshold.
Modality estimation methods in internal training reflection texts using BERT.

In-house training includes opportunities for employees to reflect on the training through the writing of written reflections. Its purpose is to create opportunities to look back and think about "how the content learned in the training could be utilised in subsequent work," and to allow employees to discover their own issues and set future action goals. In addition, it is possible to measure the effectiveness of the training, and the planners can also discover issues in the training itself, such as the appropriateness of the content, which is useful for improving future training programs. However, there has been little research on these employee reflection texts, especially text analysis using natural language processing (NLP), and there is a need for methods to find useful uses for the texts. By taking a NLP approach to the accumulated data of reflective statements, it is possible to estimate the degree of understanding of the training content and to grasp the degree of growth of employees over time. In addition, there is a potential for proposing training content suited to each employee and improving the efficiency of training as a further development of this approach. As a first step towards the realization of these possibilities, this study proposes automatic modality estimation methods focusing on sentence-final expressions in retrospective self-reflection sentences. Modality in Japanese includes the speaker’s subjective semantic content, which indicates the speaker’s judgement of facts and his/her attitude towards speech and communication to the listener. Therefore, we believe that the modality part of self-reflection sentences on training texts contains important information that leads to the understanding of each employee’s behavior and intentions. Experimental results show that the proposed methods enables modality estimation with higher accuracy than manually formulated rules based on sentence structure. The results also show the possibility of developing the proposed methods to be applied to a deeper analysis approach to reflection sentences.
An Improved 5G NR LDPC Decoding Algorithm Based On Box-Plus Operation

Iterative box-plus operation is an equivalent function of BP algorithm used in fifth generation(5G) new radio Low-Density Parity-Check (LDPC) decoders, which has gathered significant interest in the past few years. However, it is very challenging to approximate iterative Box-Plus operation effectively since it contains continuous logarithmic and exponential operation. In this paper, the problem is solved gracefully by proposing a simple yet efficient approximate function. The corresponding optimal parameters are given by density evolution and Monte Carlo simulation, which could greatly reduce the unreliability of check-to-variable messages. The extensive experiments in error-correction performance have show the feasibility of the proposed method, compared to existing NMS and OMS algorithms, which makes it more attractive in practical 5G LDPC decoding applications.
Sandwich robot: how non-programmers solve computational problems

We are investigating how non-programmers make sense and solve simple computational problems. We developed a game where a robot has to assemble a sandwich, collecting a specific list of ingredients. Our game follows principles and offers game mechanics typical of games for learning programming. The main innovation of our game is that it visualizes the generality property of algorithms through double levels, that we call twin-worlds, in which one same algorithm is executed by two robots in assembling their sandwiches. Results from early testing with university students enrolled in the education of Media Studies show that the game is usable, levels can be solved at various degrees of complexity (e.g. with and without using loops), and that the students were able to make sense of our twin-worlds levels and formulate general algorithms for both worlds. However, the gathered feedback also highlights the need to explore alternative implementations of the twin-worlds concept.
Gini Coefficients, Social Network Analysis, and Markov Chains: quantitative methods for analyzing the distribution of benefits in natural resource-dependent communities

The equitable distribution of benefits is a critical component of sustainable community-based management of natural resources. This paper measures the circulation of currency among businesses in a small, natural resource-dependence community in interior British Columbia, and shows how this could tied to a quantified equitable distribution of extraction opportunities. As part of the development of the tools, this paper shows the equivalence of input-output models, social network analysis, and Markov chains. While ideologically the approach of quantifying equality is of interest to many, in reality and practice there are significant challenges to the adoption and implementation of these methods.
Non-Coherent Discrete Chirp Fourier Transform for Modulated LFM Parameter Estimation

Linear frequency modulated (LFM) waveforms play a significant role in both military and civilian detection and ranging applications. Based on LFM, researchers have designed a phase shift keying (PSK) modulated LFM waveform to enable both communication and radar functions simultaneously. Estimating the chirp rate and carrier frequency of data modulated LFM waveforms is therefore of interest in defence applications. In the previous research, the discrete chirp-Fourier transform (DCFT) was proposed to estimate the parameters for non-modulated waveforms. However, the DCFT method is limited with respect to the transform length and the estimation range. Thus, this paper proposes a generalised coherent DCFT that extends the previous DCFT method. We also introduce a novel non-coherent DCFT to improve parameter estimation for PSK modulated LFM waveforms. In the simulation section, this paper discusses the applicability of two modified DCFT methods and demonstrates their superior performance.
Multi-Stage Pyramid Parsing Network For Lane Marking Detection

The recognition and segmentation of lane markings are one of the most difficult challenges in autonomous driving. One must overcome several challenges when attempting to solve this task, including trivial appearance, irregular painting, intricate shapes of lane markings, and limited spatial coverage of them w.r. t background. These problems lead to the generation of numerous false positives when traditional convolutional neural networks (CNN) are used. In this paper, we propose a novel MPP-Net model, which harnesses the power of attention and pyramid pooling operations to produce semantically consistent lane marking segmentations. In addition to that, the model is set up in a hierarchical manner to focus on both fine and coarse level features and yet produce less number of false positives due to the information regulation mechanism provided by the attention gates. We train and test our proposed model on two famous lane marking detection datasets VPGNet and Tusimple. The outcomes of our experimentation and ablation studies indicate that our model’s performance is better compared to most of the benchmarking models and is on par with some in terms of F1-score while improving semantic consistencies in those scenarios.
Task-Oriented Computation Offloading and Resource Management in D2D-Assisted Heterogeneous Networks

With the emergence of various newly developing applications, the computational tasks generated in the internet of things become more diverse. It is vital to match the diverse tasks with network resources according to the task differential de-mands (e.g., latency, energy, security). In device-to-device (D2D) assisted heterogeneous networks (HetNets), tasks can be offloaded to idle devices or base stations to improve the offloading efficiency and reduce the burden from the core network. Exploiting these benefits, we investigate task-oriented computation offloading and resource management in D2D-assisted HetNets. We aim to minimize the weighted sum of latency and energy consumption, by jointly optimizing the offloading decision, caching strategy and computation resource allocation, while guaranteeing differential task latency requirements and the energy budget of all devices. We first employ K-means algorithm to cluster tasks into several categories based on their characteristics. Further, as the formu-lated problem is a large-scale mixed-integer nonlinear optimizing problem, which is difficult to solve within a reasonable time, a distributed iterative algorithm based on alternating direction method of multipliers (ADMM) is proposed to find a near-optimal solution. Simulation results reveal that our proposed approach can achieve higher performance than other benchmark approaches in reducing latency and energy consumption.
Learning Low-Rank Models From Compressive Measurements for Efficient Projection Design

Recent research has uncovered information-theoretic means to design projection matrices in scenarios where one information source is compressively sampled in the presence of a secondary source. Furthermore, if both sources can be approximated by Gaussian mixture (GM) models, it has been shown that it is possible to learn the characteristics of the secondary source from compressive measurements only. In this work, we investigate techniques that exploit low-rank GM approximations to the true distributions to reduce computational complexity and memory requirements during source learning and projection design. Two novel alternative projection design strategies are also introduced. These are tested against an existing strategy to determine which approach is superior for low size, weight, and power (SWAP) applications. Experimental results validate the benefits of the proposed low-rank strategies and reveal that all projection design algorithms offer similar levels of performance.
The Coverage of Soft-Tissue Defects Around the Foot and Ankle Using Free or Local Flaps : a Comparative Cohort Study ( Ann Plast Surg . 2021 ; 86 : 668 – 673 )

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Automatic video censoring system using deep learning

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications.
A search for optical counterparts of the complex Vela X system

The pulsar wind nebula (PWN) associated with the Vela pulsar is a bright source in the radio, X-ray and gamma-ray bands, but not in the optical. This source is very near, lying at a distance of 290 pc, as inferred from the radio and optical parallax measurements of the pulsar. Knowledge of the brightness and structure of the Vela PWN in optical is important in order to constrain the underlying particle spectrum (and possibly the B-field properties and particle losses) associated with this extended source. We use results from the Digital Sky Survey, as well as results obtained using the SAAO 1.0 m telescope equipped with an imaging CCD (STE4) and BV filters, in an attempt to measure optical radiation from Vela X. To enlarge our field of view, we constructed a mosaic consisting of 3 x 3 frames around the pulsar position. We present spectral measurements from the High Energy Stereoscopic System (H.E.S.S.), Fermi Large Area Telescope (LAT), ASCA, Hubble Space Telescope (HST), Very Large Telescope (VLT), New Technology Telescope (NTT), and Wilkinson Microwave Anisotropy Probe (WMAP), in addition to our optical results. Using these data, we investigate whether or not the radio synchrotron component can be smoothly extrapolated to the optical band. This would constrain the electron population to consist of either a single or multiple components, representing a significant advancement in our understanding of this complex multiwavelength source.
SOTER: Guarding Black-box Inference for General Neural Networks at the Edge

The prosperity of AI and edge computing has pushed more and more well-trained DNN models to be deployed on third-party edge devices to compose mission-critical applications. This necessitates protecting model conﬁdentiality at untrusted devices, and using a co-located accelerator (e.g., GPU) to speed up model inference locally. Recently, the community has sought to improve the security with CPU trusted execution environments (TEE). However, existing solutions either run an entire model in TEE, suffering from extremely high inference latency, or take a partition-based approach to hand-craft partial model via parameter obfuscation techniques to run on an untrusted GPU, achieving lower inference latency at the expense of both the integrity of partitioned computations outside TEE and accuracy of obfuscated parameters. We propose S OTER , the ﬁrst system that can achieve model conﬁdentiality, integrity, low inference latency and high accuracy in the partition-based approach. Our key observation is that there is often an associativity property among many inference operators in DNN models. Therefore, S OTER automatically transforms a major fraction of associative operators into parameter-morphed , thus conﬁdentiality-preserved operators to execute on untrusted GPU, and fully restores the execution results to accurate results with associativity in TEE. Based on these steps, S OTER further designs an oblivious ﬁngerprinting technique to safely detect integrity breaches of morphed operators outside TEE to ensure correct executions of inferences. Experimental results on six prevalent models in the three most popular categories show that, even with stronger model protection, S OTER
Comment on "Scattering Theory Derivation of a 3D Acoustic Cloaking Shell"

In a recent Letter, Cummer et al. give a description of material parameters for acoustic wave propagation giving rise to a 3D spherical cloak, and verify the cloaking phenomenon on the level of scattering coefficients. A similar configuration has been given by Chen and Chan. In this Comment, we show that these theoretical constructions follow directly from our earlier work http://arxiv.org/abs/math/0611185 on full wave analysis of cloaking for the Helmholtz equation with respect to Riemannian metrics. Furthermore, the analysis there covers the case of acoustically radiating objects being enclosed in the cloaked region.
EFFECT OF SCHOOL MANAGEMENT COMMUNICATION ON ACADEMIC PERFORMANCE OF PUBLIC SECONDARY SCHOOLS IN NYATIKE SUB-COUNTY, KENYA

This study analyzed the strategies school managers apply to improve academic performance of students in their schools in Nyatike Sub County, Migori County, which has suffered poor school management resulting into low academic performance as stated by county education officer during education symposium in the year 2017. Validity and reliability of the study was tested. Cronchba’s Alpha Coefficient of 0.80 was confirmed the reliability of the instruments. The study was guided by the McGregor’s theory X and theory Y based on various interrelated variables such as communication, supervision, decision making and performance in schools. Population for the study comprises of 150 BOM, 120 heads of department, 120 members of parents teacher association and 33 school principals. A descriptive survey research design was adopted. Simple random sampling techniques was utilized to draw fifty members of the Board of Management (BOM), forty Heads of Department (HOD), forty members of Parents Teachers Association (PTA) and ten school principals yielding to a sample size of 140 subjects to participate in the study. A mixed method was used to collect data from respondents using questionnaire and qualitative data from heads of departments and members of PTA using interviews. Descriptive statistics which include percentages, coding, decoding of information was used in data analysis and data presentation was in tables and charts. Piloting of the research instruments was conducted. The findings of the study were used by Ministry of Education and other stake holders to improve academic performance and management of students in secondary schools. The findings of this study revealed that decision making have a great contribution in affecting the organizational objectives. So, it is vital for the school management and other stakeholders to make wise decisions in order to meet the objectives of an organization. Also this finding has found out that communication if well-made can lead to the promotion of the desired achievement of academic performance in Kenya. Also teachers and students do not work together as stakeholders which reduce the effort of academic performance to rise. The findings of this study have revealed that it is an imperative duty for the school management to promote participatory decision making system in encouraging good student performance. of these circumstances, education was not help to orient learners towards positive intellectual transformation out of which socio-economic challenges was contained. It has been evidenced that school management does not create room for all (students, teachers and community at large) to take active part in decision making on matters concerning the school prosperity.
Selective local binary pattern with convolutional neural network for facial expression recognition

Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.
Emissions of Volatile Organic Compounds from Dairy Cattle Manure in a Cattle Shed in Japan

The livestock industry is a major source of atmospheric volatile organic compounds(VOCs), but details on these emissions are not well documented in Japan. In particular, it remains unclear how the rearing method affects the emissions of VOCs from livestock, which originate primarily from feces and urine. Here we aimed to estimate the amounts of VOCs emitted from the feces and urine of tethered Holstein dairy cattle in a cattle shed in Japan. Dimethyl sulfide and acetone accounted for about 60% of the total VOCs emitted from feces, followed by formaldehyde and acetaldehyde. Also, dimethyl sulfide and acetone were the dominant VOCs emitted from urine, accounting for 90% of the total VOCs. The VOCs from manure were considered to be emitted between the excretion and removal of the manure during the cleaning of the shed. As a result of analyzing images from three cameras installed in the shed, the average time between excretion and cleaning during the daytime (8:00 am–5:00 pm) was 80 min for feces and urine, whereas at night (5:00 pm–7:00 am), the average time between excretion and cleaning was 480 min. Based on the above findings, the emissions of VOCs in the interval between excretion and cleaning of the shed were estimated. As a result, the emissions of VOCs from feces and urine per head of cattle in the shed were estimated to be 1.75 and 1.52 g day−1, respectively. Furthermore, contribution of VOCs emitted from manure to odor activity value (OAV) and hydroxyl radical reactivity (OHR) were also estimated. Volatile fatty acids and sulfur compounds emitted from feces estimated to have high contribution to OAV, whereas aldehydes contributed mainly for OHR from manure.