Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Linear Polarization on Channel Modeling in 5G Cellular Communications: A Statistical Approach

Millimeter wave (mmWave) is perceived as an essential innovation so as to meet the rapid increment of wireless mobile traffic request utilizing its massive bandwidth. However, channel modeling is still difficult for its high weather dependency and antenna positioning for line-of-sight (LOS) communication. Co-polarization and cross polarization(X-pol) are two main incidents of radiation pattern direction for transmitting wave; where the desired direction of transmitting wave indicates co-pol and orthogonal propagation of the desired direction represents X-pol. This paper presents an extensive analysis of polarization impact on a statistical channel modeling for two definite front-runners of mmWave bands (28 GHz & 73 GHz) by a novel simulator NYUSIM. We simulate a realistic urban microcell scenario using the 3-D statistical spatial channel model (SSCM) that is adopted by time cluster and spatial beam approach. Simulation results present 3-D channel characteristics (AoA, AoD, and path loss) and various power delay profiles (directional, omnidirectional, and small scale) for Co-pol and X-pol in 28 GHz and 73 GHz.
Deep and Efficient Impact Models for Edge Characterization and Control of Energy Events

Network control in microgrids is an active research area driven by a steady increase in energy demand, the necessity to minimize the environmental footprint, yet achieve socioeconomic benefits and ensure sustainability. Reducing deviation of the predicted energy consumption from the actual one, softening peaks in demand and filling in the troughs, especially at times when power is more affordable and clean, present challenges for the demand-side response. In this paper, we present a hierarchical energy system architecture with embedded control. This architecture pushes prediction models to edge devices and executes local control loops to address the challenge of managing demand-side response locally. We employ a two-step approach: At an upper level of hierarchy, we adopt a conventional machine learning pipeline to build load prediction models using automated domain-specific feature extraction and selection. Given historical data, these models are then used to label prediction failure events that force the operator to use backup energy sources to stabilize the network. On a lower level of hierarchy, computed labels are used to train impact models realized by LSTM networks running on edge devices to infer the probability that the power consumption of the player contributes to the upper level prediction failure event. The system is evaluated on clustered and aggregated energy traces from a public data set of academic buildings. The results show the benefits of the proposed hierarchical energy system architecture in terms of impact prediction with 55% accuracy. This allows minimizing the number of prediction failure events by 11.69 % by executing targeted local control.
Predicting the Silent Data Corruption Vulnerability of Instructions in Programs

With the decreasing size and voltage level of internal device components, soft errors are increasing and constitute a major threat on electronic devices. Silent data corruption (SDC) is the most dangerous result type of soft errors as there is no indication that an error occurs during one program execution. Identifying the SDC vulnerability of instructions is the premise for applying selective SDC detection techniques to programs. We propose proPVInsiden to predict the SDC vulnerability of instructions with a lower cost of fault injection and a better adaptability for programs and program inputs. Reducing the number of fault injections will impair the performance of the prediction. Partial fault injection is applied to control the downward slope of the performance of the prediction to maximize the reduction of fault injection. Experimental results show that the number of fault injections is reduced by 55% and 45% fault injections are sufficient to predict the relative SDC vulnerability of an instruction with respect to other instructions. The averaged Spearman's rank correlation coefficient is 0.81. proPVInsiden also shows a better applicability for programs and program inputs.
Cost-Efficient Resource Provision for Multiple Mobile Users in Fog Computing

Fog computing is an emerging paradigm that brings the computing capabilities close to distributed IoT devices, which provides networking services between end devices and traditional cloud data centers. One important mission is to further reduce the monetary cost of fog resources while meeting the ever-growing demand of multiple users. In this paper, we focus on minimizing the total cost for multiple mobile users to provide an efficient resource provisioning scheme in fog computing. The total cost includes two aspects: the replication cost and the transmission cost. We consider two cases for the resource provision problem by focusing on different cost models. First, one simple case where users can only upload one replication is discussed, and an optimal solution is proposed by converting the original problem into one of bipartite graph matching. Then we consider a more complicated case that each user can upload multiple replications on fog nodes in the resource provisioning. For different transmission cost models, the transmission cost is related to the distance of each pair of fog nodes. This problem is proven to be NP-hard. We first propose a non-adaptive algorithm which is proved to be bounded by 2/3W+1/3OPT. Another 3+ε-approximation algorithm is proposed based on local search, which has better performance with higher complexity. Extensive simulations also prove the efficiency of our schemes.
LHC: A Low-Power Heterogeneous Computing Method on Neural Network Accelerator

Accelerators can achieve high performance and low energy consumption in training or inference of neural networks. If the Non-Neural Network (Non-NN) algorithms with large amount of computation could make full use of the accelerators, it is possible to speed up its implementation, reduce energy consumption, and achieve load balancing, especially on mobile devices equipped with accelerators. However, accelerators are dedicated to neural network calculations, so that other Non-NN algorithms have difficulty in using their advantages. Furthermore, many hardware-specific restrictions have become the obstacles, such as constrained precision of operands and limited computation scale. In this paper, we propose a method named Low-power Heterogeneous Computing (LHC) to bridge the gap between Non-NN algorithms and NN accelerators. Firstly, we analyze the general principle of the accelerator and reveal the calculation model of the accelerator. To hide the details of the underlying neural network library, we extract some operators from the limited number of types of neural network computation they support. We encapsulate the low-level library, extract operators suitable for general algorithms, and implement some more advanced operators that can adapt to the constrained hardware conditions. These operators could facilitate programmers to implement some Non-NN algorithms. In the aspect of the algorithm, we extract the computationally intensive parts of the Non-NN algorithm and deploy these computational tasks on the accelerator by calling the operators. To verify our method, we implement three Non-NN algorithms by using operators and adjusting these algorithms, include Grid-based Motions Statistics, k-Nearest Neighbors, and k-Means, on a specific accelerator, Cambricon-1A. The experimental results show that the energy consumption of calculation is reduced by up to 5.4x, compared with the CPU baseline. Our method can be further applied to other similar accelerators.
The Application of Non-Sinusoidal Resonance Inverter on An Ozone Generator

The implementation of non-sinusoidal resonance inverter on the ozone generator had been investigated in this paper. There are two models such as: model 1 was placed 2 (two) ring permanent magnets above the anode electrode, and below the cathode electrode, it was limited using the holder (nylon material) with magnetic conditions repelling each other, and the configuration of model 2 was similar with model 1 whereas this magnetic condition was attracting to each other. The plasma occurred between an anode electrode and solid dielectric, it was sharply clear using eyes, and it also can be captured using a digital camera. It is shown that the light intensity for model 2 is lower than model 1. Paint software in Microsoft was used as an image processing tool with invert color mode to analyze the plasma pictures. It is also stated that the micro discharge between the anode electrode and solid dielectric occurred intermittently. The current pulse in model 2 is more significant than model 1, that is proportional to micro discharge in the gap. The ozone gas production in model 2 was faster than model 1 with model 2 of 41.4 ppm per minute rather than model 1 of 32.4 ppm per minute. It can be concluded that the application of non-sinusoidal resonance inverter can be applied as the high voltage power supply for ozone generator that also induced by the magnetic field.
KerTSDroid: Detecting Android Malware at Scale through Kernel Task Structures

The explosive growth of data makes static and dynamic analysis in an individual machine difficult to detect Android malware. Hence many popular open-source frameworks for large-scale data processing have been used to deal with iterative machine learning tasks due to the efficient parallel computation and the in-memory abstraction. In this paper we first propose KerTSDroid, a novel Android malware detection framework, to predict the malicious applications in parallel through kernel task structures in Android kernel layer. Then a data provider is orchestrated and implemented to construct the real-world dataset of 112 kernel features from 12,750 Android applications. A parallel predictor is also provided to identify Android malicious applications. Our experiments demonstrate that a Decision Tree method of KerTSDroid can achieve the best precision rate (96%-99%) with a lower overhead on average, the other three methods (Naive Bayes, Logistic Regression, and Support Vector Machine) lead to lower precision rate while discriminating abnormal behaviors with the in-memory parallel-data.
Research on Supplier Selection Method Based on BP Neural Network

Supplier selection is a multi-criteria decisionmaking problem. It also faces many risks and problems. The traditional evaluation and decision-making of suppliers is inefficient and subjective. In order to select the best bidding supplier more scientifically and effectively, considering the main influencing factors of supplier selection comprehensively, on the basis of using FAHP-TOPSIS method, a modeling method based on BP Neural Network is introduced to reduce the subjectivity of artificial evaluation in the process of supplier selection and improve the work efficiency and intelligence level of supplier selection.
EASA: Entity Alignment Algorithm Based on Semantic Aggregation and Attribute Attention

Numerous knowledge bases have been published on the web, and there are serious heterogeneous problems among them. Unifying these knowledge bases at the semantic level can better promote the development of the Linked Data Project. Various effective methods, the mainstream one of which is the iterative entity alignment algorithm based on TransE (Translation-based Embedding, an efficient knowledge graph embedding representation algorithm), have been put forward to solve the heterogeneous problems among knowledge bases. Although the TransE-based iterative entity alignment algorithm can shorten the distance between two entities with the same semantics, it has low accuracy because it ignores the importance of semantic aggregation generated by many attributes of entities in the entity alignment process. To solve this problem, a novel entity alignment algorithm based on semantic aggregation and attribute attention, named EASA, is proposed in this paper. On the one hand, semantic aggregation of entities can be generated by different attributes and attribute values. On the other hand, the addition of attribute attention can be used to distinguish the different roles of different attributes in the entity alignment process. The experimental results show that our method achieves significant improvements compared to baselines for entity alignment on Chinese and English datasets. The data and source code for this paper can be obtained from https://www.github. com/xinan711456/EASA.
WOAL: A Tool to Orchestrate Workflow Using An Abstraction Layer

The development of systems with complex business processes needs developers who can orchestrate the system workflow accurately. Orchestrating workflow normally requires someone who has the knowledge in programming. This is because they are the ones who are able to directly link the workflow to the programming framework. Contrary to the normal practise, the business people is actually the best person to design the workflow as they are experts in their domain and therefore, can design complex workflow more accurately. However, business people have difficulty in orchestrating workflows using programming languages without having to go through a long learning process. Hence, the objective of Workflow Orchestration Abstraction Layer (WOAL) is to allow business people to design workflow on their own using an easy-to-understand language. They will be able to produce workflow diagrams for verification and workflow's automator for system development. This paper presents the architecture of WOAL, including the design of domain-specific language (DSL), lexer and parser.
Instrumenting API Hooking for a Realtime Dynamic Analysis

There are various approaches to detect malware. Among them is via dynamic analysis which is a very essential technique capable of detecting unknown malware. The dynamic analysis monitors the behaviour of the executable by providing its execution behaviour information. Since the complexity of the malware is increasing, it is important to monitor the malware and study how malware behaves to help in detecting it. In this paper, we highlighted the instrumentation technique to observe behaviour of Portable Executable execution. We briefly explored some of the related works. We discussed about dynamic analysis and Windows API Calls. We discussed on our realtime behaviour monitor. The concept of n-gram was explained and before concluding, several challenges were highlighted.
EvoBot: An Open-Source, Modular, Liquid Handling Robot for Scientific Experiments

Commercial liquid handling robots are rarely appropriate when tasks change often, which is the case in the early stages of biochemical research. In order to address it, we have developed EvoBot, a liquid handling robot, which is open-source and employs a modular design. The combination of an open-source and a modular design is particularly powerful because functionality is divided into modules with simple, well-defined interfaces, hence customisation of modules is possible without detailed knowledge of the entire system. Furthermore, the modular design allows end-users to only produce and assemble the modules that are relevant for their specific application. Hence, time and money are not wasted on functionality that is not needed. Finally, modules can easily be reused. In this paper, we describe the EvoBot modular design and through scientific experiments such as basic liquid handling, nurturing of microbial fuel cells, and droplet chemotaxis experiments document how functionality is increased one module at a time with a significant amount of reuse. In addition to providing wet-labs with an extendible, open-source liquid handling robot, we also think that modularity is a key concept that is likely to be useful in other robots developed for scientific purposes.
Root-associated microbiomes of Gymnadenia conopsea under the combined effect of plant geographical location and developmental stage

Root-associated microbiomes of Gymnadenia conopsea under the combined effect 1 of plant geographical location and developmental stage 2 Min Lin , Hui Xiong, Xuechuan Xiang, ZelinZhou, lifeng Liang and Zhinan Mei 3 1 School of Pharmaceutical Sciences, South-Central University for Nationalities, 4 Wuhan, China 5 2 Institute of Ethnomedicine, South-Central University for Nationalities, Wuhan, 6 China 7 † Min Lin and Hui Xiong contributed equally to this work. 8
Role of staging laparoscopy for gastric cancer patients

Staging laparoscopy (SL) is frequently carried out in patients with advanced gastric cancer. However, some clinical questions are being debated and consensus must be obtained. With this aim, a literature search of PubMed/MEDLINE was carried out using the keywords “gastric cancer,” “SL,” and “diagnostic laparoscopy”. Articles published online up to February 2019 were analyzed, focusing on the following questions. (i) What is an adequate indication for SL? (ii) How do you carry out SL? (iii) Does SL provide accurate information about peritoneal dissemination? (iv) Is the yield of SL different by tumor location? (v) Is SL a safe procedure? (vi) Is “repeat SL” needed? (vii) Does SL provide oncological benefit? Results provided the following responses: (i) In Western countries, clinically resectable advanced tumor is an indication for SL. Terms to be introduced for adequate indication include “location,” “type 4 (linitis feature),” “large tumor,” “equivocal computed tomography (CT] findings,” and “lymph node swelling”. (ii) Exploration of the entire peritoneal cavity is preferable. (iii) Detection rate of peritoneal disease is 43%‐52% in Japanese institutions and 7.8%‐40% in other countries. False‐negative findings during SL were 0%‐17%, and 10%‐13% when limited to cytology. (iv) Yield of SL was higher in gastric cancer compared with esophagogastric junctional tumor. (v) SL‐related complications were estimated to occur in 0.4%. (vi) Repeat SL is important after treatment. (vii) If the efficacy of neoadjuvant chemotherapy for patients with P0CY1 is established, SL can provide oncological benefit. SL can be carried out safely and effectively. Considering the prevalence of neoadjuvant treatment, the role of SL will become more important.
Factors Affecting Public Willingness to Adopt Renewable Energy Technologies: An Exploratory Analysis

Renewable energy has become an important element of today’s modern technology targeting high-efficiency energy production. As part of its 2030 Vision, Saudi Arabia is aiming to increase its energy production through renewable sources. The purpose of this research study is to explore the factors affecting public willingness to adopt renewable energy technologies in the western region of Saudi Arabia. This was achieved through an extensive literature review of previous studies conducted worldwide and resulted in the extraction of 19 factors that affect public willingness to adopt renewable energy technologies. Following a quantitative research design, random cross-sectional data of 416 participants using the extracted factors were collected via an online questionnaire survey. Following a dimension reduction statistical approach, key components were extracted with exploratory factor analysis using principal component analysis. Five main components clustering the 19 extracted factors were revealed: cost and government regulations and policies, public awareness and local market, environment and public infrastructure, residential building, and renewable energy technology systems. The implications of this research study assist in guiding governments, regulations and policy makers, marketing agencies, and investors to better understand the concerns and enablers of renewable energy technologies adoption from the public perspective.
Anisotropic dynamics and kinetic arrest of dense colloidal ellipsoids in the presence of an external field studied by differential dynamic microscopy

Differential dynamic microscopy is used to study the arrest transition in concentrated suspensions of colloidal ellipsoids. Anisotropic dynamics on the colloidal length scale is ubiquitous in nature. Of particular interest is the dynamics of systems approaching a kinetically arrested state. The failure of classical techniques for investigating the dynamics of highly turbid suspensions has contributed toward the limited experimental information available up until now. Exploiting the recent developments in the technique of differential dynamic microscopy (DDM), we report the first experimental study of the anisotropic collective dynamics of colloidal ellipsoids with a magnetic hematite core over a wide concentration range approaching kinetic arrest. In addition, we have investigated the effect of an external magnetic field on the resulting anisotropic collective diffusion. We combine DDM with small-angle x-ray scattering and rheological measurements to locate the glass transition and to relate the collective short- and long-time diffusion coefficients to the structural correlations and the evolution of the zero shear viscosity as the system approaches an arrested state.
Four-dimensional micro-building blocks

We proposed a programmable modular design strategy for constructing complex 3D-to-3D shape morphing systems. Four-dimensional (4D) printing relies on multimaterial printing, reinforcement patterns, or micro/nanofibrous additives as programmable tools to achieve desired shape reconfigurations. However, existing programming approaches still follow the so-called origami design principle to generate reconfigurable structures by self-folding stacked 2D materials, particularly at small scales. Here, we propose a programmable modular design that directly constructs 3D reconfigurable microstructures capable of sophisticated 3D-to-3D shape transformations by assembling 4D micro-building blocks. 4D direct laser writing is used to print two-photon polymerizable, stimuli-responsive hydrogels to construct building blocks at micrometer scales. Denavit-Hartenberg (DH) parameters, used to define robotic arm kinematics, are introduced as guidelines for how to assemble the micro-building blocks and plan the 3D motion of assembled chain blocks. Last, a 3D-printed microscaled transformer capable of changing its shape from a race car to a humanoid robot is devised and fabricated using the DH parameters to guide the motion of various assembled compartments.
Successful clinical application of pre-implantation genetic diagnosis for infantile neuroaxonal dystrophy

Infantile neuroaxonal dystrophy (INAD) is a rare, lethal, autosomal recessive neurodegenerative disease and leads to progressive impairment of movement and cognition. A couple with a proband child with calcium-independent group VI phospholipase A2 (PLA2G6)-associated INAD and a previous affected pregnancy sought pre-implantation genetic diagnosis (PGD) to bear a healthy child. Intracytoplasmic sperm injection treatment was performed and 15 blastocystic embryos were obtained at days 5 and 6, and these biopsies were amplified. PGD was performed by next-generation sequencing-based linkage analysis in conjunction with aneuploidy screening. Only two embryos were considered for transfer. In the second frozen-thawed embryo transfer cycle, transfer of a mosaic PLA2G6 c.692G>T heterozygous embryo resulted in a singleton ongoing pregnancy. Prenatal diagnosis was performed using amniotic fluid cells, providing results consistent with those of PGD. The aneuploidy screen and karyotype analysis indicated that the chromosomes of the fetus were normal without any mosaicism. The present study reported the first successful PGD for INAD. For parents at risk, this strategy may successfully lead to pregnancies with embryos unlikely to develop INAD, thus providing valuable experience in reproductive management regarding INAD and potentially other single-gene disorders.