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Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

Abstract The medicinal attributes of honey appears to overshadow its importance as a functional food. Consequently, several literatures are rife with ancient uses of honey as complementary and alternative medicine, with relevance to modern day health care, supported by evidence-based clinical data, with little attention given to honey’s nutritional functions. The moisture contents of honey extracted from University of Veterinary and Animal Sciences, Lahore honey bee farm was 12.19% while that of natural source was 9.03 ± 1.63%. Similarly, ash and protein contents of farmed honey recorded were 0.37% and 5.22%, respectively. Whereas ash and protein contents of natural honey were 1.70 ± 1.98% and 6.10 ± 0.79%. Likewise fat, dietary fiber and carbohydrates contents of farmed source documented were 0.14%, 1.99% and 62.26% respectively. Although fat, dietary fiber and carbohydrates contents of honey taken from natural resource were 0.54 ± 0.28%, 2.76 ± 1.07% and 55.32 ± 2.91% respectively. Glucose and fructose contents of honey taken out from honeybee farm were 27% and 34% but natural source were 22.50 ± 2.12% and 28.50 ± 3.54%. Glucose and fructose contents of honey taken out from honeybee farm were 27% and 34% but natural source were 22.50 ± 2.12% and 28.50 ± 3.54%. Similarly, sucrose and maltose contents of farmed honey were 2.5% and 12% while in natural honey were 1.35 ± 0.49% and 8.00 ± 1.41% respectively. The present study indicates that such as moisture, carbohydrates, sucrose and maltose contents were higher farmed honey as compared to the natural honey. In our recommendation natural honey is better than farmed honey.

This article considers the task of text style transfer: transforming a specific style of sentence into another while preserving its style-independent content. A dominate approach to text style transfer is to learn a good content factor of text, define a fixed vector for every style and recombine them to generate text in the required style. In fact, there are a large number of different words to convey the same style from different aspects. Thus, using a fixed vector to represent one style is very inefficient, which causes the weak representation power of the style vector and limits text diversity of the same style. To address this problem, we propose a novel neural generative model called Adversarial Separation Network (ASN), which can learn the content and style vector jointly and the learnt vectors have strong representation power and good interpretabilities. In our method, adversarial learning is implemented to enhance our model’s capability of disentangling the two factors. To evaluate our method, we conduct experiments on two benchmark datasets. Experimental results show our method can perform style transfer better than strong comparison systems. We also demonstrate the strong interpretability of the learnt latent vectors.

Abstract Lactobacilli are probiotics with Aflatoxin (AF) detoxification ability, found in fermented products, GIT of animals and environment. Purpose of this study was to investigate the ability of broiler isolates of Lactobacillus against Aflatoxin B1 (AFB1). For this purpose, 5 isolates of Lactobacillus from broiler gut were incubated with 100 ppb AFB1 in aqueous environment and effect of different parameters (cell fractions, time, temperature, pH) on detoxification was determined by HPLC. The ameliorative effect of Lactobacillus salivarius (LS) against AFB1 was studied in broiler. The results revealed that LS (CR. 4) showed the best results (in vitro) as compared to other isolates (L. salivarius (CR. 3, CR, 4), L. agilis (CE. 2.1, CE. 3.1) and L. crispatus (CE. 28). Cell debris of CR. 4 showed significantly higher detoxification (P<0.05). Maximum amount of AFB1 was detoxified at 30°C (97%), pH 4.0 (99%) and 6 h (99.97%). In vivo study showed that AFB1 decreased weight gain (1,269 ± 0.04 gm/ bird), feed consumed (2,161 ± 0.08 gm/ bird), serum total protein (2.42 ± 0.34 gm/ dl), serum albumin (0.5 ± 0.2 2 gm/dl) and antibody titer (4.2 ± 0.83). Liver function enzymes were found (alanine transaminase (ALT): 32 ± 10.7 U/L) and aspartate transaminase (AST): 314.8 ± 27 U/L) elevated in AFB1 fed broilers. Treatment with 1% LS not only decreased the toxic effects of AFB1 (group D) but also improved the overall health of broilers due to its probiotic effects (p<0.05) as compared to control negative (group A). The detoxification ability of LS was better than commercial binder (CB) (0.2% Protmyc). It was concluded that detoxification of AFB1 by Lactobacillus was strain, temperature, pH and time dependent. LS has detoxification ability against AFB1 in vivo.

Time-evolving web and social network graphs are modeled as a set of pages/individuals (nodes) and their arcs (links/relationships) that change over time. Due to their popularity, they have become increasingly massive in terms of their number of nodes, arcs, and lifetimes. However, these graphs are extremely sparse throughout their lifetimes. For example, it is estimated that Facebook has over a billion vertices, yet at any point in time, it has far less than 0.001% of all possible relationships. The space required to store these large sparse graphs may not fit in most main memories using underlying representations such as a series of adjacency matrices or adjacency lists. We propose building a compressed data structure that has a compressed binary tree corresponding to each row of each adjacency matrix of the time-evolving graph. We do not explicitly construct the adjacency matrix, and our algorithms take the time-evolving arc list representation as input for its construction. Our compressed structure allows for directed and undirected graphs, faster arc and neighborhood queries, as well as the ability for arcs and frames to be added and removed directly from the compressed structure (streaming operations). We use publicly available network data sets such as Flickr, Yahoo!, and Wikipedia in our experiments and show that our new technique performs as well or better than our benchmarks on all datasets in terms of compression size and other vital metrics.

Low-precision data representation is important to reduce storage size and memory access for convolutional neural networks (CNNs). Yet, existing methods have two major limitations: (1) requiring re-training to maintain accuracy for deep CNNs and (2) needing 16-bit floating-point or 8-bit fixed-point for a good accuracy. In this article, we propose a low-precision (8-bit) floating-point (LPFP) quantization method for FPGA-based acceleration to overcome the above limitations. Without any re-training, LPFP finds an optimal 8-bit data representation with negligible top-1/top-5 accuracy loss (within 0.5%/0.3% in our experiments, respectively, and significantly better than existing methods for deep CNNs). Furthermore, we implement one 8-bit LPFP multiplication by one 4-bit multiply-adder and one 3-bit adder, and therefore implement four 8-bit LPFP multiplications using one DSP48E1 of Xilinx Kintex-7 family or DSP48E2 of Xilinx Ultrascale/Ultrascale+ family, whereas one DSP can implement only two 8-bit fixed-point multiplications. Experiments on six typical CNNs for inference show that on average, we improve throughput by over existing FPGA accelerators. Particularly for VGG16 and YOLO, compared to six recent FPGA accelerators, we improve average throughput by 3.5 and 27.5 and average throughput per DSP by 4.1 and 5 , respectively.

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, the problem of counting global and local triangles in a graph stream has been widely studied, and numerous triangle counting steaming algorithms have emerged. To improve the throughput and scalability of streaming algorithms, many researches of distributed streaming algorithms on multiple machines are studied. In this article, we first propose a framework of distributed streaming algorithm based on the Master-Worker-Aggregator architecture. The two core parts of this framework are an edge distribution strategy, which plays a key role to affect the performance, including the communication overhead and workload balance, and aggregation method, which is critical to obtain the unbiased estimations of the global and local triangle counts in a graph stream. Then, we extend the state-of-the-art centralized algorithm TRIÈST into four distributed algorithms under our framework. Compared to their competitors, experimental results show that DVHT-i is excellent in accuracy and speed, performing better than the best existing distributed streaming algorithm. DEHT-b is the fastest algorithm and has the least communication overhead. What’s more, it almost achieves absolute workload balance.

ABSTRACT Introduction: Using gene therapy to transfer specific genes to implant therapeutic proteins into damaged tissues is a more promising way to treat sports injuries. The combination of tissue engineering and gene therapy will potentially promote the regeneration and repair of various damaged tissues. Objective: This article explores the adaptive relationship between gene selection therapy and athletes in sports. Methods: We selected students of related majors in sports schools to conduct specific genetic testing and measure the muscle area, fatigue level, muscle damage, and other related indicators before and after exercise. Results: After a series of physical fitness assessments, an increase in the gene sequence, as well as changes in the biochemical indices, were confirmed Conclusions: The muscle gain of the test subject during training is better than other genotypes. Level of evidence II; Therapeutic studies - investigation of treatment results.