Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly detection of android malware using One-Class K-Nearest Neighbours (OC-KNN)

The advent of the Android Operating System has recorded a remarkable ground-breaking opportunities in the Technological world. However, this great breakthrough also has a very dark side – an uncontrollable rapid continuous releases of malware in the wild, targeted at the platform and all its information and human assets. The misuse-based approaches adopted by many detection systems do no longer have the rigidity and the tenacity to accommodate the rapid successive releases of malware that come in great volume in order to keep up with active defenses against unknown and novel attacks. Systems that are capable of offering anomaly protection are thus in dire need. This study developed a normality model that is based on One-Class K-Nearest Neighbour (OC-kNN) Machine Learning approach for anomaly detection of Android Malware. The OC-kNN was trained, using WEKA 3.8. 2 Machine Learning Suite, through a semi-supervise procedure that contained mostly benign and a very few outliers Android application samples. The OC-kNN had 88.57% true performance accuracy for normal instances while 71.9% was recorded as true performance accuracy for outliers (unknown) instances. The false alarm rates for both normal and outlier’s instances were recorded as 28.1% and 11.5%. The study concluded that a One-Class Classification model is an effective approach to be used for the detection of unknown Android malware.
Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas> 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.
Computational Model and Simulation of Articulatory Mechanism in Yoruba Voiced Speech

This study examined the physical, electrical and mathematical models used in the dynamics of voiced sound production. It formulated and designed a computational model for the standard Yoruba voiced sounds, and the designed model also implemented. This was with a view to developing a Yoruba speech recognition and text-to-speech model. The mechanism of human speech production articulatory process documented in the existing literature was examined and analysed. The mechanical coupling in the vocal cords of the oral and that of the nasal cavity were studied. Then a computational model, which defined real variables over Standard Yoruba voice speech production process was formulated and designed using algorithm. The design was then implemented using an appropriate numerical computational tool called Matlab. The implemented novel model established that more volume in time of air will be needed for nasal vowels than oral vowels in the production of Yoruba voiced speech. A minimum of 238 cm 3 of air is needed for nasal cavity while a maximum of 171 cm 3 is needed for oral cavity. It was deduced that the change in the damping coefficient along the vocal cord does not affect the response rate of the speech organ whose rise time remains 0.9ms while a change in the spring constant causes changes in the response rate parameters. Whenever damping coefficient is constant, that is, either 0.1 or 0.2 over the points positioned with masses, even if the vocal chords of individuals have different assigned mass values, the speech production is still the same and observed as normal. The study concluded by establishing a computational model for nasalized Yoruba vowels. This model has the utility of serving as a resource for Yoruba speech recognition and text-to-speech application.
INHOMOGENEOUS SPATIO-TEMPORAL POINT PROCESSES ON LINEAR NETWORKS FOR VISITORS’ STOPS DATA

We analyse the spatio-temporal distribution of visitors’ stops by touristic attractions in Palermo (Italy) using theory of stochastic point processes living on linear networks. We first propose an inhomogeneous Poisson point process model, with a separable parametric spatio-temporal first-order intensity. We account for the spatial interaction among points on the given network, fitting a Gibbs point process model with mixed effects for the purely spatial component. This allows us to study first-order and second-order properties of the point pattern, accounting both for the spatio-temporal clustering and interaction and for the spatio-temporal scale at which they operate. Due to the strong degree of clustering in the data, we then formulate a more complex model, fitting a spatio-temporal Log-Gaussian Cox process to the point process on the linear network, addressing the problem of the choice of the most appropriate distance metric.
Classifying highland quality protein maize in bred lines into heterotic groups

Efforts to boost maize productivity can be undertaken through systematic management of heterosis. Combining ability test is a critical step towards identifying a heterotic group of new parental lines. Different heterotic grouping methods has been used by different researchers. Among these, SCA and hybrid mean, hybrid index, combined use of amplified fragment length polymorphism (AFLP), SSR markers and heterotic group’s specific and general combining ability (HSGCA) are the major grouping methods. This study aimed to (i) assign maize inbred lines into heterotic groups and (ii) compare efficiency of different grouping methods. An experiment with 21 maize inbred lines crossed to two testers with known heterotic groups was conducted in 2017. The hybrid and parent experiments were tested together and laid out side by side. This study identified good heterotic grouping procedure. The breeding efficiency of HSGCA was higher by 31.6, 11.0 and 9.6% over joint SCA and hybrid mean, SCA and hybrid index methods, respectively. While the hybrid index method was more efficient than joint SCA and hybrid mean and SCA by 20.1 and 1.3% respectively, the SCA grouping method was more efficient than joint SCA and hybrid mean method by 18.5%. The highest (37%) and lowest (28.1%) breeding efficiency value were scored by HSGCA and hybrid index heterotic grouping method, respectively. Based on the result, HSGCA grouping method was more efficient. The variable heterotic grouping of the 21 newly developed QPM lines in this study indicated that different heterotic grouping methods have different efficiency in grouping the germplasms. Key words: General combing ability, heterotic-group, inter-group, specific combing ability, within-group, Zea mays.
Harms of AI

This essay discusses several potential economic, political and social costs of the current path of AI technologies. I argue that if AI continues to be deployed along its current trajectory and remains unregulated, it may produce various social, economic and political harms. These include: damaging competition, consumer privacy and consumer choice; excessively automating work, fueling inequality, ineffi ciently pushing down wages, and failing to improve worker productivity; and damaging political discourse, democracy’s most fundamental lifeblood. Although there is no conclusive evidence suggesting that these costs are imminent or substantial, it may be useful to understand them before they are fully realized and become harder or even impossible to reverse, precisely because of AI’s promising and wide-reaching potential. I also suggest that these costs are not inherent to the nature of AI technologies, but are related to how they are being used and developed at the moment – to empower corporations and governments against workers and citizens. As a result, efforts to limit and reverse these costs may need to rely on regulation and policies to redirect AI research. Attempts to contain them just by promoting competition may be insuffi cient.
First records of Brazilian three-banded armadillo (Tolypeutes tricinctus, Mammalia, Cingulata, Chlamyphoridae) predation by jaguar (Panthera onca, Mammalia, Carnivora, Felidae)

The jaguar Panthera onca and the Brazilian three-banded armadillo Tolypeutes tricinctus are two threatened mammals that coexist in the Caatinga dry forests and the Cerrado savannas of Brazil. Yet, to the best of our knowledge, interactions between these species have not been reported in the literature. Here, we present the first records of P. onca predation on T. tricinctus from two different areas in the Caatinga in northeastern Brazil. We showed that P. onca can pierce the hard carapace of T. tricinctus, which may be possible due to its distinctly strong bite and associated predation behavior. We argue that P. onca may be the most adapted non-human predator to feed on T. tricinctus, and that the smaller body sizes of P. onca individuals in the Caatinga may increase their likelihood to feed on smaller prey, including T. tricinctus. Thus, the originality of our records is probably more related to insufficient research in the areas where these species coexist than to the rarity of this interaction.
AUTOMATIC DISCONNECTION OF ENERGY METER USING GSM & MICROCONTROLLER

: The technology of ‘Electronic Metering’ has been advancing rapidly and there is increased demand for a reliable and efficient Automatic Meter Reading (AMR) system with energy bill recovery. The present invention is related to automatic electricity power cut system which can save manpower needed for collecting the energy meter reading and bill distribution process. This system consists of an electricity permitted device including verification device, processing unit, wireless communication unit & display devices. The purpose of invention is to improve energy meter billing and distribution as well as connecting or disconnecting connections wirelessly. The main purpose of this project is presenting a design of a simple, low cost wireless GSM energy meter and its associated web interface, for automating billing and managing the electricity disconnection, if consumer not paying bill in allotted time. The traditional meter reading method is replaced by this proposed system, enabling remote access of existing energy meter by the energy provider. Also, they can monitor the meter readings regularly without the person visiting every house. A GSM based wireless communication module is integrated with electronic energy meter of each entity to have remote access over the usage of electricity. A PC with a GSM receiver is at the other end, which contains the database acts as the billing point. Live meter reading from the GSM enabled energy meter is sent back to this billing point periodically and these details are updated in a central database. The complete monthly consumption and due bill is messaged back to the customer after processing these data.
Snakemake as a workﬂow engine for reciprocal BLAST analyses

In modern genomic analysis, homology describes the evolutionary relationship between two sequences. In addition to a diverse classiﬁcation of homology, the key concept for evolutionary and comparative genomics is the dichotomous differentiation of homology into orthologous and paralogous sequences. Sets of orthologous genes are used to obtain information regarding gene functions and phylogenetic relationships because orthologs tend to retain their biological function. Various methods have been developed in order to identify orthologous genes. Typically, orthologous and paralogous gene relationships are disentangled by measuring and comparing sequence similarities within sequences of interest. More precisely, when searching orthologous sequences in different genomes, those sequences will most likely match each other as best hits when performing sequence similarity searches. The resulting sequences are called reciprocal best hits (RBHs). The identiﬁcation of RBHs is the most common method to infer putative orthologs in comparative genomic studies. In order to use this method for ﬁnding orthologous sequences, various work steps have to be carried out, some of which can be solved straightforwardly by program code, without requiring considerable additional effort. In this thesis we used the Basic Local Alignment Search Tool (BLAST) command-line program in combination with the workﬂow engine Snakemake and the web framework Django to develop an application that can be used for the inference of RBHs. Snakemake was used to develop a pipeline for the execution of the workﬂow steps, providing the core functionality of the application. Due to web based data deployment, pipeline execution and monitoring, the application enables an easy and user friendly customization of reciprocal BLAST analyses. The application and all necessary software and packages can get installed with Docker, which enables a utilization of the application on every system. As a local database option the non-redundant protein database from NCBI was utilized, however, additional databases can easily be integrated. In order to test the application, a reciprocal BLAST analysis of the circadian clock protein kaiA from Synechococcus elongatus PCC 7942 was conducted. The pipeline successfully identiﬁed 606 RBHs as putative orthologous sequences in a reliable
Using anticlustering to maximize diversity and dispersion: Comparing exact and heuristic approaches

The problem of partitioning a pool of elements arises with different goals. We deal with different approaches to anticluster these elements. This means we seek groups that are similar to each other, and not the elements within those groups. We analyze the bicriterion iterated local search (BILS) heuristic, which combines two ob-jective functions for anticlustering, namely diversity and dispersion. Diversity is the sum of pairwise dissimilarities of all elements within a group. Dispersion is the minimal pairwise dissimilarity between two elements, which exists within one group of a partition. The BILS heuristic seeks solutions, that are Pareto efﬁcient regarding both objectives. Using a heuristic is necessary, as we show NP-hardness of both mentioned objectives. We go into the advantages and limitations of this heuristic. Further, we compare the BILS heuristic with the exchange method, another heuristic approach. The exchange method focuses on the contrary only on one objective, that can be speciﬁed. The BILS heuristic performs better in terms of computation and solution quality for small instances. Finally, we evaluate the quality of solutions of the BILS heuristic with exact solutions. For small instances, solutions are optimal or near-optimal.
OTT_A_261570 10417..10429

Pancreas Center, The Second Affiliated Hospital to Nanjing Medical University, Nanjing 210003, People’s Republic of China Background: Pancreatic cancer is a devastating malignancy with poor prognosis. Metformin, a classic anti-diabetes drug, seems to improve survival of pancreatic cancer patients in some studies. Methods: Cell counting kit-8 assay was used to detect the BxPC-3 and MIAPaCa-2 cell viability after treatment with gemcitabine only or with different concentrations of metformin. The methylation state and expression level of miR-663 were detected by methylation analysis and RT-PCR. Dual-luciferase reporter gene analysis, Western blot and RT-PCR were used to confirm the target of miR-663. Moreover, xenograft experiment was also performed to validate the role of metformin in chemosensitivity in vivo. Results: We found that metformin increased the chemosensitivity of pancreatic cancer cells to gemcitabine, and epithelial–mesenchymal transition (EMT) progress caused by gemcitabine was suppressed by metformin. We further explored the possible molecular mechanisms and it was demonstrated that CpG islands of miR-663 were hypomethylated and relative expression level of miR-663 was up-regulated after treatment of metformin. miR-663, an important cancer suppressor miRNA, was confirmed to increase the chemosensitivity of pancreatic cancer cells by reversing EMT directly targeted TGF-β1. Moreover, we identified that metformin increased the chemosensitivity through up-regulating expression of miR-663. Conclusion: We demonstrated that metformin increased the chemosensitivity of pancreatic cancer cells to gemcitabine by reversing EMT through regulation DNA methylation of miR663.
The Importance of Analyzing Interdependencies to Build a Healthy, Nature-Positive, and Inclusive Food System

Many technological and behavioral measures have been proposed to transition the food system to be healthier, nature-positive, and inclusive. In targeting one specific sustainability goal, these measures often have positive or negative side-effects on the other sustainability targets. Global integrated assessment models (IAMs) of the food and land system are often used to assess the quantitative direction and magnitude of these side-effects. Within this brief, we synthesize the findings of multiple analyses using the land system model MAgPIE to study the effects and side-effects of different measures taken within the land system, such as improving agricultural productivity, land-sparing for biodiversity protection, bioenergy cultivation, and changing food consumption patterns. We find that side-effects of measures are widespread and often impact multiple SDGs. We conclude that measures must be bundled to achieve a sustainable outcome; due to their interrelatedness, any implemented measure should be accompanied by as many measures as there are sustainability targets that are affected by side-effects. While we found large literature on the impact of measures, their side-effects, and their allocative efficiency, we identified few studies that also considered the policies that would lead to the implementation of these measures. This research gap warrants more research, because the type of policy instrument has a major effect on the distributional outcomes and likelihood of success.
Local patterns of spread of influenza A(H3N2) virus in coastal Kenya over a one-year period revealed through virus sequence data

The patterns of spread of influenza A viruses in local populations in tropical and sub-tropical regions are unclear due to sparsity of representative spatiotemporal sequence data. We sequenced and analyzed 58 influenza A(H3N2) virus genomes sampled between December 2015 and December 2016 from nine health facilities within the Kilifi Health and Demographic Surveillance System (KHDSS), a predominantly rural region, covering approximately 891 km2 along the Kenyan coastline. The genomes were compared with 1,571 contemporaneous global sequences from 75 countries. We observed at least five independent introductions of A(H3N2) viruses into the region during the one-year period, with the importations originating from Africa, Europe, and North America. We also inferred 23 virus location transition events between the nine facilities included in the study. International virus imports into the study area were captured at the facilities of Chasimba, Matsangoni, Mtondia, and Mavueni, while all four exports from the region were captured from the Chasimba facility, all occurring to Africa destinations. A strong spatial clustering of virus strains at all locations was observed associated with local evolution. Our study shows that influenza A(H3N2) virus epidemics in local populations appear to be characterized by limited introductions followed by significant local spread and evolution.
Raining Cryptos

I study the effect of hard forks and airdrops on cryptocurrency prices. At the hard fork and airdrop date, holders of the parent coin receive additional coins of a different cryptocurrency. The announcement of a hard fork or airdrop does not affect parent coin prices. In contrast, the distribution of coins immediately decreases the prices of the parent coin by 4.65%. The cumulative average abnormal return over the 5-day post-distribution date equals -12.29%. Importantly, I show that the price drop of the parent coin is significantly lower than the value of the distributed cryptocurrency. This suggests that hard forks and airdrops are partly free money.
Upgraded Graphical Method for the Synthesis of Direct Work Exchanger Networks

Numerous studies on heat integration have made significant progress in reducing external thermal energy in chemical plants. However, the notion of work exchange between high- and low-pressure streams to reduce the costly mechanical energy has not been fully explored. In this paper, an upgraded graphical method is proposed for the synthesis of direct work exchanger networks (WEN) using direct work exchangers under any operating conditions, such as isothermal, isentropic, and polytropic. In this method, the improved composite curves of work sources and work sinks are plotted in the pressure index (μ) versus work (W) diagram. On the basis of thermodynamic and numerical analysis, two improved linear-approximation auxiliary lines for every work sink are presented to assist identifying the feasible match. Several matching rules are proposed to accurately identify the feasible matches between work sinks and work sources. From the proposed method, the maximum amount of mechanical energy recovery, the minimum amou.
BiSECT: Learning to Split and Rephrase Sentences with Bitexts

An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this ‘split and rephrase’ task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than the previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations.