Miracle-dz/UniFAR-FTUs huggingface dataset:
A new highland species of dwarf litter frog genus Leptobrachella ( Amphibia : Anura : Megophryidae ) from Sarawak

A new highland species of the dwarf litter frog, Leptobrachella itiokai, is described from Gunung Mulu National Park, northern Sarawak, East Malaysia. It occurs syntopically with another highland species, L. brevicrus, but differs from it by a unique call that may be described as a chirp, with dominant frequency of ca. 10 kHz. The new species is morphologically distinguishable from congeners by the combination of the following traits: rounded ventrolateral glands usually not in series; side of body with small black spots; ventrum entirely dark-coloured with tiny light spots; male with thread-like lineae masculinae.
Automatically Extracting Meaning From Legal Texts: Opportunities and Challenges

This paper surveys three basic legal-text analytic techniques–ML, network diagrams, and question answering (QA)–and illustrates how some currently available commercial applications employ or combine them. It then examines how well the text analytic techniques can answer legal questions given some inherent limitations in the technology. In more detail, ML refers to computer programs that use statistical means to induce or learn models from data with which they can classify a document or predict an outcome for a new case. Predictive coding techniques employed in e-discovery have already introduced ML from text into law firms. Network diagrams graph the relations between objects and can assist in making legal information retrieval smarter. The objects may be legal cases, statutory provisions, reference concepts, or communications nodes. Finally, QA systems search large text collections to locate texts or parts of texts that directly answer a user's question. IBM's Jeopardy-game-winning Watson program is, perhaps, the most famous example of a QA system.
50 Ways to Leak Your Data: An Exploration of Apps' Circumvention of the Android Permissions System

Modern smartphone platforms implement permission-based models to protect access to sensitive data and system resources. However, apps can circumvent the permission model and gain access to protected data without user consent by using both covert and side channels. Side channels present in the implementation of the permission system allow apps to access protected data and system resources without permission; whereas covert channels enable communication between two colluding apps so that one app can share its permission-protected data with another app lacking those permissions. Both pose threats to user privacy. In this work, we make use of our infrastructure that runs hundreds of thousands of apps in an instrumented environment. This testing environment includes mechanisms to monitor apps' runtime behaviour and network traffic. We look for evidence of side and covert channels being used in practice by searching for sensitive data being sent over the network for which the sending app did not have permissions to access it. We then reverse engineer the apps and third-party libraries responsible for this behaviour to determine how the unauthorized access occurred. We also use software fingerprinting methods to measure the static prevalence of the technique that we discover among other apps in our corpus. Using this testing environment and method, we uncovered a number of side and covert channels in active use by hundreds of popular apps and third-party SDKs to obtain unauthorized access to both unique identifiers as well as geolocation data. We have responsibly disclosed our findings to Google and have received a bug bounty for our work.
Accumulation of Heavy Metals from Contaminated Wastewater by Aquatic Plant Lemna minor and Their Biochemical Effects on it

Toxic metal contamination of soil, aqueous waste streams and groundwater causes major environmental and human health problems. The most commonly used methods for dealing with heavy metal pollution are still extremely costly. Use of aquatic plants to extract, sequester and/or detoxify pollutants is a new and powerful technique for environmental cleanup. Plants are ideal agents for soil and water remediation because of their unique genetic, biochemical and physiological properties. The aim of this work is to evaluate the potential of free floating duck weed Lemna minor to remove heavy metals from wastewater and their biochemical effect on it. One-month laboratory experiments were conducted to mark the percentage removal of different heavy metals at different concentrations and the effect of heavy metals on nitrate reductase activity, total chlorophyll and protein contents of the plant. Approximately 93% of total heavy metal induced-toxicity appears resulting in the reduced activities of nitrate reductase, total chlorophyll and protein content of the plant. The results recommended the use of Lemna minor to ameliorate the wastewaters contaminated with heavy metals.
xploring the effects of roadway characteristics on the frequency and everity of head-on crashes : Case studies from Malaysian Federal oads

Head-on crashes are among the most severe collision types and of great concern to road safety authorities. Therefore, it justifies more efforts to reduce both the frequency and severity of this collision type. To this end, it is necessary to first identify factors associating with the crash occurrence. This can be done by developing crash prediction models that relate crash outcomes to a set of contributing factors. This study intends to identify the factors affecting both the frequency and severity of head-on crashes that occurred on 448 segments of five federal roads in Malaysia. Data on road characteristics and crash history were collected on the study segments during a 4-year period between 2007 and 2010. The frequency of head-on crashes were fitted by developing and comparing seven count-data models including Poisson, standard negative binomial (NB), random-effect negative binomial, hurdle Poisson, hurdle negative binomial, zeroinflated Poisson, and zero-inflated negative binomial models. To model crash severity, a random-effect generalized ordered probit model (REGOPM) was used given a head-on crash had occurred. With respect to the crash frequency, the random-effect negative binomial (RENB) model was found to outperform the other models according to goodness of fit measures. Based on the results of the model, the variables horizontal curvature, terrain type, heavy-vehicle traffic, and access points were found to be positively related to the frequency of head-on crashes, while posted speed limit and shoulder width decreased the crash frequency. With regard to the crash severity, the results of REGOPM showed that horizontal curvature, paved shoulder width, terrain type, and side friction were associated with more severe crashes, whereas land use, access points, and presence of median reduced the probability of severe crashes. Based on the results of this study, some potential countermeasures were proposed to minimize the risk of
A COMPREHENSIVE DESCRIPTIVE TOOL TO DESIGN COMPLEX SERVICES

This article aims at tackling the issue of complex-services design. Such design can be apprehended from different perspectives: new service design, service production optimisation, in service improvement, and so on. Many design tools, either specifically developed for services or adapted from the “product world” exist. Some date back to the 1960. However, knowing which tool to use, for which service and when is often a challenge for practitioners. Our contribution engages in a “specification” effort towards an integrative service tool or method that could be used in any situation. We ground this effort in a literature review on the specificities of services and on the evaluation of existing service design tools.
User-Defined Shape Constraints in SAC

We propose a method called user-defined constraints specifically for shape-generic multi-dimensional array programming. Our proposed technique allows programmers to make implicit constraints in the domain and codomain of functions explicit. This method can help compilers to generate more reliable code, improve performance through better optimization and improve software documentation. We propose and motivate a syntax extension for the functional array language SAC and describe steps to systematically transform source-level constraints into existing intermediate code representations. We discuss ways of statically resolving constraints through aggressive partial evaluation and propose some form of syntactic sugar that blurs the line between user-defined constraints and fullyfledged dependent types.
Comparison of metaheuristic algorithms for pump operation optimization

Pumping cost constitutes the main part of the overall operating cost of water distribution systems. There are different optimization formulations of the pumping cost minimization problem including those with application of continuous and integer programming approaches. To date mainly various metaheuristics have been applied to solve this problem. However, the comprehensive comparison of those metaheuristics has not been done. Such a comparison is important to identify strengths and weaknesses of different algorithms which reflects on their performance. In this paper, we present a methodology for comparative analysis of widely used metaheuristics for solving the pumping cost minimization problem. This methodology includes the following comparison criteria: (a) the “optimal solution" obtained; (b) the efficiency; and (c) robustness. Algorithms applied are: particle swarm optimization, artificial bee colony and firefly algorithms. These algorithms were applied to one test problem available in the literature. The results obtained demonstrate that the artificial bee colony is the most robust and the firefly is the most efficient and accurate algorithm for this test problem.
SMT with Handmade Phrase Table

現在,機械翻訳システムの分野において,対訳データから自動的に翻訳モデルと言 語モデルを獲得し,翻訳を行う統計翻訳が注目されている. 翻訳モデルでは,原言 語の単語列から目的言語の単語列への翻訳を,フレーズテーブルで管理する. しか しフレーズテーブルはプログラムで自動作成するため,カバー率は高いが信頼性は 低いと考えられる. 一方,手作業で作成した翻訳対は,信頼性は高いがカバー率は 低いと考えられる. そこで,それぞれの長所を生かすために,プログラムで自動作 成したフレーズ対に手作業で作成した翻訳対を追加することで翻訳精度が向上する と考えた. 実験では,手作業で作成された約 13万の翻訳対に翻訳確率を与え,プログラムで自 動作成したフレーズテーブルに追加した. 翻訳実験の結果,BLEUスコアが,日英 翻訳の単文では 0.9%,重複文では 0.8%向上した. また人間による対比較実験を行っ たところ,有効性が確認された. 以上の結果から,統計翻訳において手作業で作成した翻訳対を追加する提案手法は 有効であることが示された. キーワード:統計翻訳,Phrase Table, 手作業
CURRENT ADVANCEMENT IN STOCK PREDICTION METHODOLOGIES 3 . 1 Using support vector machine model

There has been a lot of attempts in building predictive models that can correctly predict the stock price. However, most of these models only focus on different in-market factors such as the prices of other similar stocks. This paper discusses about the efficiency/accuracy of three different neural network models (feedforward, recurrent, and convolutional) in predicting stock prices based on external dependencies such as oil price, weather indexes, etc.
Designing Directional Antennas to Maximize Spatio-temporal Sampling Rates in Multi-hop Clustered Sensor Networks

In a clustered sensor network, a possibly large number of geographically distributed sensor nodes each make local measurements and transmit them toward the clusterhead. The clusterhead may schedule simultaneous transmissions from nodes within the cluster by identifying non-interfering areas in the cluster. Directional antennas that are oriented toward the clusterhead may increase this spatial reuse of the communication channel and thus increase the number of simultaneous, non-interfering transmissions. On the other hand, the directional antennas' narrower coverage areas may reduce connectivity, thus reducing the number of sensors from which data can be collected. In this paper, we investigate this tradeoff between spatial reuse and connectivity in the context of the spatio-temporal sampling rate that can be achieved by the cluster. We also propose a simple analytical approach to determine an antenna beamwidth that improves the spatiotemporal sampling rate by maximizing the transport capacity of the network.
Topic Modeling in Large Scale Social Network Data

The growing popularity of social media such as Twitter and Facebook has made these websites an important source of information. The large amount of data available on these platforms presents new opportunities for mining information about the real world. Because of its widespread usage, a lot of useful information can be extracted from the text available on these social media platforms. It can be used to infer important aspects about the users of these services and about the things happening in their surroundings. This work proposes generative probabalistic models to identify latent topics and sentiments in social media data, mainly Twitter. In contrast to the majority of earlier work done in the field of topic modeling in social media data, this work incorporates various special characteristics of this data- mainly the short-length nature and special tokens like hashtags. The models proposed in work were compared qualitatively and quantitatively against several baseline models for evaluation. Experimental results suggest several improvements over the existing baseline techniques.
Network-driven differences in mobility and optimal transitions among automatable jobs

The potential for widespread job automation has become an important topic of discussion in recent years, and it is thought that many American workers may need to learn new skills or transition to new jobs to maintain stable positions in the workforce. Because workers’ existing skills may make such transitions more or less difficult, the likelihood of a given job being automated only tells part of the story. As such, this study uses network science and statistics to investigate the links between jobs that arise from their necessary skills, knowledge and abilities. The resulting network structure is found to enhance the burden of automation within some sectors while lessening the burden in others. Additionally, a model is proposed for quantifying the expected benefit of specific job transitions. Its optimization reveals that the consideration of shared skills yields better transition recommendations than automatability and job growth alone. Finally, the potential benefit of increasing individual skills is quantified, with respect to facilitating both job transitions and within-occupation skill redefinition. Broadly, this study presents a framework for measuring the links between jobs and demonstrates the importance of these links for understanding the complex effects of automation.
Stimulus-Specific Adaptation Decreases the Coupling of Spikes to LFP Phase

Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation (SSA) has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of SSA on the spike–LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms was evaluated. Here, we employed phase locking value method for calculating the SPC. Our data show a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. As a result, the preferred phase of LFP may play a key role in coordinating neuronal spiking activity in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.
Bacterial Dynamics in Supraglacial Habitats of the Greenland Ice Sheet

Current research into bacterial dynamics on the Greenland Ice Sheet (GrIS) is biased toward cryoconite holes, despite this habitat covering less than 8% of the ablation (melt) zone surface. In contrast, the expansive surface ice, which supports wide-spread Streptophyte micro-algal blooms thought to enhance surface melt, has been relatively neglected. This study aims to understand variability in bacterial abundance and production across an ablation season on the GrIS, in relation to micro-algal bloom dynamics. Bacterial abundance reached 3.3 ± 0.3 × 105 cells ml−1 in surface ice and was significantly linearly related to algal abundances during the middle and late ablation periods (R2 = 0.62, p < 0.05; R2 = 0.78, p < 0.001). Bacterial production (BP) of 0.03–0.6 μg C L−1 h−1 was observed in surface ice and increased in concert with glacier algal abundances, indicating that heterotrophic bacteria consume algal-derived dissolved organic carbon. However, BP remained at least 28 times lower than net primary production, indicating inefficient carbon cycling by heterotrophic bacteria and net accumulation of carbon in surface ice throughout the ablation season. Across the supraglacial environment, cryoconite sediment BP was at least four times greater than surface ice, confirming that cryoconite holes are the true “hot spots” of heterotrophic bacterial activity.
Short- and Medium-Term Exposure to Ocean Acidification Reduces Olfactory Sensitivity in Gilthead Seabream

The effects of ocean acidification on fish are only partially understood. Studies on olfaction are mostly limited to behavioral alterations of coral reef fish; studies on temperate species and/or with economic importance are scarce. The current study evaluated the effects of short- and medium-term exposure to ocean acidification on the olfactory system of gilthead seabream (Sparus aurata), and attempted to explain observed differences in sensitivity by changes in the protonation state of amino acid odorants. Short-term exposure to elevated PCO2 decreased olfactory sensitivity to some odorants, such as L-serine, L-leucine, L-arginine, L-glutamate, and conspecific intestinal fluid, but not to others, such as L-glutamine and conspecific bile fluid. Seabream were unable to compensate for high PCO2 levels in the medium term; after 4 weeks exposure to high PCO2, the olfactory sensitivity remained lower in elevated PCO2 water. The decrease in olfactory sensitivity in high PCO2 water could be partly attributed to changes in the protonation state of the odorants and/or their receptor(s); we illustrate how protonation due to reduced pH causes changes in the charge distribution of odorant molecules, an essential component for ligand-receptor interaction. However, there are other mechanisms involved. At a histological level, the olfactory epithelium contained higher densities of mucus cells in fish kept in high CO2 water, and a shift in pH of the mucus they produced to more neutral. These differences suggest a physiological response of the olfactory epithelium to lower pH and/or high CO2 levels, but an inability to fully counteract the effects of acidification on olfactory sensitivity. Therefore, the current study provides evidence for a direct, medium term, global effect of ocean acidification on olfactory sensitivity in fish, and possibly other marine organisms, and suggests a partial explanatory mechanism.
Does Repetitive Negative Thinking Influence Alcohol Use? A Systematic Review of the Literature

Over the past 20 years, researchers have used various methodologies to assess different forms of repetitive negative thinking (RNT) and their influence on alcohol consumption. Contrasting results between clinical and general populations were observed. To summarize the current literature on RNT and alcohol use, a systematic review was conducted according to the Preferred Reporting for Systematic Review and Meta-Analysis (PRISMA) guidelines (Moher et al., 2009). Among the 27 included studies, the seven conducted among patients with alcohol use disorder (AUD) and the three focusing on other adult samples demonstrated a strong positive association between RNT and alcohol use or alcohol-related problems, regardless of the form of RNT. The results were more heterogeneous in the 17 studies conducted among adolescents and students, leading the authors to conclude that the results varied as a function of the severity of alcohol use. The results of this study suggest to focus on RNT from a transdiagnostic perspective in AUD. This processual approach may improve AUD treatment and relapse prevention. Finally, some gaps in the literature must be addressed: (1) the gender differences in the link between RNT and alcohol use and (2) the specific influence of RNT on alcohol use among young adults.
Versican and Tumor-Associated Macrophages Promotes Tumor Progression and Metastasis in Canine and Murine Models of Breast Carcinoma

Versican and tumor-associated macrophages (TAMs) are involved in growth and metastases in several cancers. Here, we investigated the potential role of versican, a matrix proteoglycan, and its correlation with TAMs infiltrates in different stages of two different breast cancer models: spontaneous canine mammary gland carcinomas and the murine 4T1 breast cancer model. The stromal versican expression was correlated with TAMs accumulation in tumors with an advanced stage from spontaneous canine mammary carcinoma samples. Versican expression in mice, identified in late stages of tumor progression, was associated to a high number of peri-tumoral infiltrating TAMs. Indeed, TAMs were related to a pro-inflammatory and pro-angiogenic state in the primary tumor. Furthermore, TAMs accumulation was related to versican expression in the lungs and an increased number of pulmonary metastatic nodules with pulmonary mechanical dysfunction, which was due to leukocyte influx in the airways and elevated growth factor levels in the microenvironment. Thus, we suggest that versican and TAMs as attractive targets for breast cancer therapy.