Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of additives on fermentation characteristics, nutrient values and in vitro fermentation profile of Neolamarckia cadamba leaf silage

This study was designed to investigate the effects of additives on the ensiling characteristics of Neolamarckia cadamba leaf (NCL) silage. The NCL were allocated to six ensiling treatments, namely control (C-S), uncompacted (UC-S) (negative control), with the addition of sodium sulphite (S-S), with propionate (P-S), with citric acid plus sodium acetate (CA-S), and with glucose (G-S). Fermentation parameters, chemical compositions and in vitro fermentation profile of the silages were investigated after 60 days’ storage. Compared with the control, the application of additives improved dry matter recovery (DMR), while UC-S had a poorer DMR. All the silages increased in pH value and acetate content, except for P-S. Lactate concentration was higher in S-S and G-S, and lower in UC-S, P-S, and CA-S. P-S, CA-S, and G-S had increased water soluble carbohydrate (WSC) concentrations. Soluble crude protein was higher in UC-S and CA-S. All the treated silage had greater gas production at slower rates, except for S-S. The fermentation of S-S, P-S and CA-S resulted in higher content of short-chain fatty acids and microbial crude protein relative to UC-S. S-S (38.82%) and CA-S (37.89%) had higher organic matter digestibility relative to the control (36.43%). S-S (42.80%) and P-S (43.99%) had higher crude protein digestibility, which was lower in UC-S (30.73%) when compared with the control (37.74%). There was no difference in the predicted available energy of these silages. The low ammonium nitrogen ratio and the high proportion of true protein inferred that protein was well preserved in these silages, even the uncompacted silage. These results suggest that sulphite and organic acids, such as propionate and citrate, could be an option to improve the feed quality of NCL silage, and its great protein conservation deserves more attention. Keywords: feed quality, in vitro technique, Neolamarckia cadamba , ruminant nutrition, silage processing
Towards the Prosody of Persuasion in Competitive Negotiation. The Relationship Between f0 and Negotiation Success in Same Sex Sales Tasks

Prosodic features play a key role in a speaker’s persuasive power. However, previous studies on persuasion have been focused on public speaking and the signaling of leadership, while acoustic studies on negotiation have been primarily concerned with cooperative interactions. In this study we are taking a first step into investigating the role of acousticprosodic cues in competitive negotiation, focusing on f0 in same-sex negotiations. Specifically, we ask whether the prosodic correlates of persuasive speech are comparable for public speaking and negotiation. Sixty-two speakers (44f/18m) in 31 same-sex pairs participated in a competitive task to bargain over the selling price of a fictional company. We find a significant correlation between a speaker’s f0 features and his/her interlocutor’s concession range. In line with findings from public speaking, greater f0 excursions and higher f0 minima correlate with negotiation success. However, while the female speakers also show an expected elevated f0 mean, the opposite is the case for male speakers. We propose that in competitive negotiation, displaying dominance may overrule showing passion in contrast to public speaking, but only for male speakers.
Criminisi Image Concealment Algorithm Based on Priority Function and Blocking Matching Principle

In order to optimize the performance of Criminisi algorithm in repairing the large defect area, this paper proposes a digital image restoration model of structural continuity optimization Criminisi algorithm for the shortcomings of the structural connectivity of the damaged image. Firstly, a new priority function is designed to avoid the influence of zero of the confidence item and the data item, and the proportion of the structural component in the region to be repaired is strengthened, and the structural continuity in the repair result is strengthened. And then to improve the repair rate, without affecting the repair results under the premise of a faster search and more comprehensive block matching criteria, search for matching block using the local search, and the use of improved block matching criteria, access ,more reliable matching block at the same time, speed up the repair rate. Finally, the experimental results show that the improved Criminisi algorithm is more effective and faster in repairing large defect areas.
Fingerprinting Tooling used for SSH Compromisation Attempts

In SSH brute forcing attacks, adversaries try a lot of different username and password combinations in order to compromise a system. As such activities are easily recognizable in log files, sophisticated adversaries distribute brute forcing attacks over a large number of origins. Effectively finding such distributed campaigns proves however to be a difficult problem. In practice, when adversaries would spread out bruteforcing over multiple sources, they would likely reuse the same kind of software across all of these origins to simplify their operation and reduce cost. This means if we are able to identify the tooling used in these attempts, we could cluster similar tool usage into likely collaborating hosts and thus campaigns. In this paper, we demonstrate that it is possible to utilize cipher suites and SSH version strings to generate a unique fingerprint for a brute-forcing tool used by the attacker. Based on a study using a large honeynet with over 4,500 hosts, which received approximately 35 million compromisation attempts over the period of one month, we are able to identify 49 tools from the collected data, which correspond to off-the-shelf tools, as well as custom implementations. The method is also able to fingerprint individual versions of tools, and by revealing mismatches between advertised and actually implemented features detect hosts that spoof identifying information. Based on the generated fingerprints, we are able to correlate login credentials to distinguish distributed campaigns. We uncovered specific adversarial behaviors, tactics and procedures, frequently exhibiting clear timing patterns and tight coordination.
Trainable Dynamic Subsampling for End-to-End Speech Recognition

Jointly optimised attention-based encoder-decoder models have yielded impressive speech recognition results. The recurrent neural network (RNN) encoder is a key component in such models – it learns the hidden representations of the inputs. However, it is difﬁcult for RNNs to model the long sequences characteristic of speech recognition. To address this, subsampling between stacked recurrent layers of the encoder is commonly employed. This method reduces the length of the input sequence and leads to gains in accuracy. However, static subsampling may both include redundant information and miss relevant information. We propose using a dynamic subsampling RNN (dsRNN) encoder. Unlike a statically subsampled RNN encoder, the dsRNN encoder can learn to skip redundant frames. Furthermore, the skip ratio may vary at different stages of training, thus allowing the encoder to learn the most relevant information for each epoch. Although the dsRNN is unidirectional, it yields lower phone error rates (PERs) than a bidirectional RNN on TIMIT. The dsRNN encoder has a 16.8% PER on the TIMIT test set, a considerable improvement over static subsampling methods used with unidirectional and bidirectional RNN encoders (23.5% and 20.4% PER respectively).
Adapting Transformer to End-to-End Spoken Language Translation

Neural end-to-end architectures for sequence-to-sequence learning represent the state of the art in machine translation (MT) and speech recognition (ASR). Their use is also promising for end-to-end spoken language translation (SLT), which combines the main challenges of ASR and MT. Exploiting existing neural architectures, however, requires task-speciﬁc adaptations. A network that has obtained state-of-the-art results in MT with reduced training time is Transformer. However, its direct application to speech input is hindered by two limitations of the self-attention network on which it is based: quadratic memory complexity and no explicit modeling of short-range dependencies between input features. High memory complexity poses constraints to the size of models trainable with a GPU, while the inadequate modeling of local dependencies harms ﬁnal translation quality. This paper presents an adaptation of Transformer to end-to-end SLT that consists in: i) downsampling the input with convolutional neural networks to make the training process feasible on GPUs, ii) modeling the bidimensional nature of a spectrogram, and iii) adding a distance penalty to the attention, so to bias it towards local context. SLT experiments on 8 language directions show that, with our adaptation, Transformer outperforms a strong RNN-based baseline with a signiﬁcant reduction in training time.
The Prevalence of Chronic Kidney Disease and Associated Factors Among Patients Admitted

How to cite this article: Rwegerera GM, Bayani M, Taolo EK, Habte D. The prevalence of chronic kidney disease and associated factors among patients admitted at princess marina hospital, Gaborone, Botswana. Niger J Clin Pract 2017;20:313-9. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
The Artificial Jack of All Trades : The Importance of Generality in Approaches to Human-Level Artificial Intelligence

In this paper, we advocate the position that research efforts working towards solving human-level AI necessarily have to rely on general mechanisms and (models of) cognitive capacities, with domainspecific systems or task-dependent approaches only being of minor help towards the final goal. We revisit psychological research on intelligence and the application of psychometric methods in AI, before discussing IGOR2 and HDTP under the light of the previous considerations as examples of systems respectively implementing a general mechanism or modeling a general capacity. In the conclusion, we summarize our considerations and point out three characteristics we consider suitable candidates for serving as generally recommendable properties of HLAI systems.
Relationship between Malnutrition Risks and Functional Abilities of the Elderly in Home Care Services

Background: Malnutrition is an important and common public hea lth problem that is frequently not diagnosed earlier among the elderly living in a home, nursing home, or hospital environment. Nurses can prevent the development of malnutrition and loss of functional ability in the elderly by evaluating malnutrition r isks. Objective: This research aimed to determine the relationship between malnutrition risks and functional abilities of the elderly living in a home environment, while revealing malnutrition risks and other affecting fa ctors. Methods: This research was planned to be descriptive and co rrelational, with a total of 288 elderly participan ts (73.8 ± 7.2) (aged >65 years) being included. Three qu stionnaires were administered to gather data on demographic characteristics, malnutrition risks and functional abilities. Result: Upon examination of Mini Nutritional Assessment sc ores, 47.2% of the participants were found to have malnutrition risks, while 15.6% were identified as malnourished. Age range and education status were f ound to have an effect on malnutrition risk, whereas gender , socioeconomic status, and loneliness did not. A s tatistically significant difference was determined between malnu trition risk and functional ability (X = 143.265; p < 0.01). Additionally, a statistically significant correlati on was determined between Mini Nutritional Assessme nt and Bartel Index scores (r = 0.613; p = 0.000). Through stepwise multiple linear regression analysis, we d etermined that having children, cerebrovascular diseases, dep ression or dementia (including Alzheimer’s disease) , h alth problems related to the digestive system, lack of a ppetite, body mass index, mid-upper arm and calf circumference, and Bartel Index scores significantl y affected the Mini Nutritional Assessment scores ( R = 0.781; p < 0.01). Conclusion: This research revealed that a statistically signif icant positive correlation exists between malnutrition risks and functional abilities of the elderly living in a home environment, and that impr ovement in functional ability independence reduces such risks.
Too Good to Ignore ? A Primer on Listed Penny Stocks

This paper identifies distinct characteristics of U.S. penny stocks listed on NYSE, AMEX, and NASDAQ. They are characterized by high return, high beta, small capitalization, high bookto-market ratio, high idiosyncratic volatility, and poor liquidity. The liquidity costs as measured by Gibbs effective transaction costs for penny stocks are more than twice as large as the magnitude of non-penny stocks. Abnormal returns of penny stocks estimated using the FamaFrench (1993) three-factor model and the Carhart (1997) four-factor model become insignificant after an illiquidity factor is introduced to the asset pricing model. Although abnormal returns become insignificant as an additional risk factor is considered, zero-cost portfolios built on firm characteristics (such as firm size, value, momentum, and idiosyncratic volatility) yield significant profitability. JEL classification: G14; G18
Internet Advertising with Information Congestion

I estimate a structural model of the demand for the display advertising on Korean Internet portals that accounts for the limited attention that website users devote to ad messages. The theoretical model assumes heterogeneity in the attention span distribution of users. I simulate the probability that a user pays attention to a message posted on the website. This, in turn allows me to identify the extent to which the information congestion affects the advertising demand. The estimated model enables me to predict the probability that a message is seen by a user of one particular website in a given month. These probabilities range from . 36 to . 67 by websites. The probability is decreasing in the intensity of the users’ activity on the website and is increasing in the age and the education level of website’s users. Male users are shown to have lower probability than females do. The results are compared with those given by a model where the information congestion would be ignored. The congestion being ignored, the platform’s market power turns out to be underestimated. I separately estimate the user demand with mixed-logit model and find that 92.17% of users have negative coefficients of the ad level on a platform. Finally, the estimated user-advertising elasticity is shown to be lower than the estimated attention-advertising elasticity.
Investigating Radical-Based End-to-End Speech Recognition Systems for Chinese Dialects and Japanese

Training automatic speech recognition (ASR) systems for East Asian languages (e.g., Chinese and Japanese) is tough work because of the characters existing in the writing systems of these languages. Traditionally, we first need to get the pronunciation of these characters by morphological analysis. The end-to-end (E2E) model allows for directly using characters or words as the modeling unit. However, since different groups of people (e.g., residents in Chinese mainland, Hong Kong, Taiwan, and Japan) adopts different writing forms for a character, this also leads to a large increase in the number of vocabulary, especially when building ASR systems across languages or dialects. In this paper, we propose a new E2E ASR modeling method by decomposing the characters into a set of radicals. Our experiments demonstrate that it is possible to effectively reduce the vocabulary size by sharing the basic radicals across different dialect of Chinese. Moreover, we also demonstrate this method could also be used to construct a Japanese E2E ASR system. The system modeled with radicals and kana achieved similar performance compared to state-of-the-art E2E system built with word-piece units.
Aero-tactile integration in Mandarin

Previous research has shown that audio-aligned air puffs applied to the skin can enhance the perception of speech audio [12]. In this study, we applied dynamically varying air flow during two-way forced-choice identification of Mandarin words, comparing them to results of a study on English which showed perceptual enhancement for both stops and fricatives [6]. Two differences emerged: Psychometric testing identified the 80% accuracy signal-to-noise ratio for Mandarin words to be at 1.1 dB SNR, compared to -9.0 for English nonsense syllables. In addition, in Mandarin, aero-tactile stimuli only enhanced classification of voiceless stops, whereas it enhanced classification of voiceless stops and fricatives in English. These differences may partially result from the interaction of high conditional acoustic entropy in Mandarin compared to English [24] and air flow – that is, the Mandarin syllables had to be played with more preserved acoustic information, weakening the potential effect of air flow.
Design and structural optimization of topological interlocking assemblies

We study assemblies of convex rigid blocks regularly arranged to approximate a given freeform surface. Our designs rely solely on the geometric arrangement of blocks to form a stable assembly, neither requiring explicit connectors or complex joints, nor relying on friction between blocks. The convexity of the blocks simplifies fabrication, as they can be easily cut from different materials such as stone, wood, or foam. However, designing stable assemblies is challenging, since adjacent pairs of blocks are restricted in their relative motion only in the direction orthogonal to a single common planar interface surface. We show that despite this weak interaction, structurally stable, and in some cases, globally interlocking assemblies can be found for a variety of freeform designs. Our optimization algorithm is based on a theoretical link between static equilibrium conditions and a geometric, global interlocking property of the assembly---that an assembly is globally interlocking if and only if the equilibrium conditions are satisfied for arbitrary external forces and torques. Inspired by this connection, we define a measure of stability that spans from single-load equilibrium to global interlocking, motivated by tilt analysis experiments used in structural engineering. We use this measure to optimize the geometry of blocks to achieve a static equilibrium for a maximal cone of directions, as opposed to considering only self-load scenarios with a single gravity direction. In the limit, this optimization can achieve globally interlocking structures. We show how different geometric patterns give rise to a variety of design options and validate our results with physical prototypes.
On Optimal Ingress Treatment of Delay-Sensitive Traffic in Multi-Class OBS Systems

We examine the effect of optical switching speed and edge traffic-grooming strategies on the performance of premiumclass traffic in multi-class OBS networks. While previous studies have examined optimal burst assembly and optimal offset provisioning, this is the first study to examine both simultaneously and the first study to include the effects of finite optical switching speed. We find that the minimum achievable premium-class blocking probability is limited by the optical switch reconfiguration time and the premium-class delay requirements. We present an analytical method for finding the optimal combination of burst-assembly timeouts and offset sizes at the ingress of the network that maximizes the premium-class performance. Via a numerical study, we examine the relationship between optical switching speed and the maximum achievable premium-class performance. We find that there exists a cutoff switching speed beyond which further speed-ups yield negligible throughput gains. We give a direct expression for this cutoff switching speed as a function of the premium-class delay constraint and desired low-class switching overhead. For a system in which the premium-class delay guarantee is 20 ms, this cutoff was found to occur at approximately 10 μs.
Effect of epidemiological factors on percent disease index of rose powdery mildew caused by Podosphaera pannosa ( Wallr . ) de

The effect of epidemiological factors was studies on severity (disease index) of rose powdery mildew in cultivar Lutin during the years 2015 and 2016. The disease was first noticed in 2nd week of April in both the years and the disease index ranged from 6.92 to 48.76 per cent and 4.77 to 47.93 per cent in the year 2015 and 2016. respectively. There was a progressive increase in the disease severity upto 2nd week of June and was recorded maximum at this period during both years of study when the temperature remained 24.6 °C, cumulative rainfall (25.5 mm), mean relative humidity (61.5%) and total sunshine (4.6 hrs). Simple correlation between per cent disease index, temperature was 0.910, cumulative rainfall (-0.545), average relative humidity (0.616) and total sunshine hours (-0.760). While the partial correlation between per cent disease index, temperature was 0.475, cumulative rainfall (-0.872), average relative humidity (0.072) and total sunshine hours (-0.273). However, multiple regression equation indicated powdery mildew severity was dependent upon meteorological factors about 97.2 per cent for the disease development during both years i.e. 2015 and 2016.
A Genetic Algorithm for MinMax k-Chinese Postman Problem with Applications to Bridge Inspection

Bridge inspections ensure transportation infrastructure safety and save lives but current manual bridge inspections can be slow and costly. Automated bridge inspection research mitigates these problems and we investigate a multirobot approach to automated bridge inspection by mapping the inspection problem to the well known k-Chinese Postman problem and using multiple robots for faster, more cost-effective, and more standardized bridge inspections. We first show that a genetic algorithm quickly approaches the optimal solution to the 1-postman problem corresponding to a single robot inspecting a bridge. Then, we use the same genetic algorithm to efficiently solve the more difficult, multi-robot, NP-hard, MinMax k-postman problem (k > 1). The genetic algorithm’s solutions to this problem represent robot paths that traverse (and thus inspect) every truss at least once and that minimize the length of the longest path traversed by any of the k robots - thus minimizing time and distributing the workload. These simulation results from our immersive bridge inspection simulation and training system built with the Unity3D game engine, show that our genetic algorithm quickly and efficiently produces good paths, and in addition, achieves approximately linear speedup for each robot added to the inspection task.