Miracle-dz/UniFAR-FTUs huggingface dataset:
FDI, technology & knowledge transfer from Nordic to Baltic countries

The purpose of this study is to examine the intensity of technology and knowledge transfer to the selected Baltic countries through foreign direct investment. The intensity of technology and knowledge transfer across the Baltic countries varies widely, with Estonia showing the leading position in the Baltic region. The amount of foreign direct investment in three countries is linked with the level of technology and knowledge transfer. It is indicated that during the Financial Crisis in 2008, the extent of foreign direct ownership changed in all three countries and later recovered. In the aftermath of this disruption, countries recovered their stock Foreign direct investment attraction rates and almost reached their 2004 level. Latvia has achieved a 50 per cent increase among Baltic countries, benefiting from it. Foreign direct investment and technology transfer increased through effective strategies and policies. In contrast, Estonia maintains a sustained stock foreign direct investment and has moderately lower margins than in other Baltic countries. Among countries, Estonia is the dominant stock FDI absorber in the Baltic region and have made significant contributions in the region.
Development of Next Generation LIDAR

We are developing automotive light detection and ranging (LIDAR) sensing technologies based on both CMOS single-photon avalanche diodes (SPADs) and optical scanning systems. CMOS SPADs are highly sensitive detectors that can detect single photons and can form dense pixel arrays. These characteristics offer significant benefits for high-resolution detection. However, SPADs have the disadvantage of being oversensitive to background light due to their digital binary output. An attempt was made to overcome the adverse effect of beam thinning when the reflected beam is scanned, as well as to reduce background light by detecting only for a limited time and area. By combining a coaxial optical scanning system with a one-dimensional SPAD array, pedestrians can be easily detected at a range of up to 50 m. To the best of our knowledge, this is the first real-time CMOS SPAD-based long-range LIDAR in existence. We are also developing a MEMS mirror-scanned LIDAR using a two-dimensional SPAD array. Although this device is inferior to the coaxial type in terms of distance range, it has the advantage of being smaller in size.
Effect of light on Guinea fowl (Numida meleagris) production: A review

Artificial light is highly recognized and used in Guinea fowl production. In Guinea fowl production, access to light is an important factor for both growth and egg production. The eye is a vital sense organ under neuro-endocrine regulation in order to allow sight in animals. Guinea fowls perceive light through photoreceptors that transform the energy contained in photons in biological signals. In the eye, the energy of the photons is transformed by photosensitive pigments in the cones and rods of the retina, and transmitted through neurons to the brain where signals are integrated in an image. Photoreceptors in the hypothalamus are biological transformers that convert photon energy into neural impulses. These impulses affect the endocrine system that control ovarian activity in females and therefore, their reproductive behavior. Many physiological and behavioural aspects of Guinea fowl is affected by lightening. Certain essential function of Guinea fowl like body temperature, feeding, metabolism and digestion is directly or indirectly affected by light. Light also controls the activities of different hormones and affects growth maturation and development of organism. The physical activity of Guinea fowls can be affected by daylength, light intensity, colour and wavelength. The increase in physical activity leads to overall development of Guinea fowl. Hence, Guinea fowl producers and breeders must consider light management activities for higher productivity.
Artificial intelligence for support of the bioethical decision-making of nurses related to end of life issues

Healthprofessionals,especiallynurses,areoftencompelledtomakedecisionsinfaceofbioethicalissuescommonlyrelatedtointensivecareenvironments.Duetotheircomplexitiesandparticularities,theseissuesendupgeneratinggreatphysicalandemotionalstrainontheprofessional.Therefore,thisstudyraisedthehypothesisthatartificialintelligencetechniquescouldassistnursesinmakingbioethicaldecisions.Forthis,agroupoffourspecialistsinthefieldofnursingpreparedandansweredaquestionnairebasedonahypotheticalICUscenario.Thedatacollectedfromtheresponseswerethenusedinthetrainingandevaluationoffivemachinelearning(ML)algorithms.Theresultsculminatedfromacomparativestudybetweenthealgorithmspointtotheveracityofthehypothesisraisedinthisresearch,byshowingthatitispossibletoadoptMLalgorithmstosupportbioethicaldecisionmaking
A Review on Automatic Speech Recognition

With the advancement of speech recognition technologies, there is an increase in the adoption of voice interfaces on mobile-based platforms. While, developing a general purpose Automatic Speech Recognition (ASR) which can understand voice commands is important, the contexts of how people interact with their mobile device change very rapidly. Due to the high processing complexity of the ASR engine, much of the processing of trending data is being carried out on cloud platforms. Changed content regarding news, music, movies and TV series change the focus of interaction with voice based interfaces. Hence ASR engines trained on a static vocabulary may not be able to adapt to the changing contexts. The focus of this paper is to first describe the problems faced in incorporating dynamically changing vocabulary and contexts into an ASR engine. We then propose a novel solution which shows a relative improvement of 38 percent utterance accuracy on newly added content without compromising on the overall accuracy and stability of the system.
Modeling of Wireless Traffic Load in Next Generation Wireless Networks

Software defined WiFi network (SD-WiFi) is a new paradigm that addresses issues such as mobility management, load management, route policies, link discovery, and access selection in traditional WiFi networks. Due to the rapid growth of wireless devices, uneven load distribution among the network resources still remains a challenging issue in SD-WiFi. In this paper, we design a novel four-tier software defined WiFi edge architecture (FT-SDWE) to manage load imbalance through an improved handover mechanism, enhanced authentication technique, and upgraded migration approach. In the first tier, the handover mechanism is improved by using a simple AND operator and by shifting the association control to WAPs. Unauthorized user load is mitigated in the second tier, with the help of base stations (BSs) which act as edge nodes (ENs), using elliptic ElGamal digital signature algorithm (EEDSA). In the third tier, the load is balanced in the data plane among the OpenFlow enabled switches by using the whale optimization algorithm (WOA). Moreover, the load in the fourth tier is balanced among the multiple controllers. The global controller (GC) predicts the load states of local controllers (LCs) from the Markov chain model (MCM) and allocates packets to LCs for processing through a binary search tree (BST). The performance evaluation of FT-SDWE is demonstrated using extensive OMNeT++ simulations. The proposed framework shows effectiveness in terms of bandwidth, jitter, response time, throughput, and migration time in comparison to SD-WiFi, EASM, GAME-SM, and load information strategy schemes.
The Iraqi journal of Veterinary Medicine, 37(2): 199 -205

Mushtaq T. Abdulwahid and Mashaan A. Al-Zuhairy Department of Veterinary Public Health, College of Veterinary Medicine, Baghdad University, Iraq E-Mail: mushtaqsharba2000@yahoo.com Accepted on: 3/6/2013 Summary Four hundred fertile eggs of broiler breeder Ross strain were incubated in commercial Hatchery. Eggs were injected into amniotic fluid on 18 day of incubation after divided into four treatment groups (100 eggs per treatment) with two replicates. First treatment was injected 100 μl of PBS, second treatment was injected with100 μl of inactivated ND vaccine, the third treatment was injected with 100 μl of inactivated ND vaccine and 50 μl vitamin E (oily form) and finally the fourth treatment was injected with 100 μl of inactivated ND vaccine and 50 μl Cod liver oil. All injected eggs were carried back into hatchery for complete hatching process. Hatched chicks were transported to the farm of the Veterinary Medicine College/University of Baghdad for 42 days from 6/10/2011 to 16/11/2011. The hatched chicks from the previously treated groups were distributed into four treatments with two replicates for up to the experimental end. Results of treated groups showed significant (P ≤ 0.05) increase in body weight, weight gain, as well as significant reduce in feed intake and improvement in feed conversion ratio, also, antibody titers against Newcastle disease virus of treated groups showed significant (P ≤ 0.05) increase progressively with age until the end of the experiment compared with broiler chicks of control.
Special Issue on Offensive Language

This paper presents a new, extensible annotation scheme for offensive language data sets. The annotation scheme expands coverage beyond fairly straightforward cases of offensive language to address several cases of complex, implicit, and/or pragmaticallytriggered offensive language. We apply the annotation scheme to create a new Complex Offensive Language Data Set for English (COLD-EN). The primary purpose of this data set is to diagnose how well systems for automatic detection of abusive language are able to classify three types of complex offensive language: reclaimed slurs, offensive utterances containing pejorative adjectival nominalizations (and no slur terms), and utterances conveying offense through linguistic distancing. COLD offers a straightforward framework for error analysis. Our vision is that researchers will use this data set to diagnose the strengths and weaknesses of their offensive language detection systems. In this paper, we diagnose some strengths and weaknesses of a top-performing offensive language detection system by: a) using it to classify COLD, and b) investigating its performance on the 10 fine-grained categories supported by our annotation scheme. We evaluate the system’s performance when trained on five different standard data sets for offensive language detection. Systems trained on different data sets have different strengths and weaknesses, with most performing poorly on the phenomena of reclaimed slurs and pejorative nominalizations. NOTE: This paper contains sensitive and offensive material. The offensive materials are part of a complex puzzle we wish to better understand; they appear in the form of lightly-censored slurs and degrading insults. We do not condone this type of language, nor does it reflect the attitudes or beliefs of the authors.
EM-POSE: 3D Human Pose Estimation from Sparse Electromagnetic Trackers

Fully immersive experiences in AR/VR depend on re-constructing the full body pose of the user without restricting their motion. In this paper we study the use of body-worn electromagnetic (EM) field-based sensing for the task of 3D human pose reconstruction. To this end, we present a method to estimate SMPL parameters from 6-12 EM sensors. We leverage a customized wearable system consisting of wireless EM sensors measuring time-synchronized 6D poses at 120 Hz. To provide accurate poses even with little user instrumentation, we adopt a recently proposed hybrid framework, learned gradient descent (LGD), to iteratively estimate SMPL pose and shape from our input measurements. This allows us to harness powerful pose priors to cope with the idiosyncrasies of the input data and achieve accurate pose estimates. The proposed method uses AMASS to synthesize virtual EM-sensor data and we show that it generalizes well to a newly captured real dataset consisting of a total of 36 minutes of motion from 5 subjects. We achieve reconstruction errors as low as 31.8 mm and 13.3 degrees, outperforming both pure learning- and pure optimization-based methods. Code and data is available under https://ait.ethz. ch/projects/2021/em-pose.
Lignin Interaction with Cellulase during Enzymatic Hydrolysis

The conversion of lignocellulosic biomass into biofuels or biochemicals typically involves a pretreatment process followed by the enzyme-catalyzed hydrolysis of cellulose and hemicellulose components to fermentable sugars. Many factors can contribute to the recalcitrance of biomass, e.g., the lignin content and structure, crystallinity of cellulose, degree of fiber polymerization, and hemicellulose content, among others. However, nonproductive binding between cellulase and lignin is the factor with the greatest impact on enzymatic hydrolysis. To reduce the nonproductive adsorption of enzymes on lignin and improve the efficiency of enzymatic hydrolysis, this review comprehensively summarized the progress that has been made in understanding the interactions between lignin and enzymes. Firstly, the effects of pretreatment techniques on lignin content and enzymatic hydrolysis were reviewed. The effects of lignin content and functional groups on enzymatic hydrolysis were then summarized. Methods for the preparation and characterization of lignin films were assessed. Finally, the methods applied to characterize the interactions between lignin and cellulase were reviewed, and methods for decreasing the nonproductive binding of enzymes to lignin were discussed. This review provides an overview of the current understanding of how lignin hinders the enzymatic hydrolysis of lignocellulosic biomass, and provides a theoretical basis for the development of more economical and effective methods and additives to reduce the interaction of lignin and enzymes to improve the efficiency of enzymatic hydrolysis.
Running head: MULTIMODAL LEARNING OF MANDARIN TONE 1 Mandarin Tone Acquisition as a Multimodal Learning Problem: Tone 3 Diacritic Manipulation

Through a tone knowledge survey and tone identification training task, I explore the extent to which visual representation of tone affects tone identification and tone learning. I use T3 as a case study in understanding the extent to which visual representation affects identification. Recent work on tone learning suggests there may be a link between different multimodal cueing mechanisms and efficient learning of tones. McGinnis (1997) finds that Pinyin diacritics are one of the most effective representation systems for learning Mandarin tones. However, Pinyin is also historically used as a descriptive representation of native speaker phonological knowledge. The dual purpose of the visual representation systems of tone creates a tension between a descriptive model of tone and an L2 learning aid. T3 shows extreme variation from a full T3 that falls and rises to a low T3. Zhang (2018) claims that Mandarin learners are not taught about T3 low features. The first experiment in this study found that current Mandarin learners disproportionately lack explicit knowledge of T3 low features but not other T3 features or any other tones’ features. Next, a tone identification training task was conducted to see if changing the visualization of T3 to low T3 would affect the learning of T3 and other tones. Findings show that T3 and T2 identification improved in the condition where T3 is represented visually as a low tone rather than full tone. Together, these findings suggest that many Mandarin learners do not know about T3 low features and that learning T3 with a low-tone visualization can positively affect the identification of T3 and T2. Using multimodal means to improve the representation of T3 may help reduce confusion in early stages and remedy long term acquisition problems of T3 and T2.
Formation and evolution of secondary organic aerosols derived from urban-lifestyle sources: vehicle exhaust and cooking emissions

Abstract. Vehicle exhaust and cooking emissions are closely related to the daily life of city dwellers. Here, we defined the secondary organic aerosols (SOAs) derived from vehicle exhaust and cooking emissions as “urban-lifestyle SOAs” and simulated their formation using a Gothenburg potential aerosol mass reactor (Go:PAM). The vehicle exhaust and cooking emissions were separately simulated, and their samples were defined as “vehicle group” and “cooking group”, respectively. After samples had been aged under 0.3–5.5 d of equivalent photochemical age, these two urban-lifestyle SOAs showed markedly distinct features in the SOA mass growth potential, oxidation pathways, and mass spectra. The SOA/POA (primary organic aerosol) mass ratios of vehicle groups (107) were 44 times larger than those of cooking groups (2.38) at about 2 d of equivalent photochemical age, according to the measurement of scanning mobility particle sizer (SMPS). A high-resolution time-of-flight aerosol mass spectrometer was used to perform a deeper analysis. It revealed that organics from the vehicle may undergo the alcohol and/or peroxide and carboxylic acid oxidation pathway to produce abundant less and more oxidized oxygenated OAs (LO-OOAs and MO-OOAs), and only a few primary hydrocarbon-like organic aerosols (HOAs) remain unaged. In contrast, organics from cooking may undergo the alcohol and/or peroxide oxidation pathway to produce moderate LO-OOAs, and comparable primary cooking organic aerosols (COAs) remain unaged. Our findings provide an insight into atmospheric contributions and chemical evolutions for urban-lifestyle SOAs, which could greatly influence the air quality and health risk assessments in urban areas.
STABILISATION OF AN UNDERACTUATED CART PENDULUM USING ADAPTIVE SYNERGETIC CONTROL

This work based on the construction of an adaptive synergetic control (ASC), for the cart pendulum system that belongs to second order underactuated systems; this category of systems has a number of actuator less than theirs degrees of freedom, they cannot be stabilized, using a classical feedback law control (FC). Synergetic control (SC) known as robust control technique has the properties of robustness, decoupling and the reduction of order as sliding mode but without chattering and will be used in this paper. The operating principle of the SC consists of forcing the system to evolve towards a dynamic chosen by the designer. In order that the behavior of the system be conform to new or changed circumstances, namely parametric uncertainties and unbounded external disturbances, estimation laws are generated, using the concept of adaptive control. Simulation results show that the ASC of the underactuated cart pendulum gives a good performance in terms of stability and robustness.
A T-similarity Sensitive Information Protection Method Based on Sensitive Information Gradient Partition

Nowadays, data technology has been widely used. Researchers have also made an in-depth exploration on how to effectively protect sensitive information in massive data, including k-anonymity, l-diversity and t-similarity. In this paper, the main ideas of the three technologies are described, and the characteristics of the three technologies are analyzed. By introducing the concept of sensitive gradient when calculating the similarity distance, the defect of not being sensitive to the sensitive attribute value in t-similarity technology is improved and supplemented. Algorithm for t-similarity technology can form more effective protection for sensitive attribute values with high privacy requirements under acceptable time overhead differences, and can meet the needs of practical applications.
A Neural Machine Translation Model Based on Sequence to Dependency

A supervised self-attention network is proposed to introduce the dependency structure into the Transformer model. This scheme is mainly proposed based on the characteristics of the self-attention mechanism in Transformer, which converts the dependency syntax tree into two equivalent adjacency matrices, and then uses the adjacency matrix to supervise the self-attention network in the Transformer’s encoder. So that Transformer learns how to model the dependency structure of the source language. Although this scheme is simple, the effect is remarkable. In particular, there is no need to modify the network structure, as long as two additional loss functions are added in the process of training Transformer. In the decoding process, there is no need to use an external syntactic analyzer to perform syntactic analysis on the source language, but it can automatically construct the dependent syntactic structure of the source language. Experiments demonstrate that the quality of the constructed dependency syntactic structure is also good.
Design of Precise Delivery and Recovery System of Unmanned Aerial Vehicle Equipped with Line Patrol Robot

Based on the actual inspection requirements, this paper fully studies the application scenarios of UAV and robot inspection, and develops an intelligent UAV line inspection robot composite system based on UAV, robot, edge computing, machine vision, artificial intelligence and other technologies, which plays a significant role in promoting the application practice of UAV and robot inspection technology.
Skeleton-based Action Recognition with Two-Branch Graph Convolutional Networks

Graph convolution is a popular technique for action recognition based on human skeleton data. Due to the fact that human skeleton data can be treated as a graph in three dimensions, Graph Convolutional Networks (GCN) represent the input data as a graph structure to perform the recognition task, and thus numerous approaches based on GCN to recognize actions have achieved great results. In spite of the input data being structured as a four-dimensional tensor in GCN, it still can not fully exploit the contained rich action-related information. Therefore, we proposed a new model that the three-dimensional skeleton data is put into both the Convolutional Neural Network (CNN) and the Graph Convolutional Neural Network branches to perform feature extraction in the spatiotemporal dimension separately, then the output information is fused for prediction. Given the richness of time-domain information characteristics, feature extraction is enhanced by increasing the model’s depth. After the pooling layer and the fully connected layer, we concatenate the outputs at the ends of the graph data stream and the convolution data stream to obtain the network’s final output. Finally, the prediction results can be obtained via the SoftMax layer. On the Kinetic 400 dataset, our suggested model outperforms Benchmark STGCN in terms of accuracy. The experiment results indicate that the proposed novel model successfully increases the generalization ability and classification performance for action recognition.
Collaborating with Software Engineers: Perspectives from Non-Software Experts

—Software engineering teams are usually interdisciplinary, consisting of both software engineers and non-software-engineers. While numerous studies have examined the success and failure of software engineering efforts from the perspective of software engineers, little is known about perspectives of expert non-software-engineers. In this study, we interviewed 46 experts across 10 roles at Microsoft (artists, content developers, data scientists, design researchers, designers, electrical engineers, mechanical engineers, product planners, program managers, service engineers) about their collaborations—good and bad—with software engineers. Overall, our experts described great software engineers as masters of their own technical domain, open-minded to the input of others, proactively informing everyone, and seeing the big picture of how pieces fit together. We discuss implications of our findings for practitioners, educators, and researchers.