Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning the Spherical Harmonic Features for 3-D Face Recognition

In this paper, a competitive method for 3-D face recognition (FR) using spherical harmonic features (SHF) is proposed. With this solution, 3-D face models are characterized by the energies contained in spherical harmonics with different frequencies, thereby enabling the capture of both gross shape and fine surface details of a 3-D facial surface. This is in clear contrast to most 3-D FR techniques which are either holistic or feature based, using local features extracted from distinctive points. First, 3-D face models are represented in a canonical representation, namely, spherical depth map, by which SHF can be calculated. Then, considering the predictive contribution of each SHF feature, especially in the presence of facial expression and occlusion, feature selection methods are used to improve the predictive performance and provide faster and more cost-effective predictors. Experiments have been carried out on three public 3-D face datasets, SHREC2007, FRGC v2.0, and Bosphorus, with increasing difficulties in terms of facial expression, pose, and occlusion, and which demonstrate the effectiveness of the proposed method.
Adsorption thermodynamics of aqueous ferric ion onto bio-natural grains of rice

The performance of the bio-natural rice grains (BRG) in the removal of a ferric ion from aqueous medium is studied in a batch sorption. The sorption thermodynamics and equilibrium of inorganic aqueous ferric ion onto Bio-natural Grain Rice (BGR) influence of several factors, such as initial metal ion concentration, the amount of adsorbent and temperatures. Maximum removal finds 99.5 % by using 30 mg Lof ferric ions at 37 C and high dosage concentration (60 g L). Langmuir and Freundlich isotherm models can be used to analyze the equilibrium isotherm. The experimental data for the isotherm set are well described by the Langmuir isotherm model (R = 0.991). The largest removal of ferric ion, qmax, is equal 0.7352 mg g -1 at 37 C. The equilibrium parameter, RL, is greater than 0 but less than 1 indicating that the sorption is more favorable. Thermodynamic studies indicated that the adsorption is a spontaneous (DG = 3.19058 kJ.mol) and endothermic process (DH = + 153.343 kJ.mol) with increased entropy (DS = + 589.163 J.mol.K), and the rise of temperature can enhance the sorption. The equilibrium data are found fit into the Dubinin-Radushkevich (D-R) model proofing that the calculated values of for the ferric ion is 1.19225 kJ mol . This " " value indicates for a physical adsorption process onto the BRG adsorbent. The new information that can be learned from this study is the use of the rice grains, the natural and safest adsorbent, to remove the ferric ion from the stream of water. The new information in this study suggests that BRG could be used as a natural adsorbent for removing ferric ions from water.
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success.
Optimized endoscopic autofluorescence spectroscopy for the identification of premalignant lesions in Barrett’s oesophagus

Objective Fluorescence spectroscopy has the potential to detect early cellular changes in Barrett’s oesophagus before these become visible. As the technique is based on varying concentrations of intrinsic fluorophores, each with its own optimal excitation wavelength, it is important to assess the optimal excitation wavelength(s) for identification of premalignant lesions in patients with Barrett’s oesophagus. Methods The endoscopic spectroscopy system used contained five (ultra)violet light sources (&lgr;exc=369–416 nm) to generate autofluorescence during routine endoscopic surveillance. Autofluorescence spectroscopy was followed by a biopsy for histological assessment and spectra correlation. Three intensity ratios (r1, r2, r3) were calculated by dividing the area, A, under the spectral curve of selected emission wavelength ranges for each spectrum generated by each excitation wavelength &lgr;exc as follows . Double intensity ratios were calculated using two excitation wavelengths. Results Fifty-eight tissue areas from 22 patients were used for autofluorescence spectra analysis. Excitation with 395, 405 or 410 nm showed a significant (P⩽0.0006) differentiation between intestinal metaplasia and grouped high-grade dysplasia/early carcinoma for intensity ratios r2 and r3. A sensitivity of 80.0% and specificity of 89.5% with an area under the ROC curve of 0.85 was achieved using 395 nm excitation and intensity ratio r3. Conclusion Double excitation showed no additional value over single excitation. The combination of 395 nm excitation and intensity ratio r3 showed optimal conditions to discriminate nondysplastic from early neoplasia in Barrett’s oesophagus.
UvA-DARE (Digital Academic Repository) Neural Relational Inference for Interacting Systems Neural Relational Inference for Interacting Systems

Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which can often be explained using a simple model of the system’s con-stituent parts. In this work, we introduce the neural relational inference (NRI) model: an unsupervised model that learns to infer interactions while simultaneously learning the dynamics purely from observational data. Our model takes the form of a variational auto-encoder, in which the latent code represents the underlying interaction graph and the reconstruction is based on graph neural networks. In experiments on simulated physical systems, we show that our NRI model can accurately recover ground-truth interactions in an unsupervised manner. We further demonstrate that we can ﬁnd an interpretable structure and predict complex dynamics in real motion capture and sports tracking data.
PERCEIVED EFFECT OF COACHING AND MENTORING ON EMPLOYEE JOB PERFORMANCE AMONG ACADEMIC STAFF IN UNIVERSITY OF IBADAN, OYO STATE, NIGERIA

The study assessed the effect of coaching and monitoring on employees’ job performance among academic staff in University of Ibadan, Oyo State, Nigeria. Multistage sampling technique was used in obtaining data from ninety academic employees of Faculty of Agriculture University of Ibadan for the study. The data was analysed using Chi square and Pearson Product Moment Correlation. The mean age of the respondents was 51years while 66.3% were between ages 51-70 years, 52.3% were female and 50.2% were married. The average year of experience of the respondents was 18.1 years while majority (79.1%) of the respondents has obtained their PhD degree. On challenges facing coaching and mentoring, respondents identified mentee unwillingness to be mentored (x̅=2.84), and non conducive environment for coaching and mentoring (x̅=2.67) as the major constraints, while the least constraint was time and workload pressure (x̅=2.03). Many (62.1%) of the respondents have high job performance level. There was significant relationship between respondents’ marital status and job performance (P≤0.05). The study concluded that coaching and mentoring influence employees’ job performance positively among academic staff in academic institutions and recommended that provision of good work-environment for coaching and mentoring of academic staff should be provided by university management.
Learning to Interpret Natural Language Navigation Instructions from Observations

The ability to understand natural-language instructions is critical to building intelligent agents that interact with humans. We present a system that learns to transform natural-language navigation instructions into executable formal plans. Given no prior linguistic knowledge, the system learns by simply observing how humans follow navigation instructions. The system is evaluated in three complex virtual indoor environments with numerous objects and landmarks. A previously collected realistic corpus of complex English navigation instructions for these environments is used for training and testing data. By using a learned lexicon to refine inferred plans and a supervised learner to induce a semantic parser, the system is able to automatically learnto correctly interpret a reasonable fraction of the complex instructions in this corpus.
The comparison of fatty acids, fat-soluble vitamins and cholesterol in the muscle of wild caught, cage and pond reared rainbow trout ( Oncorhynchus mykiss W., 1792) meat yield: proximate composition and fatty acid profile of cultured

Introduction Fish meat is a good source of fats, proteins, vitamins and minerals. Therefore, it is an important component of a balanced diet for humans (Stancheva et al., 2010). Fish store the lipids in various organs, particularly in muscles and liver. Somatic muscle is the dominant tissue in fish and constitutes more than 60% of the live fish weight. Fatty fish such as salmonid mainly deposit lipids in their muscle (Love, 1980). Lipids are important components of fish diets due to their role in providing energy and essential fatty acids, as they carry of fat-soluble vitamins, and resource of polar lipid including sterols, which are important structural compounds of cell membranes (Görgün and Akpınar, 2007). Recently, there has been heightened interest in the lipid and fatty acid (FA) composition of fish. Fish naturally contain high levels of polyunsaturated fatty acids (PUFA) that are recognized as essential biochemical components of the human diet because of their beneficial effects for human health (Sushchik et al., 2007). Recent studies have shown that n-3 PUFAs play a vital role in the prevention and treatment of cardiovascular disease, inflammation, aggression, depression, hypertension, autoimmune disorders and cancer. Along with omega-3 and omega-6 fatty acids play a crucial role in brain function as well as in normal growth and development. Polyunsaturated fatty acids (PUFAs), stimulate skin and hair growth, regulate metabolism, and maintain bone and reproductive health. (Stancheva et al., 2010). The lipid content and fatty acid profile of fish vary between and within
RE-PLANNING THE MASTER PLAN: THE CORRUPTION AND POLITICS OF LAND ALLOCATION, HOUSING AND HOMELESSNESS AMONG THE URBAN POOR IN ABUJA NIGERIA

Rapid urbanization has continued to occur in many post independent capital cities in Africa with serious competition for land and housing among urban dwellers thereby posing challenge to governments. Consequently, this paper examines how corruption and politics have constituted hindrances to efficient land administration and housing provision and promoting homelessness among the urban poor in Abuja as against the provisions of the Abuja Master Plan (AMP). The locale of this study is the Federal Capital Territory (FCT), Abuja. The study adopted a survey method of data elicitation with questionnaire and interview schedule as instruments. A sample size of 271 was generated via Raosoft sample size calculator to represent the city’s estimated population i.e. 3,095,118. The generated data were presented and analyzed using Microsoft Word graphs and simple statistical percentage. The findings reveal that the government is yet to meet up with the development phases of the AMP as demands for access to land and housing by the ever increasing population of the city, particularly the downtrodden, are yet to be met. This was due largely to corruption and politicization of land administration as well as controversial provisions of the Land Use Act (LAU) and AMP. Consequently, this paper recommends among other things that the Federal Capital Territory Administration (FCTA) should collaborate with ICPC and EFCC in establishing Anti-Corruption Units at various strategic directorates of the FCTA and her agencies to curb sharp practices in land administration. Also, FCTA should synergize with the Federal Ministry of Power Works and Housing (FMPWH), Federal Housing Authority (FHA), Federal Mortgage Bank (FMB), and UN-Habitat to declare state of emergency on housing and construct affordable, accessible and quality mass housing across the FCT so as to address the lack of access to land and homelessness among the urban poor in Abuja.
Genetic polymorphisms of fecundity genes in Watish Sudanese desert sheep

Background and Aim: The Watish sheep is a strain of desert sheep of smaller size compared to other desert sheep ecotypes, and there is anecdotal evidence that it is endowed with high litter size. The present study was designed for screening for polymorphisms in the known fecundity genes (bone morphogenetic protein receptor type 1B A<G in exon 6, bone morphogenetic protein 15 (BMP15) (FecXB, FecXG, FecXH, and FecXI) in exon2, growth differentiation factor 9 (GDF9) – G1 in exon1 and G8 in exon2 and PRLG<A in intron2) and their association with litter size in Watish. Materials and Methods: The study involved 156 Watish ewes of 2-6 years of age, along with data on litter size in the first, second, and third parity from Sinnar state and contiguous Blue Nile State. Genomic DNA was isolated and genotyped using polymerase chain reaction-restriction fragment length polymorphism. Allele and genotype frequencies were calculated by direct counting. Chi-square test for goodness of fit was performed for agreement with Hardy-Weinberg expectations and association testing. Results: The results demonstrated that all individuals were non-carriers for the target mutations of FecB, BMP15 (FecXB, FecXH, and FecXI), and GDF9-G8. With regard to the GDF9-G1 gene, the genotypic frequencies were 0.07% (G+) and 0.93% (++), in FecXG gene they were 0.993% (++) and 0.006% (B+), in PRL gene 0.516(++), 0.347(B+), and 0.137(BB). The Chi-square test showed a non-significant association between ewe’s type of birth and the detected mutations genotypes. Conclusion: These results preliminarily indicated that GDF9-G1, BMP15 (FecXG), and PRL genes might have had some contribution for improving litter size in Watish Sudanese sheep. However, further studies using larger samples are needed to detect the effects of those mutations on Watish sheep litter size.
TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 ModDrop

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.
Man vs. Machine: Master Debating with Argumentative Dialogue

Natural language generation involves the machine creation of meaningful text. I address the natural language generation task in an argumentative dialogue setting. Success in this task is important because results can be applied to educational environments as an instance of AI that provokes critical thinking instead of con-ﬁrming existing preferences. My goal is to create a dialogue system capable of political argumentative conversation with a human user in a sustained message exchange. I present a Transformer-based sequence-to-sequence architecture that generates BERT embeddings from input messages, encodes these embeddings with a Transformer, and then decodes meaningful machine responses through a combination of local and global attention. The Political Language Argumentation Transformer (PLATo) is a novel architecture that achieves lower perplexity and higher accuracy outputs than existing benchmark agents. PLATo surpasses pure RNN and Transformer architectures and provides a generalizable model for future development of argumentative dialogue systems across datasets and use cases. My mentor is Arnaud. No external collaborators, mentors, or project sharing amongst other courses.
Electroanalytical sensing of Asulam based on nanocomposite modified glassy carbon electrode

In this study a facile approach to employ Copper nanoparticle (CuNPs) and multi-walled carbon nanotubes (MWCNT) as the nanomaterial for selective detection of asulam have been investigated. This work reports the electrocatalytic oxidation of asulam on glassy carbon electrodes (GCE) modified with multi-walled carbon nanotubes (MWCNT), ionic liquids (IL), chitosan (Chit) and copper nanoparticles (CuNPs). Using the proposed nanocomposite provides a specific platform with increased surface. The surface morphology of this modified electrode was characterized by field-emission scanning electron microscopy (FE-SEM) and energy dispersive X-ray spectrometer (EDX) techniques. The electrochemical behaviors of the fabricated sensor were investigated by cyclic voltammetry (CV) and chronoamperometry modes. Under optimal conditions, the amperometric study exhibits two linear ranges of 1–11 and 11–200 μmol L-1 with a detection limit (LOD) of 0.33 nmol L-1 (at an S/N of 3) and sensitivity of 1.9 nA μmol L-1 for Asulam determination. This novel sensor was used to analyze the real sample. The sensor provides a convenient, low-cost and simple method for Asulam detection and proposes new horizons for quantitative detection of Asulam.
Premenstrual syndrome among female medical students ofUniverisiti Malaysia Sabah

The premenstrual syndrome (PMS) is particularly common in the younger age groups, posing a significant public health problem among young girls. It was noted that, among the female students, PMS was a frequent reason for seeking care at the in-house health clinics of Universiti Malaysia Sabah (UMS). The Faculty of Medicine and Health Sciences (FPSK) took an initiative to explore this problem as the health and quality of life of students are the major concerns of UMS. A cross-sectional study was conducted with 211 female medical students of FPSK to determine the prevalence of premenstrual syndrome (PMS) and to investigate its associated factors. American College of Obstetrics and Gynaecology (ACOG) PMS-criteria-based questionnaires were used. The mean age of students under study was 20.94±0.92 and 51.65% responded to have experienced PMS. Among the somatic symptoms, 70.6% of the studied females complained of breast tenderness, 62. 4% of abdominal bloating, 39.4% of headache and 2.8% of swelling of extremities. Common behavioural symptoms were irritability (80.7%), angry outbursts (66.1%), depression (57.8%), anxiety (53.2%), social withdrawal (31.2%), and confusion (24.8%). Among the reported symptoms; headache, confusion, irritability, social withdrawal, anxiety and swelling of extremities were significantly associated with PMS. However, the relationships between PMS and disturbances in students’ daily physical and mental activities were not statistically significant. As the prevalence estimated by this study was quite high, in-depth studies on PMS of female UMS students need to be conducted.
A COMPARATIVE STUDY ON VERMICOMPOSTING OF EPICARP OF FRUITS (POMEGRANATE AND SATHUKUDI) USING EARTHWORM EISENIA FOETIDA

Article History: Received 2, February, 2015 Received in revised form 10, February, 2015 Accepted 4, March, 2015 Published online 28, March, 2015 The trend of using inorganic fertilizers is on a boom amongst agricultural society, farmers are magnetized towards the short term advantages privileged by inorganic fertilizers, but they are unable to understand the ill effects these fertilizers on human health and soil fertility. Epicarp of pomegranate and citrus are throughout from fruit stall and market as waste. They are potential organic waste creating solid waste problem. They were compost using earthworm. Chemical composition of compost exhibited decrease of carbon content C: N ratio and increased content of nitrogen content, phosphorus content, pottossium content and calcium content. This paper aimed to find out the role of earthworm species E. foetida in the recycling of the epicarp of two wastes varieties like Pomegranate (Punicagrantum balasta) and Sathukudi (Citrus sinensis). This paper also studies the nutritional status of different vermicomposts processed by the earthworm E. foetida were analyzed and compared.
Leclercia adecarboxylata, a rare cause of soft tissue infections in immunocompromised patients, case report and review of the literatura

Leclercia adecarboxylata is a member of the Enterobacteriaceae family that has been isolated from several environmental and animal specimens, however it rarely causes diseases in human beings. It has natural resistance to several antibiotics, and has shown the ability to harbor and produce enzymes capable of hydrolyzing most of the antibiotics used in daily clinical practice, making its treatment a challenge when a strain with such characteristics causes disease. Here we report the first known case of infection by Leclercia adecarboxylata after a trauma with plant material, in a 69-year-old male patient, with poorly controlled Diabetes Mellitus type 2.