Miracle-dz/UniFAR-FTUs huggingface dataset:
A method for static CCTV image analysis to improve biometric security systems

In this paper, a concept of an access-control system based on mantrap and an analysis of static images from surveillance cameras is introduced. The results of this concept have been obtained by the analysis of images from surveillance cameras or digital cameras. These devises are not elements of mantraps. The motivation of this study is the importance of the ability to detect such events in a protected area. The method can be used as a supplement to a system of biometric identification of persons. The paper presents the structure of a threat detection system and subsequent operations on recorded images made by an application specially developed for this purpose. The paper discusses methods of collecting and recording data as well as methods of contextual and non-contextual processing of images that have a decisive impact on the final form of the material used for comparison with a database.
COVID-19 and Suicide Tendency: Prediction and Risk Factor Analysis Using Machine Learning and Explainable AI

INTRODUCTION: Pandemics and epidemics have frequently led to a significant increase in the suicide rate in affected regions. However, these unnecessary deaths can be prevented by identifying the risk factors and intervening earlier with those at risk. Numerous empirical studies have exhaustively documented multiple suicide risk factors. In addition, many evidence-based approaches have employed machine learning models to diagnose vulnerable groups, a task that would otherwise be challenging if only human cognition were employed. To date, to the best of our knowledge, no research has been conducted on COVID-19-related suicide prediction.OBJECTIVES: This research, aims to develop a machine-learning model capable of identifying individuals who are contemplating suicide due to COVID-19-related complexities and assessing the potential risk factors.METHODS: We trained a gradient-boosting model based on tree-based learners on 10067 data consisting of 76 features, which were primarily responses to socio-demographic, behavioural, and psychological questions about COVID-19 and suicidal behaviours.RESULTS: The final model predicted individuals at risk with an auROC score of 0.77 and a 95% confidence interval of 0.77 to 0.88. The optimal cutoff produced a sensitivity of 31.37 percent and a specificity of 82.35 percent in predicting suicidal tendencies. However, the auPRC was only 0.26, with a 95 percent confidence interval of 0.13 to 0.38, as the class distribution was extremely unbalanced. Consequently, the scores for precision and recall were 0.35 and 0.31, respectively.CONCLUSION: We investigated the risk factors, the majority of which were associated with sleeping difficulties, fear of COVID-19, social interactions, and other socio-demographic factors. The identified risk factors can be considered when formulating a policy to prevent COVID-19-related suicides, which can impose a long-term economic and health burden on society.
Photoluminescence Lifetime Imaging Using LED Arrays as Excitation Source

In recent years photoluminescence (PL) has been introducd as a fast and versatile tool for characterising silicon wafers and solar cells. For PL imaging a laser as excitation source is usually used. In this contribution we replaced the laser with light emitting diode (LED) arrays, which have the advantage of avoiding the need to widen and homogenise the laser beam. By means of LED arrays, PL images can be obtained with the same quality as the laser beam excited photoluminescence. There is experimental evidence that LED excited PL is capable of measuring the lifetime distribution without artifacts that are possibly caused by internal electrical fields and that are encountered in other methods like microwave detected photoconductance decay (MWPCD). A possibly new effect is encountered in a solar cell under open circuit condition where finger-interrupted regions show unexpected high PL signal in comparison to electroluminescence.
Consensus based distributed motion planning on a sphere

In this paper, consensus theory and semidefinite programming techniques are applied for planning of multiple collision free trajectories, for a team of communicating vehicles whose motions are constrained to evolve on the surface of a sphere. Such algorithms have applications in planetary-scale motion control for mobile sensing networks in air and space. Based on the communication graph for each vehicle, each vehicle synthesizes a time-varying Laplacian-like matrix Li. The set of Laplacian-like matrices are used individually in a distributed manner to drive given initial positions of the vehicles to consensus positions on the sphere. Collision avoidance and formation configurations are realized via the concept of semidefinite programming. For each vehicle, the problem is coded as a set of linear matrix inequalities (LMI), augmented with a number of constraints, and solved by semidefinite programming (SDP). We also provide Lyapunov-based stability analysis, together with simulation results to demonstrate the effectiveness of the approach.
Contemporary challenges of restoring the threatened important bird area of Lake Haramaya, Eastern Ethiopia

The study was carried out at the margin of the threatened lake Haramaya neighboring inhabitants from March to June 2015. The objective of the study was to assess the current challenges of restoring the threatened important bird area of Lake Haramaya. Community based cross sectional study design was employed. The subject of the study was confined to households proximal to the lake in settlement, agricultural and other activities. The total population in the town was 6556 households. Considering only 50 m distance of the settlements to the proximity of the lake shore 10% (60 households) were taken. The subjects of the study was proportionally allotted to each three locality Tuji-Gebissa, n1= 16, Ifa-Bate, n2= 13 and 02 n3= 31) found around the lake based on thier proximity to the shore lines. The individuals from each site were purposely selected using judgmental sampling. Questionnaire that contains both open and closed ended questions regarding the current challenges of restoring the threatened important bird area of Lake Haramaya was used to collect data. Besides, observation checklist was also used to observe the overall threat factors operating in the site. The lake is highly threatened important bird area as a result of various threat factors. Majority (66.6 %) of the local villagers reported that, the land use of the lake is more preferred for agricultural expansion. On the other hand, 33.3% of the villagers reported that, the shore line is preferred for better settlement. Majority (100%) of the local villagers were releasing both solid and liquid types of waste towards the shoreline of the lake. 50% of the local villagers without toilet use agricultural fields proximal to the lake whereas (50%) use open shoreline of the lake. The lake is an important bird area listed by Ethiopian Wildlife conservation authority but human settlement around the lake shore is a threat factor threatening the overall ecology within the watershed. Generally, the lake needs a great concern of conservation by all stakeholders and inhabitants to manage human settlement, waste disposal, agricultural practice, pumped irrigation, uncontrolled fishing and cutting of
Particle Filtering Applied to Musical Tempo Tracking

This paper explores the use of particle filters for beat tracking in musical audio examples. The aim is to estimate the time-varying tempo process and to find the time locations of beats, as defined by human perception. Two alternative algorithms are presented, one which performs Rao-Blackwellisation to produce an almost deterministic formulation while the second is a formulation which models tempo as a Brownian motion process. The algorithms have been tested on a large and varied database of examples and results are comparable with the current state of the art. The deterministic algorithm gives the better performance of the two algorithms.
Challenge and hindrance stressors: Relationships with employees' work engagement

This paper investigated the relationships between challenge-hindrance stressor and three sub-factors of work engagement. Based on two-dimensional model of stressor and Appraisal theory of stressor, We hypothesized that challenge and hindrance stressors have divergent relationships with three sub-factors of work engagement. Using a sample of 239 data collected in China, we found that challenge stressor was positively related to vigor, dedication and absorption; in contrast, hindrance stressor was negatively related to them. Our findings provide additional supports for two-dimensional model of stressor and previous results which indicated that not all stressor had negative effects on individuals or organizations. The findings of current study suggests that managers should remove hindrance stressors in order to increase employees' work engagement, at the same time, they should provide challenge-relate stressors in workplace.
Scalable and Efficient Speech Enhancement Using Modified Cold Diffusion: A Residual Learning Approach

We introduce flexibility to the supervised learning-based speech enhancement framework to achieve scalable and efficient speech enhancement (SESE). To this end, SESE conducts a series of segmented speech enhancement inference routines, each of which incrementally improves the result of its preceding inference. The formulation is conceptually similar to cold diffusion, while we modify the sampling process so each step benefits from an easier milestone task rather than aggressively targeting the clean speech. In addition, the incremental enhancement steps are learned to recover the residual between the adjacent milestones, thus improving the overall enhancement performance. We show that the proposed method improves the baseline supervised model’s performance, while it necessitates fewer diffusion steps to achieve the comparable performance with the more complex cold diffusion-based counterpart. Furthermore, SESE’s scalability can be useful in applications where moderately suppressed non-speech interference is preferred to aggressive enhancement results, e.g., boosting dialog in movie soundtracks, speech enhancement on hearing aids, etc.
Signal Processing With Compressive Measurements

The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware systems that directly implement random measurement protocols. However, despite the intense focus of the community on signal recovery, many (if not most) signal processing problems do not require full signal recovery. In this paper, we take some first steps in the direction of solving inference problems-such as detection, classification, or estimation-and filtering problems using only compressive measurements and without ever reconstructing the signals involved. We provide theoretical bounds along with experimental results.
Modularizing error recovery

Error recovery in compilers often involves significant amounts of cognitive effort to identify the code and execution points in the compiler that are related to identifying and handling input-program errors. This is because current implementations fail to explicitly identify error-related control dependencies, and to separately characterize the actions to take when programming errors are detected. As a result, compiler writers need to navigate and understand much of the compiler source in order to replace or extend error recovery actions. In the context of AspectJ compiler (ajc), this paper encapsulates error concerns as aspects yielding improved modularity through: pointcuts that explicitly declare the loci of error detection, and advice that expose the extension points for error handling.
Genome-Wide Association Studies on the Kernel Row Number in a Multi-Parent Maize Population

Kernel row number (KRN) is a crucial trait in maize that directly influences yield; hence, understanding the mechanisms underlying KRN is vital for the development of high-yielding inbred lines and hybrids. We crossed four excellent panicle inbred lines (CML312, CML444, YML46, and YML32) with Ye107, and after eight generations of selfing, a multi-parent population was developed comprising four subpopulations, each consisting of 200 lines. KRN was accessed in five environments in Yunnan province over three years (2019, 2021, and 2022). The objectives of this study were to (1) identify quantitative trait loci and single nucleotide polymorphisms associated with KRN through linkage and genome-wide association analyses using high-quality genotypic data, (2) identify candidate genes regulating KRN by identifying co-localized QTLs and SNPs, and (3) explore the pathways involved in KRN formation and identify key candidate genes through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Our study successfully identified 277 significant Quantitative trait locus (QTLs) and 53 significant Single Nucleotide Polymorphism (SNPs) related to KRN. Based on gene expression, GO, and KEGG analyses, SNP-177304649, SNP-150393177, SNP-135283055, SNP-138554600, and SNP-120370778, which were highly likely to be associated with KRN, were identified. Seven novel candidate genes at this locus (Zm00001d022420, Zm00001d022421, Zm00001d016202, Zm00001d050984, Zm00001d050985, Zm00001d016000, and Zm00014a012929) are associated with KRN. Among these, Zm00014a012929 was identified using the reference genome Mo17. The remaining six genes were identified using the reference genome B73. To our knowledge, this is the first report on the association of these genes with KRN in maize. These findings provide a theoretical foundation and valuable insights into the genetic mechanisms underlying maize KRN and the development of high-yielding hybrids through heterosis.
Design of High-Gain Antenna Arrays for Terahertz Applications

A terahertz band (0.1–10 THz) has the characteristics of rich spectrum resources, high transmission speed, strong penetration, and clear directionality. However, the terahertz signal will suffer serious attenuation and absorption during transmission. Therefore, a terahertz antenna with high gain, high efficiency, and wide bandwidth is an indispensable key component of terahertz wireless systems and has become a research hotspot in the field of antennas. In this paper, a high-gain broadband antenna is presented for terahertz applications. The antenna is a three-layer structure, fed by a grounded coplanar waveguide (GCPW), using polytetrafluoroethylene (PTFE) material as the dielectric substrate, and the metal through-hole of the dielectric substrate forms a substrate-integrated waveguide (SIW) structure. The metal fence structure is introduced to reduce the coupling effect between the radiation patches and increase the radiation bandwidth and gain. The center frequency is 0.6366 THz, the operating bandwidth is 0.61–0.68 THz, the minimum value of the voltage standing wave ratio (VSWR) is 1.00158, and the peak gain is 13.14 dBi. In addition, the performance of the designed antenna with a different isolation structure, the length of the connection line, the height of the substrate, the radius of the through-hole, and the thickness of the patch is also studied.
Analyzing single-server network inhibition

Network inhibition is a denial-of-service attack where the adversary attempts to disconnect network elements by disabling a limited number of communication links or nodes. We analyze a common variation of network inhibition where the links have infinite capacity and the goal of the attacker is to deny connections from a single server to as many clients as possible. The problem is defined formally and shown to be NP complete. Nevertheless, we develop a practical technique for network-inhibition analysis based on logic programming with stable-model semantics. The analysis scales well up to moderate-size networks. The results are a step towards quantitative analysis of denial of service and they can be applied to the design of robust network topologies.
Implementation of a National Reference Laboratory for Buruli Ulcer Disease in Togo

Background In a previous study PCR analysis of clinical samples from suspected cases of Buruli ulcer disease (BUD) from Togo and external quality assurance (EQA) for local microscopy were conducted at an external reference laboratory in Germany. The relatively poor performance of local microscopy as well as effort and time associated with shipment of PCR samples necessitated the implementation of stringent EQA measures and availability of local laboratory capacity. This study describes the approach to implementation of a national BUD reference laboratory in Togo. Methodology Large scale outreach activities accompanied by regular training programs for health care professionals were conducted in the regions “Maritime” and “Central,” standard operating procedures defined all processes in participating laboratories (regional, national and external reference laboratories) as well as the interaction between laboratories and partners in the field. Microscopy was conducted at regional level and slides were subjected to EQA at national and external reference laboratories. For PCR analysis, sample pairs were collected and subjected to a dry-reagent-based IS2404-PCR (DRB-PCR) at national level and standard IS2404 PCR followed by IS2404 qPCR analysis of negative samples at the external reference laboratory. Principal Findings The inter-laboratory concordance rates for microscopy ranged from 89% to 94%; overall, microscopy confirmed 50% of all suspected BUD cases. The inter-laboratory concordance rate for PCR was 96% with an overall PCR case confirmation rate of 78%. Compared to a previous study, the rate of BUD patients with non-ulcerative lesions increased from 37% to 50%, the mean duration of disease before clinical diagnosis decreased significantly from 182.6 to 82.1 days among patients with ulcerative lesions, and the percentage of category III lesions decreased from 30.3% to 19.2%. Conclusions High inter-laboratory concordance rates as well as case confirmation rates of 50% (microscopy), 71% (PCR at national level), and 78% (including qPCR confirmation at external reference laboratory) suggest high standards of BUD diagnostics. The increase of non-ulcerative lesions, as well as the decrease in diagnostic delay and category III lesions, prove the effect of comprehensive EQA and training measures involving also procedures outside the laboratory.
Water Wastage Detection in Smart Homes Through IoT and Machine Learning

Promoting sustainable water usage is a critical imperative across all sectors of society. Households are no exception since a significant portion of water is wasted daily due to inefficient appliances or improper habits. Thus, there is a need for innovative solutions that not only improve water utilization but also raise residents' awareness about this issue. This paper presents a promising solution leveraging the Internet of Things (IoT) and Machine Learning (ML) techniques to detect water wastage stemming from sink usage automatically. We have designed and developed a low-cost prototype equipped with an array of sensors, including a microphone, an ultrasonic sensor, and a PIR, to monitor sink usage. A deep learning model based on Gated Recurrent Units (GRU) has been trained to classify the wastage events. To validate our concept, we have gathered a small dataset relative to nine common daily water usage activities through the IoT prototype. Our preliminary findings demonstrate the feasibility of our solution, with an average accuracy exceeding 90% in detecting wastage events.
Natural Heat Stress Affects Physiological, Hematological and Hormonal Parameters in North African Ram Breeds

To study the ability of sheep to maintain their homeothermy in a North African natural heat stress condition, three Tunisian sheep breeds the Barbarin (BTR), Queue Fine de l’Ouest (QFO) and Noire de Thibar (NTB) were used. A total of twelve (4 per breed) rams were used in this study, for the period of 3 months from March to June. During this period, the ambient temperature varied from 12.5 °C to 32 °C, relative humidity from 49% to 94% and the temperature humidity index (THI) from 50 units to 85 units. The THI values exceeded the borderline of thermal stress, resulting a higher rectal and skin temperature, heart and respiratory rates (P<0.05). The rectal temperature (RT) varied from 39.41 °C to 39.38 °C for BTR, from 39.20 °C to 39.52 °C for QFO and from 39.51 °C to 39.72 °C for NTB. A significant (P<0.05) decrease in thyroxine (T4) from 16.58 pmol/l to 7.52 pmol/l for BTR rams, from 10.99 pmol/l to 6.34 pmol/l for QFO rams and from 12.43 pmol/l to 6.91 pmol/l for NTB rams. TSH hormone decrease significantly from a comfort situation to a stressful situation for BTR (0.124 mUI/l to 0.039 mUI/l), QFO (0.132 mUI/l to 0.065 mUI/l) and NTB (0.113 mUI/l to 0.057 mUI/l). No change in tri-iododthyronine (T 3 ) and a no significantly decrease for cortisol hormone, from 6.91 ng/l to 2.83 ng/l for BTR, from 9.69 ng/l to 4.20 ng/l for QFO and from 7.65 ng/l to 2.42 ng/l for NTB. These responses allowed animals to efficiently dissipate heat, maintain a physiological rectal temperature, and avoid thermal stress. Blood samples were taken and analyzed during two different periods: no stress and heat stress to study the relationship between homeothermy and hematological (RBC, WBC, Hb, Ht, Pl, PCV, MCH, and MCHC), biochemical (glucose, cholesterol, total protein) and hormonal (T 4 , T 3 , Cortisol and TSH) parameters responses.
Mining semantic data for solving first-rater and cold-start problems in recommender systems

Recommender systems are becoming very popular in recent years, mainly in the e-commerce sites, although they are increasing in importance in other areas such as e-learning, tourism, news pages, etc. These systems are endowed with intelligent mechanisms to personalize recommendations about products or services. However, they present some serious drawbacks that impact in user satisfaction. First-rater and cold-start problems are two important drawbacks that take place respectively when new products or new users are introduced in the system. The lack of rating about these products or from these users prevents from making recommendations. Nowadays, traditional collaborative filtering methods have being replaced by web mining techniques in order to deal with scalability and performance problems, but first-rater and cold-start ones require a different strategy. In this work, we propose a methodology that combines data mining techniques with semantic data in order to overcome these two important shortcomings.
A Review on Traversability Risk Assessments for Autonomous Ground Vehicles: Methods and Metrics

Evaluating the risk associated with operations is an essential element of safe planning and an essential prerequisite in mobile robotics. This issue is very broad, with numerous definitions emerging in the recent literature adapting different application scenarios and leading to different algorithmic approaches. In this review, we will investigate how the state-of-the-art approaches define the traversability risk, particularly for mobile robots, whereby we classify existing risk-aware navigation algorithms according to their characterization of risk. Subsequently, we will overview the formulations of risk assessment along a path using traversability grid maps since it is essential for a mobile robot to evaluate its path to predict potential hazards. Finally, we will discuss the consistency of commonly used risk metrics in robotics. The aim of the review is to offer a comprehensive overview to newcomers in the field, to provide a structured reference for practitioners, and to also inspire future developments.