Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning

Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on these technologies. Ultrasonic systems have already been demonstrating their effectiveness and to possess the desired positioning accuracy and refresh rates. In this work, it is shown that a typical signal used in ultrasonic positioning systems to estimate the range between the target and reference points—namely, the linear chirp—due to the effects of acoustic diffraction, in some cases, undergoes a shape aberration, depending on the shape and size of the transducer and on the angle under which the transducer is seen by the receiver. In the presence of such signal shape aberrations, even one of the most robust ranging techniques, which is based on cross-correlation, provides results affected by a much greater error than expected. Numerical simulations are carried out for a typical ultrasonic chirp, ultrasonic emitter, and range technique based on cross-correlation and for a typical office room, obtained using the academic acoustic simulation software Field II. Spatial distributions of the ranging error are provided, clearly showing the favorable low error regions. The work demonstrates that particular attention must be paid to the design of the acoustic section of the ultrasonic positioning systems, considering both the shape and size of the ultrasonic emitters and the shape of the acoustic signal used.
Distributed Fronthaul Compression Design for Rateless Coded Multi-User Uplink Transmission in C-RAN

In this paper, we consider the multi-user uplink transmission in cloud radio access network (C-RAN), which consists of two users, two radio remote heads (RRHs), and the baseband processing unit (BBU) pool. The users adopt Raptor code and send the modulated signals to both RRHs continuously until receiving the acknowledge character (ACK) feedback from the BBU. A novel fronthaul compression scheme for RRH is proposed. Explicitly, both RRHs quantize the received signal to meet the fronthaul capacity constraint. One of the RRHs further compresses the quantized signal with low density parity check (LDPC) code. Then not only the resulted syndrome bits, additional quantization bits are also sent to the BBU according to the remaining fronthaul capacity, which can reduce the quantization noise at the BBU. We optimize the LDPC code based on extrinsic information transfer (EXIT) analysis. Simulation results show that optimized LDPC code has good compression performance compared with regular LDPC code, and the proposed fronthaul compression scheme outperforms the conventional scheme without DSC.
Simultaneous analysis of potassium and ammonium ions in the vitreous humour by capillary electrophoresis and their integrated use to infer the post mortem interval (PMI)

Post-mortem changes of ions in the body fluids have been proposed as an objective tool for inferring the time of death. In particular, the post-mortem increase of potassium concentrations in the vitreous humour has gained great attention in the literature. On the other hand, ammonium, another ion released in post-mortem processes, has received much less attention, potentially due to unresolved analytical issues using current clinical chemistry methods. This paper presents an application of a new analytical approach based on capillary electrophoresis providing the simultaneous analysis of potassium and ammonium ions in the vitreous humour. In addition, to assess the consistency of the post-mortem increase of ammonium concentrations in the vitreous humour, the determination of this ion in the vitreous humour of the two eyes of the same body at the same post-mortem interval has been verified. Vitreous humour was collected from 33 medico-legal cases where the time of death was known exactly. Prior to analysis, all samples were diluted 1:20 with a 40 μg/mL solution of BaCl2 (internal standard). In the study of the variability of the ammonium concentration between the two eyes, no statistically significant differences were found, supporting the hypothesis of an even post-mortem increase of the ion concentrations in this particular biological fluid. Significant correlations of potassium and ammonium ions with the post-mortem interval were found, with r2 of 0.75 and 0.70, respectively.
Automatically Generating Malware Summary Using Semantic Behavior Graphs (SBGs)

In malware behavior analysis, there are limitations in the analysis method of control flow and data flow. Researchers analyzed data flow by dynamic taint analysis tools, however, it cost a lot. In this paper, we proposed a method of generating malware summary based on semantic behavior graphs (SBGs, Semantic Behavior Graphs) to address this issue. In this paper, we considered various situation where behaviors be capable of being associated, thus an algorithm of generating semantic behavior graphs was given firstly. Semantic behavior graphs are composed of behavior nodes and associated data edges. Then, we extracted behaviors and logical relationships between behaviors from semantic behavior graphs, and finally generated a summary of malware behaviors with true intension. Experimental results showed that our approach can effectively identify and describe malicious behaviors and generate accurate behavior summary.
Time-Encoding Analog-to-Digital Converters: Bridging the Analog Gap to Advanced Digital CMOS-Part 1: Basic Principles

The scaling of CMOS technology deep into the nanometer range has created challenges for the design of highperformance analog ICs. The shrinking supply voltage and presence of mismatch and noise restrain the dynamic range, causing analog circuits to be large in area and have a high power consumption in spite of the process scaling. Analog circuits based on time encoding [1], [2] and hybrid analog/digital signal processing [3] have been developed to overcome these issues. Realizing analog circuit functionality with highly digital circuits results in more scalable design solutions that can achieve excellent performance. This article reviews the basic principles of time encoding applied, in particular, to analog-to-digital converters (ADCs) based on voltage-controlled oscillators (VCOs), one of the most successful time-encoding techniques to date.
A Hybrid Deep Learning Model for Malicious Behavior Detection

In recent years, malicious programs seriously threaten the security of information system. Because of its particularity, complexity and vulnerability, power information system is difficult to detect and kill by traditional anti-virus software. To solve the above problems, this paper proposes a malicious behavior detection method based on deep learning, which can identify malicious programs and attack types according to the activities of malicious programs and malicious software behaviors. In this paper, a hybrid deep learning structure based on convolutional neural network (CNN) and long-and-short term memory (LSTM) network is proposed. The call sequence of API is combined with other statistical features. The vector information obtained is input into LSTM unit through convolution, and the classification results are obtained through the above model. Compared with the existing methods, this method significantly improves the preparation rate and execution efficiency.
A Proposed Face Recognition based on Hybrid Algorithm for Features Extraction

This paper offers a suggested method of facial recognition to identify any person. Initially, captured images in the database to apply matching processing, which ease the recognition operation. The most difficult problems in the recognition algorithms are, the difference in face appearance, the lighting effect, and the composite background of the image. This paper presents the fundamental step in the extraction of the properties that depend on the Principal Component Analysis (PCA) also known as Karhunen–Loe (KL) to convert Eigen values to reduce the number of inputs on the network. To identify a person's image, apply Artificial Neural Networks (ANN) using Elman Neural Network (ENN), which is the most necessary and effective applications suitable for biometric systems and image processing. Practically taking photos (10) each person has eight poses (4 to 4 training to test). The experimental results of the ENN classification are calculated as a true acceptable rate (GAR) of 97% while the false acceptable rate FAR is equal to 3%. In addition, the artificial neural network gave a good performance to anyone.
The Use of Neural Networks to Improve the Recognition Accuracy of Explosive and Unvoiced Phonemes in Uzbek Language

Currently, speech recognition systems are becoming more widespread, especially in those applications where speech dialogue is the most convenient mean of information control and exchange with technical facilities. Obtaining an effective voice control system is currently an important task, requiring the development of methods to obtain high recognition accuracy of voice commands. Under these conditions, along with reducing the noise effect on the quality of recognition, the task is to increase the accuracy in voice control system operation, to increase the likelihood of correct command recognition under stationary interference. Another requirement in the voice control system is the correct recognition speed, since the system must work in real time. In this paper, we propose an algorithm for voice control by technical facilities, based on Uzbek language. Some sounds in Uzbek language (explosive and unvoiced consonants) differ strongly from the sounds in other languages; when creating control algorithms, the recognition accuracy does not meet the requirements. Therefore, to ensure the necessary processing speed and maintain the required accuracy, it is proposed to introduce an additional normalization with a decrease in feature space. The algorithm is based on the principle of primary separation of speech signal spectral characteristics. Further, the signal spectrum is normalized and its resolution increases due to the use of low-frequency conversion and logarithms. The obtained cepstral coefficients are fed to the input of a previously learned neural network.
Utilization of diabetes management health care services and its association with glycemic control among patients participating in a peer educator-based program in Cambodia

Background Substantial evidence supports the effectiveness of peer educator programs for diabetes management in low- and middle-income countries. However, little is known about peer educators’ impact relative to other treatment components such as medication and physician consultation. In Cambodia, the non-governmental organization MoPoTsyo organizes four services for people with diabetes: self-management training through peer educator visits, lab tests, physician consultations, and low-cost medicines. Our aims were to 1) quantify MoPoTsyo participant utilization of each program service and 2) define the relationship between each program service and glycemic control. Methods We conducted a retrospective cohort study among 4,210 MoPoTsyo participants, using data collected by MoPoTsyo from 2006–2016. Independent variables assessed were medication adherence, number of peer educator visits, number of physician consultations, and number of lab tests. A multiple logistic regression model was used to evaluate the association between these disease management services and glycemic control—fasting plasma glucose ≤130 mg/dl or post-prandial glucose ≤180 mg/dl—based on most recent glucose level. The model was adjusted for baseline demographic and disease characteristics. Findings Participants with 12 or more peer educator visits per year had a 35% higher odds of glycemic control relative to participants with 4 or fewer visits (odds ratio 1.35, 95% CI: 1.08–1.69; p = 0.009), after adjustment for utilization of other treatment components (medication adherence, number of physician visits, number of lab tests), follow-up time, and demographic and disease characteristics. Better adherence to medications and a greater number of lab tests per year were also associated with a higher odds of glycemic control after adjustment. Number of physician consultations was not associated with glycemic control after adjustment. Conclusions This study demonstrates a positive association between peer educator utilization and glycemic control incremental to other elements of diabetes management. These results suggest that peer educators may be a valuable addition to comprehensive diabetes management programs in low- and middle-income countries even when other health care services are accessible. The associations identified in this research warrant further prospective studies to explore the causal impact of peer educators on glycemic control relative to other disease management components.
COPULATION AND OVIPOSITION BEHAVIOUR OF PROTOPIOPHILA LITIGATA (DIPTERA: PIOPHILIDAE)

Abstract Using field and laboratory (stereoscope) observations, we investigated copulation and oviposition behaviours of Protopiophila litigata Bonduriansky, which mates and oviposits exclusively on discarded cervid antlers. Typically, a male leapt onto a female, briefly tapped the sides of her abdomen with his legs, then stimulated her abdominal tip with his tarsi, parameres, and gonopods until she extended her genitalia (~4 min), after which the pair established genital lock (~10 min). Meanwhile, the female emigrated to a part of the antler where density of single males was lower. Following sperm transfer (~87 min) and genital separation (~5 min), the female deposited and ingested two droplets containing spermatozoa (~6 min). Then she oviposited (~29 min) into cracks or pores in the antler’s surface while the male remained in tandem and pushed away single males with his wings. Males often wrestled for possession of females, but take-overs were rare. Whereas most aspects of the copulation-oviposition cycle of P. litigata are characteristic of other necrophagous or scatophagous flies, the manner in which P. litigata females expel and ingest ejaculate materials after copulation has not been reported in any other-species of Diptera. Résumé Des observations sur le terrain et en laboratoire (à la loupe binoculaire) nous ont permis d’étudier les comportements d’accouplement et de ponte chez Protopiophila litigata Bonduriansky, qui s’accouple et pond exclusivement sur les bois rejetés des cervidés. De façon caractéristique, le mâle saute sur la femelle, frappe brièvement les côtés de son abdomen avec ses pattes, stimule ensuite le bout de son abdomen avec ses tarses, ses paramères et ses gonopodes jusqu’à ce qu’elle déploie ses genitalia (~4 min), après quoi leurs genitalia s’unissent (~10 min). Pendant ce temps la femelle émigré vers la partie des bois où la densité des mâles non accouplés est plus faible. Après transfert du sperme (~87 min) et séparation des genitalia (~5 min), la femelle émet et ingère deux gouttelettes contenant des spermatozoïdes (~6 min). Ensuite, elle pond ses oeufs (~29 min) dans des crevasses ou des pores à la surface des bois, alors que le mâle reste sur elle en tandem et repousse les autres mâles avec ses ailes. Les autres mâles se battent parfois pour prendre possession de la femelle, mais réussissent rarement. Bien que la plupart des aspects du cycle accouplement-ponte de P. litigata soient caractéristiques des autres diptères nécrophages ou scatophages, l’expulsion et l’ingestion, après l’accouplement, de substances éjaculées par le mâle n’ont jamais été signalées chez d’autres espèces de diptères. [Traduit par la Rédaction]
Rehydration of Frozen Boll Weevils1 to Provide a Supply of “Fresh” Weevils for Quality Control in Boll Weevil Eradication Programs

Abstract Field capture of boll weevils, Anthonomus grandis Boheman, in pheromone traps adjacent to fields of cotton, Gossypium hirsutum L. , and along fence lines and roads is an important tool used by Boll Weevil Eradication Programs to monitor abundance of the insect pest. For quality control, previously collected and frozen boll weevils are marked with fingerprint powder. Program supervisors randomly spike boll weevil traps with marked boll weevils to monitor recovery by program trappers. One problem, however, is that dead boll weevils placed in traps desiccate rapidly, rendering them fragile and prone to breakage, which makes identification difficult. This study evaluated methods for marking and rehydrating desiccated boll weevils by measuring the length of time boll weevils remain pliable and intact. An alcohol-based hand sanitizer was used for rehydration, and three fluorescing markers were tested to determine the duration of boll weevil pliability and fluorescence. Boll weevils treated with hand sanitizer were pliable significantly longer than were dried weevils, P < 0.0001, and remained intact 2 to 4 days longer All boll weevils retained the three applied fluorescent markers throughout each experiment.
Overfitting reduction of pose estimation for deep learning visual odometry

Error or drift is frequently produced in pose estimation based on geometric "feature detection and tracking" monocular visual odometry(VO) when the speed of camera movement exceeds 1.5m/s. While, in most VO methods based on deep learning, weight factors are in the form of fixed values, which are easy to lead to overfitting. A new measurement system, for monocular visual odometry, named Deep Learning Visual Odometry(DLVO), is proposed based on neural network. In this system, Convolutional Neural Network(CNN) is used to extract feature and perform feature matching. Moreover, Recurrent Neural Network(RNN) is used for sequence modeling to estimate camera's 6-dof poses. Instead of fixed weight values of CNN, Bayesian distribution of weight factors are introduced in order to effectively solve the problem of network over-fitting. The 18,726 frame images in KITTI dataset are used for training network. This system can increase the generalization ability of network model in prediction process. Compared with original Recurrent Convolutional Neural Network(RCNN), our method can reduce the loss of test model by 5.33%. And it's an effective method in improving the robustness of translation and rotation information than traditional VO methods.
Neural Network Approach to Short and Long Term Load Forecasting Using Weather Conditioning

Electricity Load forecasting has consistently been the fundamental part of expanding productivity and income of intensity framework planning and activity such as generation and distribution of the organizations. However, since the connection between load power and factors affecting power control is nonlinear and dynamic uncertainties, it is difficult to distinguish its nonlinearity by utilizing conventional strategies. This paper proposes a multilayered/deep neural network-based predictive models, that forecast yearly and season wise monthly loads. Mean absolute percentage error (MAPE) is used to yield models' accuracy. A Deep-Feed forward Neural Network (Deep-FNN) with a sigmoid transfer function, resilient backpropagation training algorithm and Deep-FNN with Rectified Linear Unit (ReLu) activation function, Levenberg-Marquardt training algorithm for short term and long term, respectively, exhibit accurate and robust forecast compared to other forecasting models. The performance of the models are compared with the state-of-the-art methods for the peak loads depending on the weather conditions.
In vitro activity of Bacillus firmus against the burrowing nematode Radopholus similis, the root-knot nematode Meloidogyne incognita and the stem nematode Ditylenchus dipsaci

Abstract Antagonistic bacteria have been repeatedly shown to be promising microorganisms for the biological control of sedentary and migratory endoparasitic nematodes. Depending on the bacteria involved, the mechanisms of action include: obligate parasitism, reduction in penetration, growth inhibition due to competition for nutrients and antibiosis associated with bioactive metabolites. In the present studies, the mode of action of the antagonist bacteria Bacillus firmus, isolated from a bionematicide, was evaluated. Significant rates of paralysis and mortality were detected after incubation of three nematode species in low concentrations of the pure culture filtrates following removal of the bacterial cells. The same culture filtrates also significantly reduced hatching of Meloidogyne incognita. Pure bacterial cell suspensions added to sand also reduced survival of R. similis in bioassays by 41% over the controls. The mode-of-action responsible for nematode paralysis and mortality was therefore demonstrated to be closely associated with the production of bioactive compounds secondary metabolites by the bacteria.
"Making the Unusual Usual:" Students' Perspectives and Experiences of Learning at Home during the COVID-19 Pandemic.

This child-parent research is a student-led inquiry into three adolescent girls’ experiences of learning during the age of COVID-19 shelter-in-place mandate. In this collaborative autoethnography, a research team of five (three adolescent researchers—two of whom are sisters—and their respective mothers) met via videoconference to engage in five rounds of inductive and deductive data collection and analyses. Findings capture the three adolescents’ experiences of new teaching methods in new learning spaces: (1) the physical space of “Doing School at Home-How it Feels;” (2) the negotiations undertaken by the girls called “Improvisation and a School Mindset;” and (3) the need to respond to one constant: “Everything is Always Changing. ” A fourth theme, called “Being at Home Gives Me. ” was created to capture the human experience of doing school at home—and the new spaces that opened up. Recommendations to help make school work in the home space are provided.
The skin as a peripheral clock model

This work aimed to investigate the skin as a potential model of peripheral clock by characterizing its rhythmic and synchronization properties. Circadian activity was examined in abdominal skin explants and fibroblasts derived from Per1-Luciferase transgenic rats by real-time recording of bioluminescence. First, the skin clock was characterized from early postnatal to old age. Low amplitude oscillations appeared at 1 month only and their robustness increased until 6 months. In 1-2 year-old rats, skin circadian rhythms showed decreasing amplitude and abnormal cycles. Primary fibroblasts derived from the skin at the same ages demonstrated similar pattern of clock activity. Temperature compensation, an intrinsic clock feature, was shown the first time in skin and primary fibroblasts. Secondly, we demonstrated a phase-dependent effect of melatonin to increase the amplitude of oscillations in skin primary fibroblasts, indicating it displays a synchronising role in the circadiansystem. Finally, to facilitate our studies on the multioscillatory skin tissue, we constructed a lentivirus carrying a Bmal1-luciferase reporter, to measure clock genes activities in human skin cells.