Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble Detection Models for LiDAR Point Clouds

We address the problem of the ensemble neural networks in the LiDAR pointclouds. Neural networks are sometimes saturated for specific situations meaning they perform worse on some scenarios due to capacity issues. The prediction results in the LiDAR point cloud domain are deteriorating with increasing distance due to the low density of the measurement in the remote areas. We are using a multi-view ensemble, which consists of detection models operating on a separate view and merging the transformation into one data representation. We are using frontview projection, which is the transformation of the canonical coordinates of the LiDAR point cloud to the spherical coordinates. The second view is the projection of scan points to xy plane called Bird’s Eye View (BEV). In the both projections we merge models focusing on specific areas or distance range. We further exploit semi-supervised learning approach called pseudo-labelling in order to generate labels from the ensemble for baseline improvement. All methods are evaluated on semantic segmentation tasks in autonomous driving scenarios and achieve improvement in terms of IoU against the baseline architecture.
Status of antioxidants and free radical scavenging enzymes in heat stressed buffalo calves

A study was carried out in male and female buffalo calves of Murrah breed belonging to arid tracts from India to find out status of antioxidants and free radical scavenging enzymes during hot ambience. Blood sample were collected during moderate and hot ambiences. Maximum ambient temperature ranged from 44.5-46.5 degree centigrade. All calves were managed in similar condition and were apparently healthy. The antioxidants selected were Vitamin C and Glutathione and free radical scavenging enzymes was Glutathione reductase. The moderate (control) mean values of vitamin C and Glutathione in erythrocytes were 2.50±0.02 μmol gHb-1 and 1.51±0. 03μmol gHb-1, respectively. The mean values of vitamin C and Glutathione in erythrocytes were 1.54 and 1.23 times lower, respectively during extreme hot ambience than respective moderate ambience mean values. The moderate (control) mean values of Glutathione reductase in erythrocytes were 0.82±0.01kU gHb-1. The erythrocytes mean values was 1.97 times higher during extreme hot ambience than respective moderate ambience mean values. The sex and age effects were significant (p≤0.05) in moderate and hot ambiences for all parameter studied. The mean values were significantly (p≤0.05) higher in male buffalo calves than female buffalo calves. In each ambience the age effect showed a significant (p≤0.05) increase in the mean values being highest in the buffalo calves of 6 monthsone year of age. Results clearly indicated that status of antioxidants and free radical scavenging enzymes changed in heat stressed calves probably to scavenge higher number of free radicals formed during hot ambience. All above three parameter used as important biomarker of oxidative stress. Present study provided data which can be used as reference values to for future studies and for diagnostic
An Improvement to TF*PDF: Salient Long Running Event Detection from News Documents based on various features

Automated extraction of popular news from the freely accessible news corpora is becoming important in today’s internet world. Because of the availability of large volumes of news wire sources, it is hectic for the human beings to search and decide whether it is popular or not. This necessitates a tool which should extract hot news in a period of time. Term weighting is a useful technique which extracts salient features from the text documents. Though, there exist different tools based on different term weighting algorithms, these are inaccurate in the extraction of hot news. In this paper, a new feature extraction algorithm for long running events based on frequency, position, scattering and topicality is proposed. Experimentation has been done on different retrospective news wire sources. Experimental results demonstrate that the proposed algorithm is suitable for extracting hot news.
Ray-Traced Constructive Solid Geometry

This project presents an implementation of constructive solid geometry (CSG) for ray-traced rendering. Our ﬁrst challenge was extending the existing code base to allow for parsing of constructive solid geometry trees. Then, we extended the intersection testing to record spans of intersection with primitives. Finally, we implemented the union, intersection, and diﬀerence set operations on collections of spans to resolve the intersection of rays cast through constructive solid geometry objects, and identify the true surface of the object for rendering. Our implementation supports reﬂection, including self-reﬂection, and can assign diﬀerent materials for each primitive in the constructive solid geometry tree.
An Efficient Adaptable Exact Solution Framework for the Capacitated Multi-Trip Vehicle Routing Problem with Time Windows and Its Variants

In this paper, we consider the capacitated multi-trip vehicle routing problem with time windows (CMTVRPTW), where vehicles are allowed to make multiple trips instead of just one as in the well-known capacitated vehicle routing problem with time windows (CVRPTW). The ability to perform multiple trips is favorable in real-world applications since it reduces the number of vehicles and drivers needed, thus, the operating costs for carriers. Nevertheless, CMTVRPTW is substantially more challenging to solve than the CVRPTW. To the best of our knowledge, all state-of-the-art exact methods struggle in solving Solomon instances with 50 customers. By contrast, our proposed efficient adaptable exact solution framework (EAESF) significantly outperforms all existing exact methods, which can solve All test instances with up to 70 customers to optimality and obtain a near-optimal solution with an average optimality gap less than 0.3% within a 3-hour time limit when there are 80 to 100 customers. The EAESF can be easily adapted to solve four CMTVRPTW variants and achieve exceptional performance. A linkable C++ library for reproducing the results in this paper and solving new instances is available at https://github.com/Yu1423/CMTVRPTW.
Towards a robust experimental framework and benchmark for lifelong language learning

In lifelong learning, a model learns different tasks sequentially throughout its lifetime. State-of-the-art deep learning models, however, struggle to generalize in this setting and suffer from catastrophic forgetting of old tasks when learning new ones. While a number of approaches have been developed in an attempt to ameliorate this problem, there are no established, uniﬁed or generalized frameworks for rigorous evaluations of proposed solutions; a problem which is particularly pronounced in the domain of NLP. The few existing benchmarks are typically limited to a speciﬁc ﬂavor of lifelong learning – continual open-set classiﬁcation – where new classes, as opposed to tasks, are learned incrementally. Moreover, the only general lifelong learning benchmark combines a multi-label classiﬁcation setup with a multi-class classiﬁcation setup resulting in misleading gradients during training. We empirically demonstrate that the catastrophic forgetting observed here can be attributed to the experimental design rather than to any inherent modeling limitations. To address these issues, we propose an experimental framework for true, general lifelong learning in NLP. Using this framework, we develop a comprehensive suite of benchmarks that target different properties of lifelong learning (e.g., forgetting or intransigence); experiment with diverse facets of language learning: multi-domain, multilingual, and different levels of linguistic hierarchy; and present a continuous evaluation scheme under a new metric: Area Under the Lifelong Test Curve . Our framework reveals shortcomings of prevalent memory-based solutions, demonstrating they are unable to outperform a simple experience replay baseline under the realistic lifelong learning setup.
Securing 5G: NATO’s Role in Collaborative Risk Assessment and Mitigation

The 5th generation of mobile systems (5G) unleashes a new cohort of services that promise to revolutionise transportation, manufacturing, and healthcare and to have a major economic impact. 5G systems are also being adopted by military organisations. They introduce a unique set of security challenges related to the trend towards a ‘softwarisation’ of the network, the support for high-reliability services, and the international supply chain for these networks. This paper outlines measures that governments, and in par-
Flow-Based SBFL Techniques

Automated fault localization techniques assist developers with the task of pointing out program elements that are most probable to be responsible for a detected error. Over the years, many different techniques have been developed [4]. Spectrum-based fault localization (SBFL) is a statistics-based technique that operates on execution information that is collected from successful and failing runs of a software system. For each considered program element, a suspiciousness score is calculated based on its execution or non-execution in the runs. The basic idea is that execution in failing runs adds to the suspiciousness of the element, while execution in successful runs makes the element less suspicious. Non execution, on the other hand, has the adverse effect. Traditionally, SBFL techniques operate on statement or method level, but there also exist more intricate approaches that collect data about the execution of control flow and data flow elements, e.g., def-use pairs [3, 2], information-flow pairs [2] and dependence chains [1]. While these approaches are more powerful for locating some types of bugs, they also cause more overhead in terms of performance. The goal for the student is to evaluate and discuss the current state of the art of flow-related fault localization techniques and to compare them to techniques that operate on structural program elements like, e.g., statements.
Building parties on the ground: Explaining grassroots party activism in Buenos Aires

What can explain the ongoing persistence of grassroots party activism? Despite multiple studies demonstrating the decline in “party on the ground” organisation, recent literature indicates grassroots activism remains alive in both theory and practice. This paper examines a new Argentine political party, Nuevo Encuentro, which, in a short period of time, built a large grassroots organisation of local branches and activists, and argues that the role of territory is a key explanatory factor. Based on a qualitative analysis of Nuevo Encuentro in Buenos Aires it demonstrates that a territorial organisation strategy helped the party: accumulate already mobilised activists; sustain and further motivate activists through a local branch structure; and recruit new members via linkages with urban communities. In so doing the paper extends literature on party-building and high-intensity activism and also contributes to the dismantling of methodological state-centrism in party scholarship by highlighting the significance of local organisational strategies.
Determination of sun protective factors (SPF) and antioxidant activity of ethanolic extract of rambutan rind (Nephelium lappaceum L.)

Background, The fruit peels of rambutan (Nephelium lappaceum L)are organic garbage with many potential health benefits but have not been used optimally. Purposes This study aims to determine the sun protective factor (SPF) and antioxidant activity of ethanolic extract of rambutan rind. Methods The rambutan (Nephelium lappaceum L) rind was extracted by maceration method using ethanol as solvent. A simple, rapid and reliable in vitro method was used to assess the SPF values by measuring the absorbance of diluted extract between 290-320 nm at every 5 nm intervals using Ultraviolet Spectrophotometry. The SPF values were calculated based on the recorded absorbance using a simple mathematic equation developed by Mansur. The antioxidant activity of rambutan rind was determined by the 1,1-diphenyl-2-picryl-hydrazil (DPPH) assay whereas vitamin C as positive control. Results The SPF values of rambutan rind in concentration series of 50, 100, 150 and 200 ppm were 6,47±0,45 , 9,26±0,28, 13,01±0,33, and 16,17±0,63 respectively. The IC50 value of rambutan rind was 41,47 ± 3,89 µg/ml whereas the IC50 value of vitamin C was 24,87 ± 0,69 µg/ml. Conclusion Based on the result, the ethanolic extract of rambutan rind has potential as sunscreen and antioxidant.
Distributed Sketching with Traffic-Aware Summaries

Network measurements are important for identifying congestion, DDoS attacks, and more. To support realtime analytics, stream ingestion is performed jointly by multiple nodes, each observing part of the traffic, periodically reporting its measurements to a single centralized server that aggregates them. To avoid communication congestion, each node reports a compressed version of its collected measurements. Traditionally, nodes symmetrically report summaries of the same size computed on their data. We explain that to maximize the accuracy of the joint measurement, nodes should imply various compression ratios on their measurements based on the amount of traffic observed by each node. We illustrate the approach for two common sketches: The Count-Min sketch (CM), which estimates flow frequencies, and the K-minimum-values (KMV) sketch, which estimates the number of distinct flows. For each sketch, we compute node compression ratios based on the traffic distribution. We perform extensive simulations for the sketches and analytically show that, under real-world scenarios, our sketches send smaller summaries than traditional ones while retaining similar error bounds.
Machine Learning-Future of Quality Assurance

Machine learning models represent a data framework that takes data from a particular set and makes assumptions about the new observation through learning from the data. Machine learning techniques are developed to operate on the current data set and predict the existing trends. Machine learning uses neural networks for quality inspection. Neural networks are made out of a set of structured algorithms modified as per the process of learning. To create outcomes and then compare them with set outcomes, the learning process needs data inputs. Additionally, to produce fast and reliable performance, programs use technology to extract data trends and interpret the overwhelming data volume. Machine learning uses AI technology to provide programs to understand dynamically without specific scripting or human interaction. Experience-based applications and test automation can boost and continuously read information, check up with it, learn from the findings, and enhance the detection methods. Concerning the potential of machine learning testing
REAL TIME VOICE CLONING

: Recent progress in deep learning has shown impressive results in the area of speech-to-text. For this reason, a deep neural network is usually trained from a single speaker using a corpus of several hours of voice recorded professionally. Giving such a model a new voice is highly expensive, as it needs a new dataset to be collected and the model retrained. A recent research has developed a three-stage pipeline that allows you to clone an unseen voice from just a few seconds of reference speech during practice and without retraining the template. The researchers share strikingly natural-sounding findings. The plan is to replicate this model and open source it to the public. With a new vocoder model, the aim is to adapt the framework to make it run in real time. The aim is to develop a three-stage deep learning system that will perform real-time voice cloning. This framework is the result of Google's 2018 paper, for which only one public implementation exists before ours. The system could capture a realistic representation of the voice spoken in a digital format from a speech utterance of only 5 seconds. Because of a text prompt, it can use any voice extracted from this process to perform text-to-speech. With our own implementations or open-source ones then plan is to replicate each of the three stages of the model. The plan is to implement successful models of deep learning and appropriate pipelines for pre-processing information. The next step is training these models from several thousand speakers for weeks or months on large datasets of tens of thousands of hours of speech. Instead examine their strengths and their drawbacks. The main focus on making this system function in real time, that is, to allow a voice to be captured and speech to be generated in less time than the duration of the speech produced. The framework will be able to clone voices it has never heard during
BallerStats: Estimating Distance Traveled with Video Footage

Obtaining advanced statistics beyond those simply observable to the naked eye can prove immensely useful in basketball, helping teams and players glean key insights into their performance and how their movement on the court may translate to points on the board. This type of data is currently available to professional teams in the National Basketball Association (NBA). Still, it remains largely out of reach for everyday pickup basketball players who want to improve their game. There is a company, Second Spectrum, that is officially partnered with the National Basketball Association (NBA) and provides teams with these advanced statistics to help them improve their game. This type of digital analysis would help save hours of manual review on film and provide more accurate data to coaches and players. This company currently utilizes a player tracking system and various undisclosed machine learning and computer vision techniques [1]. Similarly, a group of students from Stanford University recently completed a project to track basketball players from NCAA basketball games and map the player’s positions from the court onto an aerial view of the court, showing how a player’s movement changed between frames [7]. Using computer vision to obtain various advanced statistics in basketball is solvable using computer vision technologies. Our project estimates the distance a player has moved on a court, which is not an easy statistic to track while a game is occurring. This problem is solvable as it relies on object tracking, single view depth perception, and camera calibration. Object tracking has been a staple of computer vision for quite some time and is reasonably reliable. Similarly, camera calibration using checkerboards is accurate and dependable, as found in [8]. Simultaneously, the accuracy of single view depth perception has been drastically improved by deep learning techniques in recent years [6]. To address this issue, we have figured out a way to obtain this data through computer vision by tracking and identifying a single player throughout a video and then determining the real-world coordinates of the player. We can accomplish it by finding and using the intrinsic matrix of a camera and the depth of an object in an image. We obtained our intrinsic matrix through camera calibration and depth by utilizing single view depth perception. This approach then allowed us to compute and track the distance traveled by the player.
SkinDeep: Diagnosing Dermatological Images via Computer Vision

In this paper, we use the state-of-the-art VGG19 pre-trained on the ImageNet dataset to classify common skin diseases and cancers. We train on the ISIC 2019 challenge dataset consisting of 25,331 images across 8 classes. Due to the large class imbalances in the dataset, by using careful preprocessing and weighted cross-entropy loss, we acheive a average weighted recall score of 0.72.
Real Robot Challenge: Phase 3 Report

Our approach combines motion planning with several scripted motion primitives to manipulate the cuboid. In the previous two phases we focused on learning corrections to our controller, but in this phase we did not have time to train and test the models. However, we find that our controller is able to complete the four tasks. A video of our approach can be found here.
Shorter Fasting Periods Improve Athletic Performance among Mixed Martial Artists

Objective: This study aimed to relate weight management practices to athletic performance in Mixed Martial Arts (MMA). MMA athletes reduce body mass through intentional dehydration and fasting, known as Rapid Weight Loss (RWL), to qualify for weight-class competitions. Methods: Nine male MMA athletes were observed during a week of competition. Food journals were collected to measure fasting duration. Urine Specific Gravity (USG), body composition, and peak punch velocity were assessed at baseline and endpoint. USG and body mass were assessed at weigh-ins. Self-rated performance was also assessed. Descriptive statistics, Pearson correlation coefficients, and chisquare analyses assessed the significance of relationships between variables. Results: After reducing body mass through RWL (M=14.54 lb, SD=4.21 lb), eight participants (88.9%) were dehydrated at weigh-ins. Only one participant (11.1%) failed to achieve euhydration prior to competition. Muscle-and-bone mass changes were positively correlated with changes in punching velocity (r=0.698, p<0.05). Shorter fasting periods, defined as less than 24 hours, had a statistically significant relationship with improved punching velocity χ2 (1, N=9)=5.760, p<0.05. Additionally, improved punching velocity was positively correlated with self-rated performance (r=0.676, p<0.05). Conclusions: These findings suggest athletes and coaches in combat sports may benefit from utilizing shorter fasting periods of less than 24 hours during RWL to improve athletic performance after reducing body mass by 5%-12%. However, a more robust study of these factors across a larger sample is warranted before drawing conclusions.
Digital Transformation of Forest Services in Finland—A Case Study for Improving Business Processes

This case study introduces an innovation and development concept for agile software tools for the improvement of the productivity and customer experience of forest services. This need was recognized in the context of the opening of forest data and the development of service platforms for a forest-based bioeconomy in Finland. The forest services that were studied covered a continuum from a single type of work, e.g., soil preparation and young stand management through timber procurement, to comprehensive forest property management services. The study concentrated on the needs of micro-, small, and medium-sized enterprises (SMEs), which provide either retail- or business to business (B2B) services as sub-contractors. In addition, the challenges and bottlenecks in service processes detected by other stakeholders were considered. The prevailing service processes were conceptually modelled in order to search for opportunities for improvements in business and ecosystem services, i.e., agile software concepts. For example, we examined whether it would be possible to create opportunities for flexible operational models for precision, resilience, and protection of valuable microsites in forests. These software concepts were developed and evaluated in co-operation with the stakeholders in a co-creative workshop. The technological feasibility and commercial viability of the concepts, as well as the desirability for the customer were considered. The results of this business development process—i.e., agile software concepts and their anticipated benefits—were provided for further evaluation. In addition to the practical implications of this kind of innovation process tested, the potential of these kinds of agile tools for the further development of knowledge-intensive service processes was further discussed.