Reviewerly/exHarmony huggingface dataset:
Raindrop Recognition and Spectrum Analysis Algorithm based on Stroboscopic Image Processing

Raindrop spectrum is one of the most important physical characteristics of precipitation. The accurate collection of raindrop spectrum data is of great significance for meteorological observation. A raindrop recognition algorithm based on stroboscopic photo processing is proposed to accurately locate all raindrops in the picture, identify and cluster the raindrop data, and finally obtain the raindrop spectrum. Experimental results show that the algorithm can identify raindrops efficiently and obtain raindrop spectrum data accurately.
Towards a Taxonomy of API Services in Logistics

Data are a valuable asset for companies in the logistics sector to optimize internally and develop new business models.They can be like a magnifying glass, making previously opaque logistical processes transparent and finding previously hidden optimization potentials.Typical applications are tracking the transport status, route optimization, monitoring pharmaceutical products, or monitoring shocks for fragile cargo along the trade lanes.One way to use data is to tap into publicly or commercially available Application Programming Interfaces (APIs).As a result, logistics service providers can get or provide data automatically via a machine-tomachine interface.However, the landscape of API service providers is vast, unstructured, and intransparent in terms of potential data that companies can leverage.Given their high potential for logistics, the paper proposes a taxonomy of API services in logistics based on the inductive analysis of three API databases.
Minimum Link Lengths Computation of Violin Bow Playing Robot Arm Using Kinematic Analysis

For a violin playing robot to be successful as an entertainment robot it needs to portray the image of a human arm playing the violin. Hence it is desired that the robotic manipulator has an articulated structure similar to the human arm. Since shorter link lengths result in smaller link weight, smaller actuator size/weight, and thus fast motion capabilities of the manipulator, it is crucial to determine the minimum link lengths of the manipulator. This paper presents a computation method of the minimum link lengths required for the bow motion and the kinematic design of the violin bow playing robot arm. The workspace of the bow playing arm is analyzed using the geometric dimensions of the bow and the violin bridge. The bow frog motion paths are computed for four violin strings. It is shown that a planar RRR mechanism can generate the motion required for violin bow playing robot, and the minimum link lengths of the manipulator are found that result in the robot workspace encompassing the bow frog motion paths and that avoids the singularities in the robot motion path.
I2P Forensic Analysis

I2P is an anonymous P2P distributed communication layer used to send messages to each other anonymously and safely. It is built on top of the internet and can be considered as an internet within the internet. Even though I2P is developed with an intention to create censorship resistant environment for the free flow of information, it is misused for illegal activities now a days. The possible misuses are less known among law enforcement agencies and existing industry approved software programs have no detection functionality for I2P. Because of the increased use of I2P in criminal purposes, there is a need for methods and tools to acquire and analyze digital evidence related to I2P. We conducted a detailed live memory dump analysis in order to find out the I2P related artifacts from a host machine. Furthermore, we propose a tool that will analyze the memory dump and system local files to find out the I2P related artifacts and provide a detailed report to the investigator.
An Empirical Study of Speaker Identification System for Mono and Traverse Linguistic Background Using EM and SMEM

In this eon of digitization, automatic detection of the speaker by employing speaker-specific information included in speech signals to validate identities asserted by an individual, to access the voice-controlled services is necessary for the security control of confidential information as in online banking systems, security vaults, etc. In this paper, the performance of the speaker identification system (SIS) for Indian languages is evaluated in the context of a traverse linguistic and monolinguistic background by employing Expectation-Maximization (EM) and Split and Merge Expectation-Maximization (SMEM) Method. Due to data constraints, three languages were considered: Hindi, Punjabi, and Indian English. A corpus of 50 speakers, 25 male, and 25 female, who speak three distinct languages, were produced. The spectral characteristics in this experiment were retrieved using the Linear Prediction Cepstral Coefficient approach. It has been observed that SIS efficacy for traverse linguistic conditions gets improved by SMEM in comparison with the EM method.
An Agent-Based Approach for Procedural Puzzle Generation in Graph-Based Maps

We present an algorithm to add puzzles to maps represented as graphs. The algorithm starts from an empty map, represented as a graph, with at least one entry area and one exit area. It runs several specialized agents responsible for adding puzzles (e.g., locked doors, keys, switches). It generates a map with at least one acceptable solution (path) whose difficulty depends on the type of agents used (that is, the variety of puzzles added) and the number of puzzles added by each agent. Most importantly, no sequence of actions can leave the players stuck in a dead-end situation with no way to reach the goal. We include two examples of agents specialized in (i) switch mechanics (e.g., a lever that opens a passage and closes another one, the lighting of a fire that shows an inscription needed to solve another puzzle), and (ii) element collection mechanics (e.g., collecting keys or other puzzle elements to open a passage).
Supervised Bilingual Word Embeddings for Low-Resource Language Pairs: Myanmar and Thai

Bilingual word embeddings (BWEs) represent the lexicons of two different languages in a shared embedding space, which are useful for cross-lingual natural language processing (NLP) tasks. In particular, bilingual word embeddings are extremely useful for machine translation of low-resource languages due to the rare availability of parallel corpus for that languages. Most of the researchers have already learned bilingual word embeddings for high-resource language pairs. To the best of our knowledge, there are no studies on bilingual word embeddings for low resource language pairs, Myanmar-Thai and Myanmar-English. In this paper, we present and evaluate the bilingual word embeddings for Myanmar-Thai, Myanmar-English, Thai-English, and English-Thai language pairs. To train bilingual word embeddings for each language pair, firstly, we used monolingual corpora for constructing monolingual word embeddings. A bilingual dictionary was also utilized to alleviate the problem of learning bilingual mappings as a supervised machine learning task, where a vector space is first learned independently on a monolingual corpus. Then, a linear alignment strategy is used to map the monolingual embeddings to a common bilingual vector space. Either word2vec or fastText model was used to construct monolingual word embeddings. We used bilingual dictionary induction as the intrinsic testbed for evaluating the quality of cross-lingual mappings from our constructed bilingual word embeddings. For all low-resource language pairs, monolingual word2vec embedding models with the CSLS metric achieved the best coverage and accuracy.
Neural Architecture Search as Self-assessor in Semi-supervised Learning

Neural Architecture Search (NAS) forms powerful automatic learning, which has helped achieve remarkable performance in several applications in recent years. Previous research focused on NAS in standard supervised learning to explore its performance, requiring labeled data. In this paper, our goal is to examine the implementation of NAS with large amounts of unlabeled data. We propose the NAS as a self-assessor, called NAS-SA, by adding the consistency method and prior knowledge. We design an adaptive search strategy, a balanced search space, and a multi-object optimization to generate a robust and efficient small model in NAS-SA. The image and text classification tasks proved that our NAS-SA method had achieved the best performance.
Fuzzy Logic Approach to the Amount of Medication Taken During Breathing with an Inhalator

The fuzzy logic is form of logic that helps professionals solve various issues in the field of health care by grading right and incorrect in classical reasoning. Experts decide whether to change the inhalation time medication dose using the inhaler depends on the patient's age, weight, height, and co-morbidities. In the study, the doses of more regularly prescribed inhaler medications were calculated by applying fuzzy logic. The study uses fuzzy logic to compute the medication dose that should be given to the patient set on the patient's age, human body’s mass index, and patient class at the outset. The research was based on calculating the medicine dosage without use of any other medications and assuming no premedication. The medication dosage levels discovered at the conclusion of the trial were quite close to the values recommended by specialists. As a result, fuzzy logic estimator utilized in this work may be thought of as a source of assistance for inhaler specialists and users.
Research on Energy-Efficient Path of Electric Vehicle Traveling

This research proposes energy control based on Dijkstra’s path planning and optimal control strategy to reduce the energy consumption of electric vehicles when traveling. We use path planning and consider the current remaining battery power, slope, number of turns and other parameters, and cooperate with the best control strategy to observe the energy consumption of the path, hoping to reach the destination and seek the lowest energy consumption under the battery power limit and the environmental gradient.
Smartboost Learning for Tabular Data

Abstract We introduce SMARTboost (boosting of symmetric smooth additive regression trees), a machine learning model capable of fitting complex functions in high dimensions, yet designed for good performance in small n and low signal-to-noise environments, and on data generated by the most common parametric models in financial econometrics. SMARTboost inherits many of the qualities that have made boosted trees the most widely used machine learning tool for tabular data; it automatically adjusts model complexity, handles continuous and discrete features, can capture nonlinear functions in high dimensions without overfitting, performs variable selection, and can handle highly non-Gaussian features. In addition, SMARTboost attempts to address some of the specific needs of empirical work in finance. The combination of smooth symmetric trees and of carefully designed Bayesian priors gives SMARTboost an edge (in comparison with a state-of-the-art tool like XGBoost) in most settings with continuous and mixed discrete-continuous features. Unlike other tree-based methods, it can also compute marginal effects. SMARTboost's performance is illustrated in numerous simulations and in two applications to global equity returns and realized volatility prediction.
SmartCoach: Comprehensive Tutor Recommender and Student Attentiveness Measuring Platform

With the pandemic, coaching or tutoring classes became to halt as the underlying system wasn’t facilitating to adapt to emergency teaching mechanisms due to sparse of relevant technologies and tutor unawareness. SmartCoach is an integrated platform that allows students to find suitable tutors based on recommendations and other parameters like proximity, previous track record etc. using sentiment analysis and natural language processing. For tutors, SmartCoach allows publishing learning materials, automatic quiz creation, tracking attendance and, attentiveness during classes using OpenCV library. Moreover tutors get a dashboard featuring critical aspects about their classes and, personal income. This research is aimed to introduce a comprehensive distance learning platform with novel technological approaches that connect both potential students and tutors to a common platform.
Sports photography and its artistic dimensionality

Sports photography is a powerful visual tool that can be used to promote sports, athletes and recreational physical activities. Her main role is to present sport as an art. In that respect, good sports photography also implies the recorded moment of the athlete in a specific movement position, which is unusual and which cannot be seen on television. When it comes to the promotion of professional sports, sports photography occupies a significant place in the sports press and sports magazines. On the other hand, sports photography also promotes recreational sports, which is intended for all those who want to do sports for themselves and their own health. Sports photography has the power to present important sports and recreational events to readers in a simple way, without large and dry texts. Good sports photography refers to the composition, angle, light, dynamics and color contrast, i.e. the relationship between warm and cold tones. Also, sports photography depends on good photographic equipment, knowledge and experience of a sport photojournalist. Sports photojournalists always try to present quality and interesting sports photographs in the most professional and high-quality way, which in an artristic way convey ''visual information'' of sports events to loyal readers and sports fans. It is very important to understand that sports photography is not only sports, but also artistic. Therefore, the aim of this paper is to present sports photography and its artistic dimension.
RTLNet: Recursive Triple-Path Learning Network for Scene Parsing of RGB-D Images

Scene parsing approaches have attracted extensive attention in recent years; although several methods have been developed for scene parsing, most include complex modules for both cross-modality fusion between RGB and depth images in the encoder and image scale level recovery in the decoder under label supervision for high inference accuracy. Cross-modality information in the encoder may be diluted when processed through the decoder, and the supervision results may not be reused effectively, which adversely affects scene parsing. To address these problems, we propose a recursive triple-path learning network (RTLNet) for cross-modality interactions in the decoder using global context and cross-modality fusion modules. The proposed modules fully use cross-modality information to reduce information loss. To enhance the robustness of RTLNet, we add a path to reuse the initial predictions from the decoder and introduce a ladder-shaped feature consistency module to further leverage multiscale features. Experiments are conducted with the proposed RTLNet and nine recent RGB-D indoor scene parsing methods on the NYUv2 and SUN-RGBD indoor scene datasets; the results show that the RTLNet outperforms the other methods.
Knowledge in Critical Events: Know-How at Work of Emerging Country Firefighters

This study analyses the know-how at work of emerging country firefighters involved in a critical event. The research was a qualitative exploratory study in which 19 firefighters were interviewed. The results show that the firefighter's work is marked by the intense use of sensory perceptions as hearing, feeling, smelling and seeing and artifacts as uniform, siren sound, victims’ cell phone ringing, military career, professional’s work instruments. The error and failure also appear as artifacts that mobilizes knowing-in-practice and, at the same time, it shows a fragile image of indestructible firefighters. Therefore, the know-how at work of emerging countries firefighters is performed by present and absent artifacts that mobilizes aesthetic judgments and emotions that are different when considering before, during and after the critical event. The study emerges as a possibility to rethink the organizational training of these professionals.
Medical Phrase Mining Method for Biomedical Text Processing

With the rapid increase of biomedical data, mining high-quality phrases from these unstructured text data has become a hot research issue. Converting unstructured text into structured units will greatly reduce the semantic ambiguity in the text, which is very important for mining high-quality phrases. Phrase mining is one of the basic tasks of text processing. It aims to extract high-quality phrases from a corpus and has many downstream applications, such as information retrieval classification, document classification, entity recognition, topic modeling, etc. In this paper, we proposed a new model, which selects candidate sets from the corpus through phrase frequency and calculates the characteristics of candidate phrases to evaluate the quality. We collected text data of questions and answers in the medical field as a corpus for experiments. Experiments show that the model can mine medical professional phrases, and solve the problem of long phrases missing, with good accuracy.
Free by Design: On the Feasibility of Free-Riding Attacks Against Zero-Rated Services

With free-riding attacks against zero-rated services, an attacker can avoid expenses by transmitting wrongly labeled zero-rated network traffic. When applied on large scale, these attacks impact network quality and revenue of network operators. Hence, various solutions are proposed to protect networks against this growing threat. While these protections cure some of the symptoms, they do not solve the underlying issue. In this paper, we argue that secure web services with high bandwidth requirements will increase the chances of free-riding attacks, when services are zero-rated by network operators. Therefore we show how tunnel-based free-riding attacks can be designed and implemented for secure services, such as instant messaging, cloud storage, and video chats. Furthermore, we evaluate these tunnels in terms of usability and performance and judge their feasibility. In addition, we also discuss the existing countermeasures and how they can be improved. As part of our work, we want to point out that free-riding attacks are a self-imposed problem created by Internet Service Providers that try to create artificial walls within a system that is free and open by design, such as the Internet. To tackle this issue, we publish the created tools to support those that are opposed to any form of zero-rating, censorship, and other forms of traffic discrimination.
MCTS-Based Robotic Exploration for Scene Graph Generation

Many researchers have used scene graphs to improve performance in some tasks in recent years, such as complete image matching, image generation, and visual questions answers. However, the current scene graph generation mainly constructs a single picture or a scene graph of a part of the scene. To construct a scene graph of the entire room, the agent needs to explore the entire room autonomously. We propose a scene graph generation method based on Monte Carlo Tree Search (MCTS). In the agent’s exploration of the room, the agent uses MCTS to select a reliable location among the locations that the agent can reach in the next step. After multiple searches, the agent can find most or all objects in the room. Experiments show that our method helps the agent finds more objects in a fixed step size than the agent’s random exploration of the room. The agent can create a complete scene graph because it sees most of the objects in the room.
Further Non-local and Multiple Granularity Network for Vehicle Re-identification

An algorithm that can effectively distinguish different vehicles with high similarity by identifying vehicle photos collected from multiple angles is called vehicle recognition algorithm. This algorithm has been extensively applicable to the region of intelligent transportation and urban computing, but it is always challenging to implement the algorithm. In this paper, an modified feature extraction method is provided, which enhances the traditional nonlocal neural networks and improves its ability to capture the relationship between different positions in the image. At the same time, we adopt a multi-branch global and local information learning strategy, which not merely captures the global features, moreover, those local parts of the feature can be more focused on finer discrimi-nating information in each part of the partition and filtering information on other partitions as the number of partitions increases. Finally, a hybrid relationship between channel feature fusion and channel level is introduced based on this learning strategy. The experimental results show that the MAP and Rank-1 indexes on the VERI-776 mainstream public dataset are 78.9% and 93.86%, which are the highest running scores in this dataset at the present stage, proving that the proposed algorithm is excelled other main stream design.
A New Hybrid Approach of Gravitational Search Algorithm with Spiral-Shaped Mechanism-based RBF Neural Network

This article proposes a neural network and a non-linear time series method via a prediction model based on an RBF neural network. The proposed model predicts and identifies a non-linear system using the Hybrid Gravitational Search Algorithm (HGSA). The proposed algorithm HGSA is deemed with the optimal parameter settings and network topology of a neural network. GSA is implemented with a spiral-shaped mechanism (SSM) to eradicate primary drawbacks such as slow convergence. Thus, it tends to premature convergence. Moreover, HGSA-SSM selects updated particles' locations through the most suitable selection law that provides an exact match in global and local search competencies. Additionally, HGSA-SSM could optimize the RBF neural network's parameters such that a network model is generated with high precision. Hence, our proposed novel proposed model (HGSA-SSM &#x2013;RBFNN) overcomes the non-linear problems by developing several numerical precedents, and it is found efficient than the existing RBF neural networks.
Mutual Exclusivity in Pragmatic Agents

Abstract One of the great challenges in word learning is that words are typically uttered in a context with many potential referents. Children's tendency to associate novel words with novel referents, which is taken to reflect a mutual exclusivity (ME) bias, forms a useful disambiguation mechanism. We study semantic learning in pragmatic agents—combining the Rational Speech Act model with gradient‐based learning—and explore the conditions under which such agents show an ME bias. This approach provides a framework for investigating a pragmatic account of the ME bias in humans but also for building artificial agents that display an ME bias. A series of analyses demonstrates striking parallels between our model and human word learning regarding several aspects relevant to the ME bias phenomenon: online inference, long‐term learning, and developmental effects. By testing different implementations, we find that two components, pragmatic online inference and incremental collection of evidence for one‐to‐one correspondences between words and referents, play an important role in modeling the developmental trajectory of the ME bias. Finally, we outline an extension of our model to a deep neural network architecture that can process more naturalistic visual and linguistic input. Until now, in contrast to children, deep neural networks have needed indirect access to (supposed to be novel) test inputs during training to display an ME bias. Our model is the first one to do so without using this manipulation.