Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Model to Detect the Presence of Hinglish Text in YouTube Data

Sentiment Analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. Majority of the content available on various social media websites is in English Language. Due to advent of technology and access to Internet in huge populated country like India, people tend to share their views on a language that they are more comfortable in. This gives rise to users sharing their opinions in code-mixed languages such as Hindi mixed with English. Opinions in the form of tweets or comments are available all-over social media. These views/comments posted by the users can be analyzed for various purposes. This paper proposes a model that will find the percentage of Hinglish text present in the text retrieved from various social media platforms. The user can keep or discard the text for analysis depending on the percentage of Hinglish text present. The accuracy value attained is 83%, this can further increase when add more words in the dataset that is classifying the comments.
Multi-Granularity Feature Fusion Network for Dynamic Sequential Facial Expression Recognition

Dynamic sequential facial expression images can effectively reflect the essence of dynamic process of facial expression, and it is easy to eliminate the influence of various interference factors existing in static facial expression images. However, most previous studies only recognize static facial expression images, ignoring the facial expressions change is a dynamic and continuous process. To address these issues, this work proposes a network based on multi-granularity feature fusion for dynamic sequential facial expression recognition. The proposed network firstly extracts 68 facial landmarks of each frame in the sequence, and then calculates two types of offset distances of different frame’ s facial landmarks in the sequence. One is the offset distances of landmarks between each frame and the first frame. Another is the offset distances of landmarks between two adjacent frames. These distances are served as the coarse-granularity feature of the facial expression sequence. Meanwhile, the facial features of single image in the sequence are used as the fine-granularity feature of the facial expression sequence. Finally a dual channel neural network model is built to learn muti-granularity features for predicting facial expression. Compared with the experimental results of mainstream facial expression recognition methods on the CK+ and Oulu-CASIA datasets, this method has improved the accuracy by 1.21% and 3.34% respectively.
Increasing Return on Annotation Investment: The Automatic Construction of a Universal Dependency Treebank for Dutch

We present a method for automatically converting the Dutch Lassy Small treebank, a phrasal dependency treebank, to UD. All of the information required to produce accurate UD annotation appears to be available in the underlying annotation. However, we also note that the close connection between POS-tags and dependency labels that is present in UD is missing in the Lassy treebanks. As a consequence, annotation decisions in the Dutch data for such phenomena as nominalization and clausal complements of prepositions seem to differ to some extent from comparable data in English and German. Because the conversion is automatic, we can now also compare three state-of-theart dependency parsers trained on UD Lassy Small with Alpino, a hybrid Dutch parser which produces output that is compatible with the original Lassy annotations.
Effects of CCK-4 infusion on the acoustic eye-blink startle and psychophysiological measures in healthy volunteers

The acoustic startle response (ASR) and a range of psychophysiological parameters were evaluated during a continuous intravenous administration of cholecystokinin-tetrapeptide (CCK-4) in healthy volunteers. Subjects (n=28) were randomly assigned to double-blind infusion of either CCK-4 (0.5 mg/60 min) or placebo. The ASR sessions were performed prior to infusion and at 20 min and 50 min after the onset of infusion by recording eye-blink response to a series of acoustic stimuli (110 dB, 40 ms). An effect of CCK-4 on the eye-blink startle was observed in the first half of infusion. CCK-4 produced an increase of eye-blink startle amplitude from baseline values in contrast to the decrease observed at this time point with placebo. A mild increase in anxiety and heart rate followed by fatigue was reported with CCK-4. Administration of CCK-4 produced increases in plasma concentrations of adrenocorticotropic hormone, cortisol, prolactin and growth hormone. The results of this study show that a prolonged intravenous administration of CCK-4 may be a useful challenge method for further studies on the role of CCK system in the modulation of human anxiety and stress response.
Towards A Contextual and Scalable Automated-testing Service for Mobile Apps

As app quality is a deciding factor for user base growth, many automated testing services are available to reduce app developers' burden. However, we argue that these existing services do not sufficiently bring real-world contexts into app testing, which reduces the visibility into how an unreleased app would perform in the wild. In fact, this is a challenging problem that current emulator-based or device-based testing services cannot properly or scalably address. This paper envisions a split-execution model for building automated and contextual testing services for mobile apps. This model allows the service to evolve over time, by adopting new algorithms and recruiting new physical devices. Finally, preliminary results from a prototype demonstrate the potential and feasibility of our proposed architecture.
On the Trade-Off Between Privacy and Information Quality in Location Based Services

Location based services (LBS) are leveraged in everyday services and applications, as they can provide contextual and relevant information for the user needs. These services require the location of the user to be sent along with other relevant information, to provide the data in return that is relevant to the sent position. Although this opens up exciting scenarios for users, it has also been studied since it encompasses several potential privacy issues, which range from the re-identification of the user to the discovery of habits and routines. In this work, we present a study on the tradeoff between the information quality obtained from an LBS and the location precision sent by the user. Our results indicate that by sending out queries with imprecise location enhances the privacy of the users, while still providing a satisfactory quality of information.
The Influence of User Profile and Post Metadata on the Popularity of Image-Based Social Media: A Data Perspective

In modern internet communication, social media platforms have emerged as indispensable tools for self-expression, social engagement, and content distribution. Nevertheless, existing research on predicting social media popularity tends to over-look the complex interactions between different characteristics of user profiles, such as geolocation and account type, and post metadata, such as postdate and hashtags. This paper addresses this gap by investigating the factors derived from user profiles and post metadata contributing to content popularity within social media environments, with a specific emphasis on image-sharing. Utilizing a comprehensive dataset, we employ artificial intelligence-based data analytics techniques to uncover patterns, correlations, and predictive models, thereby providing a detailed insight into the dynamics of popularity within these online spaces.
Optimising Swarm Robotic Navigation: A Comparative Analysis of Fastest Path vs. Nearest Neighbour Path Projection Strategies

Swarm robotic systems hold the potential to revo-lutionise various fields by executing complex tasks collectively. Efficient navigation remains a pivotal challenge that can significantly impact the performance and applicability of swarm robotic systems. This study delves into exploring two distinct path projection strategies, namely the Fastest Time/First Path to the Goal and the Nearest Neighbour methods, to optimise the navigation of a swarm of Kilobots towards a designated goal. Through a series of experiments, each strategy's efficiency and time effectiveness are thoroughly analysed and compared. The Fastest Time/First Path to the Goal strategy endeavours to minimize the time taken by having subsequent Kilobots follow the trail of the quickest Kilobot to reach the goal. On the other hand, the Nearest Neighbour strategy, utilizing the Euclidean Path Cost Estimation technique, aims at projecting the path with the minimum overall cost for Kilobots to follow, promoting a cost-effective navigation approach. The findings reveal that the Nearest Neighbour strategy emerges as a more balanced and efficient approach, thereby presenting substantial promise for further research in swarm robotics navigation. The insights gathered from this study have implications for the application of swarm robotics in dynamic and varied environmental conditions.
Weathered wooden surfaces: their influence of the durability of coating systems

This paper concentrates on the condition of wood prior to painting, how wood degrades, and how this affects the durability of exterior coatings. From the work reported here, it can be concluded that a paint system applied to a sound or unweathered wooden surface will have far better durability than if the wood had been exposed to normal weathering, even for a relatively short period of time. -- AATA
Improved Deep Learning Laryngeal Tumour Identification

Laryngeal cancer incidence has significantly increased recently all over the globe. There are several difficulties in correctly diagnosing and treating laryngeal cancer, especially when it is advanced. This kind of cancer affects patients’ head and neck areas and is a complicated malignancy. Researchers have created a number of diagnostic techniques and instruments throughout time to help clinical specialists accurately diagnose laryngeal cancer. However, these currently available techniques and methods show a number of performance-related drawbacks, including reduced early-stage laryngeal cancer detection accuracy, increased computational complexity, and extended patient screening periods. Furthermore, our suggested methodology greatly accelerates the real-time screening of patients by successfully catching and recognizing even small laryngeal cancers. The time saved by the professionals due to this efficiency allows them to screen more patients each day. When tested utilizing the ImageNet dataset, the outcomes of our suggested model are more accurate and useful, boasting a rate. Numerous research on laryngeal cancer detection have been conducted recently, each using a different methodology. Future research has a lot of chances to investigate innovative methods for detecting laryngeal cancer by using varied and substantial medical picture databases.
Flexible Communication and Control Protocol for Injectable Neuromuscular Interfaces

BION2 is a system based on injectable neuromuscular implants whose main goal is to restore the functional movement of paralyzed limbs. To achieve this objective, the functional requirements of the implanted interfaces include not only stimulation but also integrated sensors in order to detect patient intention, to provide servocontrol of muscle activation and to sense posture to inform more global motor planning and coordination. The technical constraints for managing the system include the efficient use of forward and reverse telemetry channels with limited capacity, minimization of adverse consequences from errors in data transmission or intermittent loss of power to the implants, and ability to adjust stimulation rates and phases to achieve efficient fine control of muscle force while minimizing fatigue. This paper describes a communication and control architecture with several novel features that address these requirements
A bug you like: A framework for automated assignment of bugs

Assigning bug reports to individual developers is typically a manual, time-consuming, and tedious task. In this paper, we present a framework for automated assignment of bug-fixing tasks. Our approach employs preference elicitation to learn developer predilections in fixing bugs within a given system. This approach infers knowledge about a developer's expertise by analyzing the history of bugs previously resolved by the developer. We apply a vector space model to recommend experts for resolving bugs. When a new bug report arrives, the system automatically assigns it to the appropriate developer considering his or her expertise, current workload, and preferences. We address the task allocation problem by proposing a set of heuristics that support accurate assignment of bug reports to the developers.
PoliMedia: Analysing Media Coverage of Political Debates by Automatically Generated Links to Radio & Newspaper Items

markdownabstractStudents and researchers of media and communication sciences study the role of media in our society. They frequently search through media archives to manually select items that cover a certain event. When this is done for large time spans and across media-outlets, this task can however be challenging and laborious. Therefore, up until now the focus of researchers has been on manual and qualitative analyses of newspaper coverage. PoliMedia aims to stimulate and facilitate large-scale, cross-media analysis of the coverage of political events. We focus on the meetings of the Dutch parliament, and provide automatically generated links between the transcripts of those meetings, newspaper articles, including their original lay-out on the page, and radio bulletins. Via the portal at [www.polimedia. nl](http://www.polimedia.nl/) researchers can search through the debates and find related media coverage in two media- outlets, facilitating a more efficient search process and qualitative analyses of the media coverage. Furthermore, the generated links are available via a SPARQL endpoint at [data.polimedia. nl](http://data.polimedia.nl/) allowing quantitative analyses with complex, structured queries that are not covered by the search functionality of the portal, thus challenging the student to go across the academic borders and enter fields that previously have been neglected.
Answer Relevance Score (ARS): Novel Evaluation Metric for Question Answering System

The evaluation of question answering systems plays a crucial role in assessing their performance and effectiveness. Existing evaluation metrics often focus on aspects such as recall, precision and F1 score, but may not fully capture the relevance and informativeness of the generated answers. In this paper, a novel evaluation metric called Answer Relevance Score (ARS) is proposed that takes into aspects i.e. correctness and completeness. Correctness considers the comprehensiveness of answer which consider the semantic and contextual analysis of sentences selected for answer. The answer's completeness analyse the rate of information overlap in reference and generated answer. ARS is compared with existing metrics and to this end, comparative study is also given for evaluating the efficiency of existing metrics across two datasets; Natural Questions and WikiQA. It is find that ARS metric correlates much better with human judgement than other benchmarks.
Luxury Goods Identification based on Multi-Head Convolutional Neural Network with Attractor Loss

The counterfeit goods trade is a global issue. Traditionally, customized equipment, elaborate labeling, and expert inspection are the primary ways to identify fakes. In recent years, deep learning technology has been used for counterfeit identification based on image detection and classification. There are two critical challenges. First, in real-world situations, the counterfeiting patterns are diverse, while the genuine ones are often identical. This is because counterfeit goods may be partially assembled from some parts of genuine products. Second, authenticating a counterfeit as genuine can cause a lot of trouble. To address these issues, we propose a novel end-to-end algorithm that combines a new Multi-Head Convolution Neural Network (MH-CNN) with a new Loss function named Attractor Loss (AtL) and applies it to image classification of real and fake goods. Technically, our method takes multiple images of different positions of an object as input. Then it outputs 1) the classification probability of belonging to a real class, and 2) a feature vector that is used for nearest neighbor retrieval. On engineering implementation, we integrate an MH-CNN structure, which contains a shared CNN backbone for extracting deep features and multiple heads for computing the deep representation of different positions. To improve the recognition performance, we propose a joint training method to train the MH-CNN model and propose a new AtL to filter vector representation of images from real class and pull them closer for enhancing the recognition rate without loss of accuracy. Extensive experimental results demonstrate the superior performance of our proposed method.
Bulk controlled offset cancellation mechanism for single-stage latched comparator

A reliable offset cancelation strategy is proposed for single-stage latched comparators by in turn domination of positive and negative feedback devices. Feedback type is cautiously swapped by small voltage variations on bulk terminals of PMOS devices. For offset cancellation, at first a reset switch removes the previously latched data on comparator's outputs while the inputs are shorted together. Secondly, the sampled offset is dependably pre-amplified providing a finite gain in presence of slightly stronger negative feedback. Positive feedback cannot be dominated right after the reset phase because small voltage might be still remained across the switches. Thirdly, the loop gain is considerably increased by slightly dominating the positive feedback. Then the main comparison is similarly scheduled by identical controls on bulk terminals. Worst-Case simulation results confirms that the proposed comparator can detect 2 mVolts input difference, at all corner conditions, in presence of 15 mVolts offset voltage at 625 MS/s comparison rate. Monte-Carlo simulation results show that the standard deviation of input referred offset is 0.35 mV while the input offset is within the range of ±15mVolts. Power consumption is 1.2 mW at 625 MS/s comparison speed. Simulations are performed for all corner conditions using the BSIM3 model of a 0.18 μm CMOS technology.
Optimization of the Versatile Tensor Accelerator (VTA) Load Module in a Time-Triggered Memory Access

Embedded systems powered by artificial intelligence (AI) are widely employed in diverse domains. However, the lack of inherent predictability in existing AI accelerators poses significant challenges, especially in safety-critical applications where deterministic safety specifications are essential. Moreover, temporal unpredictability caused by memory access contention further hinders the suitability of current platforms for safety-critical tasks. To address these issues, we propose a time-triggered memory access approach for the versatile tensor accelerator (VTA) that provides temporal predictability guarantees for load data. Our research focuses on investigating the temporal predictability of Neural Network load input, weight, and operations. We introduce a time-triggered memory access mechanism that pre-fetches data from DDR memory ahead of the VTA load module request. Through extensive experimentation and analytical evaluations in the VTA runtime environment, we demonstrate the effectiveness of our time-triggered memory access concept. The results reveal an 8% performance improvement and reduced execution time while upholding strict safety specifications and predictability. These findings establish the feasibility of employing a time-triggered approach for runtime neural network prediction.
Link Setup Latency in Vehicle-to-Vehicle WLANs: A Comparative Study

Decentralized content distribution over vehicle-to-vehicle (V2V) WLANs holds promise to mitigate load on cellular networks. The delay in establishing a communication link is a key to maximize V2V data transfer opportunity, as contact duration of vehicles is often limited due to their fast mobility. IEEE 802.11ai standard defines Fast Initial Link Setup (FILS), which simplifies the link setup process to reduce latency. In this paper, we investigate the benefit and limitations of FILS in V2V WLANs. We develop a testbed involving multiple vehicles, and compare the link setup latency of FILS with regular EAP authentication. Counterintuitively, regular EAP achieved equivalent performance in V2V WLANs due to architectural constraints of FILS.
A Heuristic Stable Time via Two-Hop Dissimilar Trees in Wireless Sensor Networks

A wireless sensor network WSN is composed of one base station BS and many sensor nodes SNs that sense and transmit data toward the BS. After data transmission many times, some SN dries out its energy, when the time is termed a stable time or a first death node time FDN. Our research interest is to examine the possibility of extending such FDN of a given routing protocol in a WSN. During WSN operations, spanning in-trees of data flow are easily collected at the BS. To such trees, we have so far applied a greedy heuristic algorithm to select dissimilar ones, whose size is equal to an FDN. As, examples, we deal with two layer 3 routing protocols LEACH (Low Energy Adaptive Clustering Hierarchy) and EECS (Energy-Efficient Clustering Scheme). Simulation experiments of our algorithm show that both have room to extend their FDNs.
Permeation Characteristic of Silk Micro-Particle Controlling by External Electric Field

Transdermal iontophoresis is the system that delivers the ionic active ingredient through skin controlling by applying low-level electric current. In this research work, silk micro-particle (MS) was selected as active ingredient. MS loaded gelatin hydrogel (MS/Ge) was prepared for facial mask application. The effect of crosslinking agent and electrical voltage on physical, chemical and permeation characteristic of MS from gelatin hydrogel (Ge) was investigated. The passive and iontophoresis permeation characteristic was conducted by Modified Franz Diffusion Cell at pH 7.4, 37 °C for 24 h under applying electrical voltage of 0, 0.01. 0.05, 0.1 and 0.3 V. To study the effect of crosslinking agent, Ge was prepared at various amount of crosslinker (5, 10, 15, and 20 %(w/w) for MS/Ge/Ga_1, MS/Ge/Ga_2, MS/Ge/Ga_3 and MS/Ge/Ga_4, respectively). The %swelling of MS/Ge was first characterized. The % swelling of MS/Ge/Ga_1, MS/Ge/Ga_2, MS/Ge/Ga_3 and MS/Ge/Ga_4 was 1533.55%, 1154.71%, 1200.76% and 1026.59%, respectively. The amount of MS permeation increased with hydrogel mesh size which corresponds to % swelling of Ge. For iontophoresis permeation, the amount of MS permeation was higher than passive permeation due to electro-repulsive force and expansion of hydrogel pore size. The permeation kinetic was calculated. The permeation was controlled by nonfickian diffusion. The amount of micro silk particle permeation was controlling by amount of crosslinker and electrical voltage. Micro silk particle loaded gelatin hydrogel will be alternative material for cosmetic product.