Reviewerly/exHarmony huggingface dataset:
Forensics with IoT Based Systems’ Evidences: A Futuristic Review on Forensic and IoT Frameworks

The Internet of Things and digital forensics are the broad fields where the remnants of smart gadgets open doors for brilliant crimes. It is necessary to investigate the use and procedures of smart device involvement in criminal investigations. Despite the existence of research on the Internet of Things (IoT), scant consideration has been given to utilizing Digital Forensics (DF) methodologies for IoT-based infrastructure. It is feasible to perform digital forensic investigations on IoT systems using DF techniques (DFIs).IoT and DF tools’ dispute has not totally reconciled with one anotherUsing the present DF tools and methodologies, these techniques must address the heterogeneity of IoT infrastructures. As a result, potentially acceptable bits of evidence from IoT contexts are gathered, looked at, and assessed. They could be utilized as evidence in court to support a legal claim that poses a provocative challenge to DF investigators and law enforcement authorities (LEA). Hence, the issue addressed is that there are currently DF frameworks that support the use of DFIs in an IoT-based heterogeneous sensing environment but have not yet gained widespread acceptance. The authors have put out a general DFIF-IoT (Digital Forensic Investigation Framework for IoT) based on this hypothesis, which can enable future IoT investigative capability with a respectable level of assurance. The following benefits of the proposed framework: It complies with ISO/IEC 27043: 2015, which applies to a global standard for information technology, security measures, incident investigation concepts, and procedures. As a result, the authors anticipate that if the suggested paradigm is successfully adopted in future DF tool development, it will facilitate effective digital forensic criminal investigation for IoT infrastructure.
Ranking of Web Search for Best Link Identification by Using Hierarchy of Web Page Content

Due to explosion of online published content, there is an increasing need for search engines to fetch more relevant results with respect to a user query. A lot of work has already been done to improve search results and many algorithms and techniques have been devised. Search engines collect, index and process content in order to answer search queries accurately. In this work we are addressing web search and results presentation in a completely different perspective. Irrespective of how the search engine have ordered the results, we have proposed a technique to identify that which web link is more relevant to the user query amongst returned search results based on content of the page of returned link and its siblings. We have also demonstrated the effectiveness of the proposed technique with the help of experimental implementation and results.
Prototype of an Online Concert System that Brings the Viewers’ Sense of Participation

The spread of COVID-19 has limited people’s contact, thus various things have rapidly spread online, including live entertainment. Online Concert performances let all participants enjoy them equally regardless of location, thus eliminating regional disparities considered a problem in live entertainment. Yet, the special feeling that conventional offline live entertainment has does not exist. Therefore, we propose a camera device for online live performances that brings a sense of presence. We also discuss the value of online live entertainment and its future development in the after-Corona stage.
Hardware Optimization in Data Mining Using Logical Synthesis

The article discusses the use of logical synthesis in terms of reducing the hardware complexity of data analysis systems. The methods of linear decomposition and functional decomposition are discussed. The use of attribute reduction reduces the complexity of decomposition algorithms. The presented results indicate that logical synthesis methods can be successfully used to solve data mining tasks, effectively simplifying the hardware complexity of the systems and speed up the calculations.
XI Bass: Design of a Smart Sound Application that Implements the “Physical Feel” of Playing Slap Bass

In this paper, we design an analog bass application: XI BASS, which provides a more realistic bass playing experience specifically by reflecting the real feel of playing bass. Various analog bass guitar (bass) applications have been developed in the past. However, most of them are limited to simulating the bass fretboard screen as playing sound while touching the screen and analog keyboard-like functions. it is difficult to say that they reflect the “original playing fee”’ of the bass as a musical instrument. There is also sophisticated software with MIDI controller functions, But it requires a device other than a smartphone such as a computer, and besides it tends to be expensive. Thereupon, we analyzed the real feel of playing bass, and designed a smartphone application that enables the user to experience the real thrill of playing by reproducing the tactile sensation of the strings and the experience of plucking strings.
Կառավարման մոդելը Սպիտակի քաղաքային գրադարանում

The article discusses management methods in the library sector, brief characteristics, the main directions of management and development of library work, and modern trends at the Spitak City Library. The article presents the innovative leader program implementation, leadership skills, communication styles, professionalism, knowledge, the ability to use different information means, the stages of developing the innovative ideas and, projects, and making action plans. 137 The purpose of creating a public library as an education supporting institution, content and management methods, and the role in the digital age as a structure that ensures continuous education is also discussed. Additionally, marketing as a modern tool of library management is discussed, including directions for effective imple-mentation of library activities, tools that can expand the range of users and make the library more accessible, and changes that the library needs along with the development of new technologies and social networks.
Survey on Natural Language Interfaces to Databases

In today’s world, data holds utmost importance and value and all industries like health care, education, government, law, finance, etc., require data to perform statistical analysis to make business decisions. To get access to all this data, users need to heavily interact with the database. To extract accurate data, complex SQL queries involving JOINS, nesting has to be written. But not all users are proficient at SQL because of which it takes time to extract and process the correct data efficiently, which leads to wastage of resources. In order to smooth this process, we aim to provide and perform survey on a natural language abstraction layer over the database so that users can focus on the application and not the data. The system would accept a query in multilingual natural language (Way Humans Communicate with each other) through speech or text. This would be then converted to a SQL query which would be fired on the database.
Implementation of Human-Robot Interaction Through Hand Gesture Recognition Algorithms

This article presents the implementation of a human-robot interaction system using hand gesture recognition algorithms. The objective is to improve the living conditions of people with physical and mobility disabilities by developing a mobile robot with advanced interaction capabilities. The system interprets dynamic hand gestures, including a distinct set of gestures involving thumb movements, to control a two-wheel drive mobile robot platform. Based on geometric features extracted from camera images, control points and control area, the system recognizes and classifies the detected gesture. These gestures serve as triggers for motor commands, allowing the robot to execute predefined maneuvers such as moving forward and backward, turning left and right, and stopping. Successful implementation of this system has the potential to significantly improve the independence and autonomy of people with physical and mobility disabilities, thereby increasing their overall quality of life.
A Conceptual Framework of Generalizable Automatic Speaker Recognition System Under Varying Speech Conditions

Aside from the main linguistic information, speech of human is preserving a variety of other types of information. Speech data from each individual contain its particular information that reveal about the speaker due to the wide diversity of vocal tract shape. Although numerous algorithms have been developed to effectively extract this information for speaker recognition applications, extracting speaker-specific information from complicated voice data is a tough task for computers. Discriminative models have traditionally been used to learn voice data. As more computational resources become accessible, generative models are becoming increasingly popular. Such models are independently performing with different types/nature of speech data; however, this triggers model’s generalizability problem. This issue is being addressed in this paper as several databases are deployed for training deep learning algorithms such a Feedforward Neural Network (FFNN), Forward Cascade Backpropagation (FCBP), and Elman Propagation Neural Network (EPNN).
Vehicle Detection in Indian Traffic Using an Anchor-Free Object Detector

Traffic camera video feeds are helpful in implementing intelligent vehicle detection and classification (IVDC). It has various applications in the transportation engineering domain, such as queue length estimation, vehicle tracking, traffic parameters estimation. However, in the Indian traffic, wide variety of vehicles (motorbikes, auto-rickshaws, cycle-rickshaws, minitrucks, trucks, etc.) travel on the road. They do not follow lane disciplined and occluded each other, making vehicle detection very challenging. This work presented an anchor-free object detection model (YOLOX) on the Indian traffic dataset (ITD) and compared it with the existing object detection models. It achieves 88% mean average precision (mAP) and 37 frames per second (FPS) on ITD.
Managing Schema Migration in NoSQL Databases: Advisor Heuristics vs. Self-adaptive Schema Migration Strategies

Schema-flexible NoSQL databases are increasingly popular backends in the agile application development as they allow developers to write code assuming a new database schema that is different from the current one. If the application is in production already, non-functional requirements for application performance and cost efficiency are routinely part of service-level agreements (SLAs). Co-evolving the schema with the application code then requires subtle management decisions regarding the migration of variational legacy data that is persisted in the production database. Eventually, project managers have to deal with the repercussions of schema evolution in order to comply with SLAs, especially if stipulated metrics compete with each other in tradeoffs. To this end, we present a NoSQL Schema Migration Advisor that supports the schema migration management in NoSQL databases in two distinct ways: If the migration situation can be elicited, a heuristic is offered to estimate the impact of schema evolution by means of choosing a migration strategy and pace code releases accordingly. If this information is not sufficiently or not readily available, self-adaptive schema migration strategies are presented that can automatically curate variational data such that competing metrics can be balanced out in order to comply with SLAs, if possible, making management interventions superfluous.
Vehicle detection and classification using three variations of you only look once algorithm

&lt;p&gt;&lt;span&gt;Vehicle detection and classification are essential for advanced driver assistance systems (ADAS) and even traffic camera surveillance. Yet, it is challenging due to complex backgrounds, varying illumination intensities, occlusions, vehicle size, and type variations. This paper aims to apply you only look once (YOLO) since it has been proven to produce high object detection and classification accuracy. There are various versions of YOLO, and their performances differ. An investigation on the detection and classification performance of YOLOv3, YOLOv4, and YOLOv5 has been conducted. The training images were from common objects in context (COCO) and open image, two publicly available datasets. The testing input images were captured on a few highways in two main cities in Malaysia, namely Shah Alam and Kuala Lumpur. These images were captured using a mobile phone camera with different backgrounds during the day and night, representing different illuminations and varying types and sizes of vehicles. The accuracy and speed of detecting and classifying cars, trucks, buses, motorcycles, and bicycles have been evaluated. The experimental results show that YOLOv5 detects vehicles more accurately but slower than its predecessors, namely YOLOv4 and YOLOv3. Future work includes experimenting with newer versions of YOLO.&lt;/span&gt;&lt;/p&gt;
Backdooring Neural Code Search

Reusing off-the-shelf code snippets from online repositories is a common practice, which significantly enhances the productivity of software developers.To find desired code snippets, developers resort to code search engines through natural language queries.Neural code search models are hence behind many such engines.These models are based on deep learning and gain substantial attention due to their impressive performance.However, the security aspect of these models is rarely studied.Particularly, an adversary can inject a backdoor in neural code search models, which return buggy or even vulnerable code with security/privacy issues.This may impact the downstream software (e.g., stock trading systems and autonomous driving) and cause financial loss and/or life-threatening incidents.In this paper, we demonstrate such attacks are feasible and can be quite stealthy.By simply modifying one variable/function name, the attacker can make buggy/vulnerable code rank in the top 11%.Our attack BADCODE features a special trigger generation and injection procedure, making the attack more effective and stealthy.The evaluation is conducted on two neural code search models and the results show our attack outperforms baselines by 60%.Our user study demonstrates that our attack is more stealthy than the baseline by two times based on the F1 score.
Weakly Supervised Vision-and-Language Pre-training with Relative Representations

Weakly supervised vision-and-language pretraining (WVLP), which learns cross-modal representations with limited cross-modal supervision, has been shown to effectively reduce the data cost of pre-training while maintaining decent performance on downstream tasks.However, current WVLP methods use only local descriptions of images, i.e., object tags, as cross-modal anchors to construct weaklyaligned image-text pairs for pre-training.This affects the data quality and thus the effectiveness of pre-training.In this paper, we propose to directly take a small number of aligned image-text pairs as anchors, and represent each unaligned image and text by its similarities to these anchors, i.e., relative representations.We build a WVLP framework based on the relative representations, namely RELIT 1 , which collects high-quality weakly-aligned imagetext pairs from large-scale image-only and text-only data for pre-training through relative representation-based retrieval and generation.Experiments on four downstream tasks show that RELIT achieves new state-of-the-art results under the weakly supervised setting 2 .
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction

The Zero-Shot Learning (ZSL) task pertains to the identification of entities or relations in texts that were not seen during training.ZSL has emerged as a critical research area due to the scarcity of labeled data in specific domains, and its applications have grown significantly in recent years.With the advent of large pretrained language models, several novel methods have been proposed, resulting in substantial improvements in ZSL performance.There is a growing demand, both in the research community and industry, for a comprehensive ZSL framework that facilitates the development and accessibility of the latest methods and pretrained models.In this study, we propose a novel ZSL framework called Zshot that aims to address the aforementioned challenges.Our primary objective is to provide a platform that allows researchers to compare different stateof-the-art ZSL methods with standard benchmark datasets.Additionally, we have designed our framework to support the industry with readily available APIs for production under the standard SpaCy NLP pipeline.Our API is extendible and evaluable, moreover, we include numerous enhancements such as boosting the accuracy with pipeline ensembling and visualization utilities available as a SpaCy extension.https://youtu.be/Mhc1zJXKEJQ
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification

Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would under-represent minor class samples. We investigate this phenomenon and discover that the subspaces of minor classes being squeezed by those of the major ones in the latent space is the main cause of this failure. We are naturally inspired to enlarge the decision boundaries of minor classes and propose a general framework GraphSHA by Synthesizing HArder minor samples. Furthermore, to avoid the enlarged minor boundary violating the subspaces of neighbor classes, we also propose a module called SemiMixup to transmit enlarged boundary information to the interior of the minor classes while blocking information propagation from minor classes to neighbor classes. Empirically, GraphSHA shows its effectiveness in enlarging the decision boundaries of minor classes, as it outperforms various baseline methods in class-imbalanced node classification with different GNN backbone encoders over seven public benchmark datasets. Code is avilable at https://github.com/wenzhilics/GraphSHA.
FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs

Graph Neural Networks (GNNs) have achieved remarkable success in various domains but most of them are developed under the in-distribution assumption. Under out-of-distribution (OOD) settings, they suffer from the distribution shift between the training set and the test set and may not generalize well to the test distribution. Several methods have tried the invariance principle to improve the generalization of GNNs in OOD settings. However, in previous solutions, the graph encoder is immutable after the invariant learning and cannot be adapted to the target distribution flexibly. Confronting the distribution shift, a flexible encoder with refinement to the target distribution can generalize better on the test set than the stable invariant encoder. To remedy these weaknesses, we propose a Flexible invariant Learning framework for Out-Of-Distribution generalization on graphs (FLOOD), which comprises two key components, invariant learning and bootstrapped learning. The invariant learning component constructs multiple environments from graph data augmentation and learns invariant representation under risk extrapolation. Besides, the bootstrapped learning component is devised to be trained in a self-supervised way with a shared graph encoder with the invariant learning part. During the test phase, the shared encoder is flexible to be refined with the bootstrapped learning on the test set. Extensive experiments are conducted for both transductive and inductive node classification tasks. The results demonstrate that FLOOD consistently outperforms other graph OOD generalization methods and effectively improves the generalization ability.
We may finally know how galaxy-scale magnetic fields arise

In a lifetime of tree climbing, Nalini Nadkarni has led the way in discovering the secrets of cloud forest biology. Interview by Matthew Ponsford
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition

Recently, there has been an increasing interest in two-pass streaming end-to-end speech recognition (ASR) that incorporates a 2nd-pass rescoring model on top of the conventional 1stpass streaming ASR model to improve recognition accuracy while keeping latency low.One of the latest 2nd-pass rescoring model, Transformer Rescorer, takes the n-best initial outputs and audio embeddings from the 1st-pass model, and then choose the best output by rescoring the n-best initial outputs.However, training this Transformer Rescorer requires expensive paired audio-text training data because the model uses audio embeddings as input.In this work, we present our Joint Audio/Text training method for Transformer Rescorer, to leverage unpaired text-only data which is relatively cheaper than paired audio-text data.We evaluate Transformer Rescorer with our Joint Audio/Text training on Librispeech dataset as well as our large-scale in-house dataset and show that our training method can improve word error rate (WER) significantly compared to standard Transformer Rescorer without requiring any extra model parameters or latency.
Entropy-Based Vocabulary Substitution for Incremental Learning in Multilingual Neural Machine Translation

In a practical real-world scenario, the longstanding goal is that a universal multilingual translation model can be incrementally updated when new language pairs arrive.Specifically, the initial vocabulary only covers some of the words in new languages, which hurts the translation quality for incremental learning.Although existing approaches attempt to address this issue by replacing the original vocabulary with a rebuilt vocabulary or constructing independent language-specific vocabularies, these methods can not meet the following three demands simultaneously: (1) High translation quality for original and incremental languages, (2) low cost for model training, (3) low time overhead for preprocessing.In this work, we propose an entropy-based vocabulary substitution (EVS) method that just needs to walk through new language pairs for incremental learning in a large-scale multilingual data updating while remaining the size of the vocabulary.Our method has access to learn new knowledge from updated training samples incrementally while keeping high translation quality for original language pairs, alleviating the issue of catastrophic forgetting.Results of experiments show that EVS can achieve better performance and save excess overhead for incremental learning in the multilingual machine translation task.
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training

Training state-of-the-art ASR systems such as RNN-T often have a high associated financial and environmental cost.Training with a subset of training data could mitigate this problem if the subset selected could achieve performance on-par with training with the entire dataset.Although there are many data subset selection (DSS) algorithms, direct application to the RNN-T is difficult, especially the DSS algorithms that are adaptive and use learning dynamics such as gradients, since RNN-T tends to have gradients with a significantly larger memory footprint.In this paper we propose Partitioned Gradient Matching (PGM) a novel distributable DSS algorithm, suitable for massive datasets like those used to train RNN-T.Through extensive experiments on Librispeech 100H and Librispeech 960H, we show that PGM achieves between 3× to 6× speedup with only a very small accuracy degradation (under 1% absolute WER difference).In addition, we demonstrate similar results for PGM even in settings where the training data is corrupted with noise.