Reviewerly/exHarmony huggingface dataset:
Multi-DNN Accelerators for Next-Generation AI Systems

As the use of AI-powered applications widens across multiple domains, so do increase the computational demands. Primary driver of AI technology are the deep neural networks (DNNs). When focusing either on cloud-based systems that serve multiple AI queries from different users each with their own DNN model, or on mobile robots and smartphones employing pipelines of various models or parallel DNNs for the concurrent processing of multi-modal data, the next generation of AI systems will have multi-DNN workloads at their core. Large-scale deployment of AI services and integration across mobile and embedded systems require additional breakthroughs in the computer architecture front, with processors that can maintain high performance as the number of DNNs increases while meeting the quality-of-service requirements, giving rise to the topic of multi-DNN accelerator design.
Sign Language Recognition and Interpretation System

Sign language is a visual language that individuals with speech and hearing difficulties use to communicate in their daily conversations. Through its original grammar, it is totallyan optical communication language, as opposed to vocal languages. This study report proposed an effective strategy to accomplishing the translation of 24 static sign language alphabets and numerals of American Sign Language into humanoid. Researchers are now focusing their efforts on building commercially viable Sign Language Recognition systems. Researchers use a range of techniques to perform their research. It all starts with the methods of data acquisition. The data collection method varies depending on the cost of a suitable device, however for the Sign Language Recognition System to be commercialized, a low-cost strategy is required. The methods used by researchers to develop Sign Language Recognition varied as well. Each approach has its own advantages over others, and researchers are continually experimenting with various methods to produce their own Sign Language Recognition. Each approach has its own set of limitations when compared to others. The purpose of this study is to examine several ways to sign language recognitionand choose the optimal method. Keywords — Feature Extraction and Representation, Artificial Neural Networks, Convolution Neural Network, TensorFlow, Keras, OpenCV.
End‐to‐end waveform level receiver with deep learning

Numerous researches on communication receiver design with deep learning algorithms have been implemented effectively recently. In this paper, an innovative scheme is proposed to construct an end-to-end neural network (NN) receiver to directly recover information bits from unsynchronized waveform sequences. Considering the correlation between samples, multiple bidirectional long-short term memory (BiLSTM) layers to process oversampled signals. Then, shifted binary cross-entropy (SBCE) function is devised to tackle the overfitting issue and eliminate instances with abnormal bit error rate (BER), which originate from standard binary cross-entropy (BCE) loss function. Substantial simulation results demonstrate that the trained NN receiver can achieve theory BER values under excellent conditions for transceivers. Compared with traditional synchronization algorithms, the proposed method can obtain significant BER performance gain under harsh conditions, such as low oversample rate, small roll-off factor, short frame length.
Twist Decoding: Diverse Generators Guide Each Other

Many language generation models are now available for a wide range of generation tasks, including machine translation and summarization. Combining such diverse models may lead to further progress, but ensembling generation models is challenging during inference: conventional ensembling methods (e.g., shallow fusion) require that the models share vocabulary/tokenization schemes. We introduce Twist decoding, a simple and general text generation algorithm that benefits from diverse models at inference time. Our method does not assume the vocabulary, tokenization or even generation order is shared. Our extensive evaluations on machine translation and scientific paper summarization demonstrate that Twist decoding substantially outperforms each model decoded in isolation over various scenarios, including cases where domain-specific and general-purpose models are both available. Twist decoding also consistently outperforms the popular reranking heuristic where output candidates from one model are rescored by another. We hope that our work will encourage researchers and practitioners to examine generation models collectively, not just independently, and to seek out models with complementary strengths to the currently available models. Our code is available at https://github.com/jungokasai/twist_decoding.
KAJIAN PENGARUH JENIS MATERIAL TERHADAP KUALITAS UKURAN PADA PROSES PENEKUKAN PELAT DENGAN METODA ANOVA

Abstract&#x0D; In previous research, it has been proven that the measurement technique, setting and use of quality measuring instruments can increase the quality of measurements during the bending process of steel plates (ST.37). However, this research has not examined the types of materials other than steel, even though other materials apart from ST.37, for example aluminum plates, brass plates and steel plates are widely used, especially in the manufacturing industry.. Therefore, the aim of this research is to analyze the effect of the type of material on the quality of the measurement in the bending process. This study uses an experimental method, combined with a theory that supports the discussion of the problem. The materials used are 3 different types of material plates (aluminum plate, brass plate and steel plate). The parameters observed include the measurement technique, setting and use of measuring instruments. Each material received 10 repetitions of the measurements. These three parameters were analyzed using ANOVA statistics, in order to determine the quality of the measurement of each material and the most dominant factors affecting the quality of the measurements.&#x0D; Keywords: Plates, measurements, bending processes, and statistics.
REAL-TIME MULTIPLE OBJECT DETECTION AND TRACKING

In many computer vision applications, such as surveillance, vehicle navigation, and autonomous robot navigation, object recognition and tracking are critical and difficult problems. One of the flow testing research subjects in PC vision is video observation in a powerful climate, particularly for people and cars. It is a critical innovation in the fight against illegal intimidation, crime, public safety, and effective traffic control. The endeavour entails developing an effective video surveillance system for use in complex contexts. Detecting moving things from a video is critical for object identification and target tracking in video surveillance. The detection of moving objects in video streams is the first significant phase of information, and background subtraction is a common method for foreground segmentation. Due to an increased demand for such systems in public spaces such as airports, subway stations, and mass events, intelligent and automated security surveillance systems have been an important study topic in recent years. In this context, one of the most significant needs for surveillance systems based on the tracking of abandoned, stolen, or parked vehicles is the tracking of fixed foreground regions. Because they work reasonably well when the camera is stationary and the change in ambient lighting is gradual, object tracking-based techniques are the most popular choice for detecting stationary foreground objects. They are also the most popular choice for separating foreground objects from the current frame.
PreQuEL: Quality Estimation of Machine Translation Outputs in Advance

We present the task of PreQuEL, Pre-(Quality-Estimation) Learning. A PreQuEL system predicts how well a given sentence will be translated, without recourse to the actual translation, thus eschewing unnecessary resource allocation when translation quality is bound to be low. PreQuEL can be defined relative to a given MT system (e.g., some industry service) or generally relative to the state-of-the-art. From a theoretical perspective, PreQuEL places the focus on the source text, tracing properties, possibly linguistic features, that make a sentence harder to machine translate. We develop a baseline model for the task and analyze its performance. We also develop a data augmentation method (from parallel corpora), that improves results substantially. We show that this augmentation method can improve the performance of the Quality-Estimation task as well. We investigate the properties of the input text that our model is sensitive to, by testing it on challenge sets and different languages. We conclude that it is aware of syntactic and semantic distinctions, and correlates and even over-emphasizes the importance of standard NLP features.
Enhancing Slot Tagging with Intent Features for Task Oriented Natural Language Understanding using BERT

Recent joint intent detection and slot tagging models have seen improved performance when compared to individual models. In many real-world datasets, the slot labels and values have a strong correlation with their intent labels. In such cases, the intent label information may act as a useful feature to the slot tagging model. In this paper, we examine the effect of leveraging intent label features through 3 techniques in the slot tagging task of joint intent and slot detection models. We evaluate our techniques on benchmark spoken language datasets SNIPS and ATIS, as well as over a large private Bixby dataset and observe an improved slot-tagging performance over state-of-the-art models.
Development of thermal insulated organic &amp; Aluminum foil based hand grips for safely holding cookware &amp; cooking spoons

Metals and their alloys have many unique properties including the property of conduction of heat and electric current.However conductivity of depends upon various factors.In our daily important tasks cooking in the kitchen is most basic & vital task.Since we uses cookwares and cooking accessories made of metals like aluminium and alloy steel and stainless steel which are very good conductors of heat and gives ease in fast cooking.Such thermal conductivity keeps cooking spoons and cooking utensils warm on high and low flame burners due to which we feels uneasiness and discomfort in holding them.Thermal conductivity of items made from alloys and pure metals depends on many factors like porosity,metal content, skin depth, length of metallic conductor, thickness of metallic conductor, permeability, moisture content, operating temperature, pressure, aging & surface velocity and metal composition of alloys.The important fact to remember these things are unchangeable for us.The unique solution of this problem retains in the creation/ development of Thermal insulated organic hand grips and their use in our routine cooking work and practice.
Predicting Safeness of Women in Indian Cities using Machine Learning

As we all know ladies and women area unit annoyed everyplace in each a part of the cities. ladies or lady within the country thinks about with characteristic. though she is worshiped and she or he plays several roles and however, she isn't safe. This base paper chiefly concentrate on the way to promote the characteristic for ladies’ victimization social media platforms to market women safety. Here, we tend to chiefly concentrate on twitter platform. As we all know many ladies raise their problems within the social media platform like twitter that has hash tags wherever it reaches the message to everyone seems to be one in all the most effective platform were women tell their unsafe facing publicly transport, college, work space etc. we tend to predict the characteristic however ladies feel victimization twitter knowledge whether ladies feel unsafe. Key Words: Women, safety, Sentiment analysis.
News Chat: User Based Daily News Content Retrieval System

In Today's world, technology and communication network is getting wide day by day. With time and such fast development of technology, people are changing their ways, they want to stay connected with their peers as well as the world too. knowing what's happening in today's worldwide they want to be up to date. This News Chat app is an android application which allows user to get connected with people through the digital chatting platform from any distance and consisting of Online News browsing feature to get latest updates of environment. The main focus of this News Chat applicationis to connect users to their friends and peers as well as the live news of incidents taking place around the globe.
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs

Message passing neural networks (MPNN) have seen a steep rise in popularity since their introduction as generalizations of convolutional neural networks to graph-structured data, and are now considered state-of-the-art tools for solving a large variety of graph-focused problems. We study the generalization error of MPNNs in graph classification and regression. We assume that graphs of different classes are sampled from different random graph models. We show that, when training a MPNN on a dataset sampled from such a distribution, the generalization gap increases in the complexity of the MPNN, and decreases, not only with respect to the number of training samples, but also with the average number of nodes in the graphs. This shows how a MPNN with high complexity can generalize from a small dataset of graphs, as long as the graphs are large. The generalization bound is derived from a uniform convergence result, that shows that any MPNN, applied on a graph, approximates the MPNN applied on the geometric model that the graph discretizes.
Voxel-informed Language Grounding

Natural language applied to natural 2D images describes a fundamentally 3D world. We present the Voxel-informed Language Grounder (VLG), a language grounding model that leverages 3D geometric information in the form of voxel maps derived from the visual input using a volumetric reconstruction model. We show that VLG significantly improves grounding accuracy on SNARE, an object reference game task. At the time of writing, VLG holds the top place on the SNARE leaderboard, achieving SOTA results with a 2.0% absolute improvement.
Virtual Mouse using Hand Gesture to avoid Infectious Diseases

One of the most astonishing Human-Computer Interaction (HCI) inventions is the mouse. A wireless mouse or a Bluetooth mouse now require devices and are not completely free of gadgets because they rely on a battery for power and a dongle to connect to a PC. This problem can be solved in the proposed AI virtual mouse system by capturing hand motions and recognising hand tips with computer vision utilising a webcam or a built-in camera. In the system's algorithm, the machine learning algorithm is used. Hand movements can be used to control the computer remotely, and it can perform left click, right click, scrolling operations, and computer cursor functions without the need for a hardware mouse. Deep learning is used by the system to detect hands. As a result, by eliminating human influence, the proposed technique will prevent the spread of COVID-19 and other infectious diseases. In this paper, we have discussed the implementation of AI virtual keyboard and mouse system that uses computer vision to perform mouse functions in a computer by using hand gestures and fingertip detection. Keywords —Machine Learning, OpenCV, Media pipe, Hand tracking, Python
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks

Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et al., 2018) to further improve sample efficiency via synthetic sample generation. We build upon this technique with Neighborhood Mixup Experience Replay (NMER), a geometrically-grounded replay buffer that interpolates transitions with their closest neighbors in state-action space. NMER preserves a locally linear approximation of the transition manifold by only applying Mixup between transitions with vicinal state-action features. Under NMER, a given transition's set of state action neighbors is dynamic and episode agnostic, in turn encouraging greater policy generalizability via inter-episode interpolation. We combine our approach with recent off-policy deep reinforcement learning algorithms and evaluate on continuous control environments. We observe that NMER improves sample efficiency by an average 94% (TD3) and 29% (SAC) over baseline replay buffers, enabling agents to effectively recombine previous experiences and learn from limited data.
Perbandingan Algoritma Fisherface dan Algoritma Local Binary Pattern Untuk Pengenalan Wajah

Face recognition system is a system used to detect facial images, which is used to provide accuracy in a system used for access control for facilities that require such a security system. However, it is not uncommon to find problems in the face recognition process, such as the difficulty of the system to recognize faces if similar training data are found. In addition, there are difficulties in the face recognition process due to the lack of the number of images and/or poses of the training data images which results in the system not being optimal in recognizing faces. Several methods were proposed to create a reliable facial recognition system, such as Fisherface and Local Binary Pattern. The advantage of using the Fisherface method is that the processing time is relatively fast. This is because the fisherfaces process uses a matrix process. In addition to the fisherface method, the Local Binary Pattern method is also used in facial recognition systems. This method is also known to be quite easy and efficient in its application to obtain facial image characteristics. With the advantages and disadvantages of each of the above methods, the author will conduct a comparative test between the two methods to measure the level of accuracy of each method. In this study, two algorithms are used, namely the Fisherface algorithm and the Local Binary Pattern algorithm. Where the Fisherface algorithm is a derivative of FLD combined with (PCA). PCA is in charge of reducing input data to simplify and speed up the process and FLD is in charge of producing a distribution matrix to facilitate classification and recognition. In addition to the fisherface method, the Local Binary Pattern algorithm is also used in the face recognition system. LBP is defined as the comparison of the binary value of the pixel at the center of the image with the 8 values ​​of the surrounding pixels so that the test image can be matched with the reference image. This research is useful for the development of facial recognition systems to be precise in the selection of methods, to minimize the occurrence of errors in the face recognition process. As well as getting a high level of accuracy and speed in facial recognition
FDA*: A Focused Single-Query Grid Based Path Planning Algorithm

Square grid representations of the state‐space are a commonly used tool in path planning. With applications in a variety of disciplines, including robotics, computational biology, game development, and beyond. However, in large scale and/or high dimensional environments the creation and manipulation of such structures become too expensive, especially in applications when an accurate representation is needed.In this paper, we present a method for reducing the cost of single‐query grid‐based path planning, by focusing the search to a smaller subset, that containsthe optimal solution. This subset is represented by a hyper‐rectangle, the location, and dimensions of which are calculated departing from an initial feasible path found by a fast search using the RRT* algorithm. We also present an implementation of this focused discretization method called FDA*, a resolution optimal algorithm, where the A* algorithm is employed in searching the resulting graph for an optimal solution. We also demonstrate through simulation results, that the FDA* algorithm uses less memory and has a shorter run‐time compared to classic A* and thus other graph based planning algorithms, and in the same time, the resulting path cost is less than that of regular RRT based algorithms.
INITIATING THE SECURITY MEASURES FOR WOMEN IN TERMS OF MOBILE APPLICATION USING ANDROID OS

People utilising smart phones has expanded rapidly in today's society, and thus a smart phone can be Utilised effectively for personal security or other types of protection.The horrible occurrence that Horrified the entire nation has reawakened us to the importance of addressing safety concerns, and as a Result, a slew of new apps have been developed to equip women with protection systems via their Phones.This Android Application for Women's Safety and Security can be triggered with a simple click if The need arises.A single click on this app recognises the location of a place using GPS and sends a Message to the registered contacts with the location URL, as well as calling the first registered contact to Assist the person in danger.
Deterministic training of generative autoencoders using invertible layers

In this work, we provide a deterministic alternative to the stochastic variational training of generative autoencoders. We refer to these new generative autoencoders as AutoEncoders within Flows (AEF), since the encoder and decoder are defined as affine layers of an overall invertible architecture. This results in a deterministic encoding of the data, as opposed to the stochastic encoding of VAEs. The paper introduces two related families of AEFs. The first family relies on a partition of the ambient space and is trained by exact maximum-likelihood. The second family exploits a deterministic expansion of the ambient space and is trained by maximizing the log-probability in this extended space. This latter case leaves complete freedom in the choice of encoder, decoder and prior architectures, making it a drop-in replacement for the training of existing VAEs and VAE-style models. We show that these AEFs can have strikingly higher performance than architecturally identical VAEs in terms of log-likelihood and sample quality, especially for low dimensional latent spaces. Importantly, we show that AEF samples are substantially sharper than VAE samples.