Reviewerly/exHarmony huggingface dataset:
NeuroSpector: Systematic Optimization of Dataflow Scheduling in DNN Accelerators

This paper presents an optimization framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NeuroSpector</i> that systematically analyzes the dataflow of deep neural network (DNN) accelerators and rapidly identifies optimal execution methods. The proposed methodology is demonstrated to work effectively with a variety of accelerator architectures and DNN workloads. It has been a baffling challenge to devise scheduling schemes for neural accelerators to maximize energy efficiency and performance. The challenge lies in that hardware specifications associated with multi-dimensional DNN data create an enormous number of possible scheduling options that can be exerted on accelerators. Related work suggested various techniques to solve the challenge encompassing brute-force search of massive solution spaces pruned by user constraints, solving the objective functions of system models, learning-based optimization, etc. However, each suggested technique was devised only for a specific accelerator model. Therefore, we find that they are not adaptively applicable to different accelerators and DNN workloads in that they produce hit-or-miss results with 100.1% greater energy and cycles on average compared to optimal scheduling schemes obtained from fully comprehensive brute-force searches. In contrast, NeuroSpector identifies efficient execution methods for various accelerators and workloads with only 1.5% differences on average to the optimal scheduling solutions. The optimization strategy of NeuroSpector is based on an observation that optimal executions are strongly correlated with minimizing data movements to the lower-level memory hierarchy of accelerators rather than maximizing the utilization of processing elements. Thus, NeuroSpector prioritizes optimizing lower-level components in the accelerator hierarchy, which is proven highly effective for various accelerators and DNN workloads.
S-ViT: Sparse Vision Transformer for Accurate Face Recognition

Most of the existing face recognition applications using deep learning models have leveraged CNN-based architectures as the feature extractor. However, recent studies have shown that in computer vision tasks, vision transformer-based models often outperform CNN-based models. Therefore, in this work, we propose a Sparse Vision Transformer (S-ViT) based on the Vision Transformer (ViT) architecture to improve the face recognition tasks. After the model is trained, S-ViT tends to have a sparse distribution of weights compared to ViT, so we named it according to these characteristics. Unlike the conventional ViT, our proposed S-ViT adopts image Relative Positional Encoding (iRPE) method for positional encoding. Also, S-ViT has been modified so that all token embeddings, not just class token, participate in the decoding process. Through extensive experiment, we showed that S-ViT achieves better performance in closed-set than the other baseline models, and showed better performance than the baseline ViT-based models. For example, when using ArcFace as the loss function in the identification protocol, S-ViT achieved up to 3.27% higher accuracy than ResNet50. We also show that the use of ArcFace loss functions yields greater performance gains in S-ViT than in baseline models. In addition, S-ViT has an advantage in cost-performance trade-off because it tends to be more robust to the pruning technique than the underlying model, ViT. Therefore, S-ViT offers the additional advantage, which can be applied more flexibly in the target devices with limited resources.
An Efficient Robot with Wireless Control

Recent advances in motion control technology placing various challenges in terms of accuracy & speed of operations. Development of accurate model & algorithm becomes the important aspect in robotics and control operation. It is seen that, 3–4 degrees of freedom robotic arms are the most common in industries and mostly used by medium and large scaled industries. In industries, there is a great need of constantly moving the goods from one place to another in logistics, warehouses, etc. This task is trivial and does not require human decision-making skills. But, employing robotic arms is expensive and requires skilled workers to operate them. Currently most of the Micro, Small & Medium Enterprises use human workers to accomplish this task. A robotic arm can perform this function with negligible amount of human interference. Proposed robotic arm will make sure that the workers can perform other skilful tasks also. With the help of 5 degrees of freedom, it also makes the task of goods manipulation easy and moves around in the facility with the help of its car. It performs the required tasks at different places which attempts to eliminate the need of incorporating static robotic arms. Proposed robotic is controlled with a smartphone and saves the motion of robotic arm as well as the motion of the car which in turn helps in autonomous operation. The developed algorithm has the ability to control the motion of the robot in the indoor designated area.
COSTA: A cost-driven solution for migrating applications in multi-cloud environments

This paper focuses on the runtime replacement of microservice-based applications (MBAs), considering cost aspects. Several existing solutions also focus on replacement issues, but they usually concentrate on performance aspects and do not consider cost in the decision-making process. This paper presents COSTA (COST management and Adaptive system), a solution that monitors public clouds' costs and manages the migration of applications between them, considering the application's budget and execution cost. Several experiments were conducted to assess the solution, looking for scenarios ideal for evaluating a migration among clouds. In the end, COSTA effectively reduced the cost of applications through cloud providers migration.
Towards global neural network abstractions with locally-exact reconstruction

Neural networks are a powerful class of non-linear functions. However, their black-box nature makes it difficult to explain their behaviour and certify their safety. Abstraction techniques address this challenge by transforming the neural network into a simpler, over-approximated function. Unfortunately, existing abstraction techniques are slack, which limits their applicability to small local regions of the input domain. In this paper, we propose Global Interval Neural Network Abstractions with Center-Exact Reconstruction (GINNACER). Our novel abstraction technique produces sound over-approximation bounds over the whole input domain while guaranteeing exact reconstructions for any given local input. Our experiments show that GINNACER is several orders of magnitude tighter than state-of-the-art global abstraction techniques, while being competitive with local ones.
Teaching Students How to Design an Adventure Game with Web3D Technology

In recent years, due to the prosperity of the game industry, many students hope to take game development as their future work, and they have applied for college majors related to game development. The gradually improved discipline construction of game development in various colleges and universities, coupled with the huge impact of the pandemic, renders the teaching having been changing from offline, face-to-face teaching to online, video teaching. How to design an online teaching so that teachers can teach students game development basics is of utmost significance. In this paper, we designed an online teaching scheme, by applying which students can easily and successfully acquire knowledge from finishing a small game project utilizing Web3D technology. In this simple game project for teaching, the characters can be manipulated to avoid the attack of an intelligent NPC enemy located in an indoor scene.
Choice Of Distance Metrics in DBSCAN Based Color Template Matching Applied to Real-Time Human Shoe Detection

In human-robot collaborative environments, human subject detection and tracking is one of the most pertinent problems in recent times. In some of our recent works, we have demonstrated how this problem can be addressed from a vision sensor-based perspective, by utilizing general-purpose template matching algorithms for the purpose. A state-of-the-art such algorithm, namely the FAsT-Match, and its improved variant for RGB color images, termed the CFAsT-Match, can be successfully implemented in real robots for the purposes of visual human shoe detection, during people following. The CFAsT-Match involves the use of a popular density-based clustering algorithm, named DBSCAN, to form irregular-shaped clusters of the template image pixels. In this paper, we have presented a detailed study, where we implement various distance metrics while clustering the template image using the DBSCAN algorithm, and investigate the effects on the final detection outcomes.
CrossChat: Instant Messaging across Different Apps on Mobile Devices

Mobile instant messaging (IM) apps allow people from everywhere to chat with each other using mobile devices, but chat between different mobile IM apps remains a problem. For example, a user who only uses WhatsApp cannot chat with another user who only uses LINE. In this work, we present CrossChat, a system that enables instant messaging across multiple mobile IM apps. Specifically, CrossChat establishes a gateway on a mobile device to connect users running different IM apps and leverages user interface (UI) techniques to capture and exchange messages across multiple mobile IM apps at run time. A main advantage of CrossChat is that no modification to the original apps' software implementation is required to realize cross-app chat. We present several use cases to show the benefits of CrossChat. Experiments were conducted on mainstream IM apps, which show that CrossChat brings slight run-time overhead and can provide a smooth user experience.
Safe Methods for Authenticating Internet-of-Things Devices

The Internet of Things (IoT) allows any connected item to build and take part in a networked experience that brings together the user's physical world with their digital and social lives, the IoT is a phenomenon that is sweeping the globe. The reliability, quality of service, safety, and scalability of an IoT determine its ultimate success. Major repercussions may result from vulnerabilities in Internet of Things applications, so, it's crucial to improve security. If these devices aren't validated before sending data to servers, the number of security flaws grows. Internet of Things security is difficult to design since sensors have limited resources. In IoT's perception layer, authenticating devices is discussed. The system's gateway checks the credentials of perception-layer devices in a centralized network model. In the device registration phase of the protocol, we offer a safe and lightweight mutual authentication technique utilizing Elliptic Curve Diffie-Hellman. This protects device credentials with a reduced key size and reduces device computing costs. After authentication, symmetric key cryptography establishes a key agreement between sensor devices and the gateway. So, devices can safely collect and acquire data without being assaulted. During the protocol's initial phase, ECIES is employed to prevent a "man-in-the- middle" attack, and the results are recorded, we compared how well it worked with existing schemes based on how long it ran and how much it cost to calculate on the device. The comparison shows that the proposed technology is low-cost and secure.
Data Sharing in RoPax Ports: Challenges and Opportunities

Aiming to engage additional sectors of the market, numerous companies adopt data-intensive solutions for their customers. Data ecosystems have emerged across a variety of industrial sectors. By design, they create value exploiting data exchange among participating organizations. Establishing such an ecosystem in the maritime sector is accompanied by various impediments. However, exploiting maritime data to the fullest and optimizing the supply chains might become critical for future generations. We investigate, in this article, data sharing in the maritime domain, focusing on data ecosystems in RoPax ports. These ports accommodate maritime vessels which allow the transport of vehicles, passengers and goods. The choice of maritime domain and of RoPax ports specifically is rooted in their crucial role in global trade, transportation, and environmental sustainability. To understand data sharing in this context, we conducted an exploratory case study based on a Finnish RoPaX port. This study was based on a qualitative case-study approach for data collection and analysis. We identified the main challenges and practical implications of data sharing, along with their benefits in the context of RoPax ports. In addition, we provide a conceptual diagram for data sharing for future maritime data ecosystems.
Cost-effective Data Upkeep in Decentralized Storage Systems

Decentralized storage systems split files into chunks and distribute the chunks across a network of peers. Each peer may only store a few chunks per file. To later reconstruct a file, all its chunks must be downloaded. Chunks can disappear from the network at any time as peers are untrusted and may misbehave, fail or leave the network. Current systems lack a secure and cost-effective mechanism for discovering missing chunks. Hence, a client must periodically re-upload all of the file's chunks to keep it available, even if only a few are missing from the network. Needlessly re-uploading chunks waste significant amounts of the network's bandwidth, takes additional time to complete, and forces the client to pay for unwarranted resources.
Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings

Abstract Purpose Media exaggerations of health research may confuse readers’ understanding, erode public trust in science and medicine, and cause disease mismanagement. This study built artificial intelligence (AI) models to automatically identify and correct news headlines exaggerating obesity-related research findings. Design/methodology/approach We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings. The reasons for exaggerations include: inferring causality from observational studies, inferring human outcomes from animal research, inferring distant/end outcomes (e.g., obesity) from immediate/intermediate outcomes (e.g., calorie intake), and generalizing findings to the population from a subgroup or convenience sample. Each headline was paired with the title and abstract of the peer-reviewed journal publication covered by the news article. We drafted an exaggeration-free counterpart for each original headline and fined-tuned a BERT model to differentiate between them. We further fine-tuned three generative language models—BART, PEGASUS, and T5 to autogenerate exaggeration-free headlines based on a journal publication’s title and abstract. Model performance was evaluated using the ROUGE metrics by comparing model-generated headlines with journal publication titles. Findings The fine-tuned BERT model achieved 92.5% accuracy in differentiating between exaggeration-free and original headlines. Baseline ROUGE scores averaged 0.311 for ROUGE-1, 0.113 for ROUGE-2, 0.253 for ROUGE-L, and 0.253 ROUGE-Lsum. PEGASUS, T5, and BART all outperformed the baseline. The best-performing BART model attained 0.447 for ROUGE-1, 0.221 for ROUGE-2, 0.402 for ROUGE-L, and 0.402 for ROUGE-Lsum. Originality/value This study demonstrated the feasibility of leveraging AI to automatically identify and correct news headlines exaggerating obesity-related research findings.
TCPM: A Reconfigurable and Efficient Toom-Cook-Based Polynomial Multiplier Over Rings Using a Novel Compressed Postprocessing Algorithm

Polynomial multiplication over rings is a significant bottleneck of ring learning with error (RLWE)-based encryption. To speed it up, three algorithms are widely used, i.e., number theoretic transform (NTT), Schoolbook, and Toom-Cook. Compared with Schoolbook and NTT, Toom-Cook can achieve a better trade-off between performance and flexibility. However, in Toom-Cook postprocessing, there are many redundant steps and calculations that have not been eliminated. Therefore, we propose an efficient, compressed, and fused Toom-Cook postprocessing algorithm that reduces the number of steps and at least 33.33% of the arithmetic operations of postprocessing. A highly reconfigurable and efficient Toom-Cook-based polynomial multiplier (TCPM) is proposed to speed up polynomial multiplication over rings. In TCPM, a high-throughput and efficient heterogeneous processing element (PE) array is designed to exploit the parallelism of Toom-Cook, and based on the compressed algorithm, the PE array for postprocessing is scaled down. In addition, as it is provided with a reconfigurable evaluation module, a flexible polynomial data storage module and a universal PE array, TCPM can efficiently map and execute Toom-Cook-2, 3, and 4 on a unified hardware architecture. Implemented on the Xilinx VC709 field-programmable gate array (FPGA) platform, TCPM can perform a Toom-Cook-4-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256\times256$ </tex-math></inline-formula> polynomial multiplication over rings with a modulus of a power of two or a prime every 3.28 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> at a 360-MHz clock frequency. It achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.47\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$50.11\times $ </tex-math></inline-formula> speedup compared with the previous designs.
Modulation Recognition using YOLOv5 on the WBSig53 Dataset

This paper discusses applying a set of YOLOv5 neural networks to the WBSig53 dataset in order to perform modulation recognition. Identifying modulation schemes is a main step in the development of smart receivers. In this effort, attention is payed to the amount of time needed for training as well as inference speed. Signal detection, modulation family classification, and individual modulation scheme recognition are explored on clean and impaired WBSig53 data.
Accurate SMS Spam Detection Using Support Vector Machine in Comparison with Logistic Regression

For performing SMS Spam Detection incorporating Support Vector Machine as well as logistic regression (LR) is done here. Depending on accuracy, analysis for sms spam detection dataset associated with 5573 sentences. Classification of sms spam detection is performed by a support vector machine whose sample takes (N=27) as well as Linear Regression whose sample takes (N=27), G-power generation is performed whose value takes 80%. SVM accuracy is 97.67% whereas LR takes accuracy to be 97.02%. Significant value of accuracy is 0.02 (p<0.05). SVM performs better in tracking down precision when contrasted with Logistic Regression.
Preventing Privacy Leakage in Cloud Service with Weighted Majority Algorithm in Presonal Information

The article discusses the use of the weighted probabilistic C-Means (WPCM) System, a soft clustering technique that is commonly used for cloud-based data analytics. However, it highlights the risk of exposing sensitive information through raw data when transmitting it to the cloud for analysis. Therefore, the authors propose a modification to the WPCM algorithm that utilizes the BGV encryption system to secure the raw data during the clustering process. The BGV encryption system is a homomorphic encryption method that allows computations to be performed on encrypted data without requiring decryption. This is useful because it preserves the privacy of the data and avoids the need to decrypt it, which can be a time-consuming process. To implement the modified algorithm, the authors use polynomial approximations of the weight value calculations, membership matrix updates, and cluster center updates. The polynomial functions only involve addition and multiplication, which makes them computationally efficient. The authors use the Taylor theorem to ensure that the approximations are accurate and do not affect the overall performance of the algorithm. The effectiveness of the proposed technique is evaluated by comparing it with the standard WPCM algorithm on two large datasets (eGSAD and sWSN). The results show that the proposed method outperforms the standard method in terms of effectiveness, efficiency, and scalability. Specifically, the proposed algorithm achieves better clustering accuracy, requires less computation time, and can handle larger datasets compared to the standard algorithm.
A Data-Driven Methodology for Guiding the Selection of Preprocessing Techniques in a Machine Learning Pipeline

The performance of a Machine Learning (ML) model greatly depends on the previous preprocessing of the data. Unfortunately, the decision on which preprocessing techniques should be applied relies on the expertise of data scientists and/or ML practitioners. Since the correct application of some techniques depend on the characteristics of the data whereas others depend on the particular ML model to be trained, this leads to an error-prone process that requires the data scientist to be knowledgeable in all the combinations that may arise. To tackle this problem, we propose a methodology that guides the selection of the most appropriated preprocessing techniques that are highly required or strongly recommended taking into account both the ML model as well as the data characteristics, so that the developer is able to freely experiment with different models while ensuring that no needed preprocessing techniques are overlooked. According to the ML model and the data at hand, the methodology will (i) obtain the characteristics of the model (ii) check whether these characteristics are met by the data or not and (iii) show to the developer which variables require preprocessing and which techniques should be applied so that a proper decision can be made. To the best of our knowledge, this is the only work that tries to gather the most common ML models together with its most adequate preprocessing techniques and encode this information into a methodology that guides this process in a systematic way.
Deriving metrics for software architectures from the "protected entry points" security patterns

Deciding, as early as the software architecture is designed, whether the resulting system will be secure is challenging. We propose three metrics inspired by a security-related design pattern in the structural architecture model, the "protected entry points" pattern. We evaluate these metrics on the real-life Bitwarden web client and server, as well as a synthetic system.
Improving Accuracy of Face Detection in ID Proofs using CNN and Comparing with DLNN

The goal of the proposed work is to upgradethe accuracy rate in face observation with Convolutional Neural Networks algorithm and comparing with Deep Learning Neural Networks. A count aggregate of 40 trails of different angles of id cards were collected along with their passport size photos for detecting the faces. These samples are divided into two groups each having 20 samples and the accuracy values were calculated to face detection in CNN. The G power for this is taken as 0.8. Convolutional Neural Networks achieved the accuracy of 93% and the same for Deep Learning Neural Networks is 96.80% in the recognition of face in ID cards. From this task it is observed that the DLNN algorithm executed significantly better than CNN algorithm in face detection in ID proofs on the basis of accuracy.