Reviewerly/exHarmony huggingface dataset:
A Manipulation Prevention Model for Blockchain-Based E-Voting Systems

Security and trust are seen as the most important issues in electronic voting systems. Therefore, it is necessary to use cryptographic procedures to ensure anonymity, security, privacy, and reliability in these systems. In recent years, blockchain has become one of the most commonly used methods for securing data storage and transmission through decentralized applications. E-voting is one of these application areas. However, data manipulation is still seen as a major potential problem in e-voting systems. In theproposed model, administrators or miners are prevented from previewing election results which are normally accessible data due to the blockchain structure. A double-layer encryption model is proposed and tested to prevent manipulations that may occur with the election results. It is ensured that the election results can be counted after the participation of all stakeholders at the end. In this way, potential manipulations may be prevented during the election period. As a result of the model, the privacy of voters is ensured, no central authority is needed, and the recorded votes are kept in a distributed structure.
Layerweaver: Maximizing Resource Utilization of Neural Processing Units via Layer-Wise Scheduling

To meet surging demands for deep learning inference services, many cloud computing vendors employ high-performance specialized accelerators, called neural processing units (NPUs). One important challenge for effective use of NPUs is to achieve high resource utilization over a wide spectrum of deep neural network (DNN) models with diverse arithmetic intensities. There is often an intrinsic mismatch between the compute-to-memory bandwidth ratio of an NPU and the arithmetic intensity of the model it executes, leading to under-utilization of either compute resources or memory bandwidth. Ideally, we want to saturate both compute TOP/s and DRAM bandwidth to achieve high system throughput. Thus, we propose Layerweaver, an inference serving system with a novel multi-model time-multiplexing scheduler for NPUs. Layerweaver reduces the temporal waste of computation resources by interweaving layer execution of multiple different models with opposing characteristics: compute-intensive and memory-intensive. Layerweaver hides the memory time of a memory-intensive model by overlapping it with the relatively long computation time of a compute-intensive model, thereby minimizing the idle time of the computation units waiting for off-chip data transfers. For a two-model serving scenario of batch 1 with 16 different pairs of compute- and memory-intensive models, Layerweaver improves the temporal utilization of computation units and memory channels by 44.0% and 28.7%, respectively, to increase the system throughput by 60.1% on average, over the baseline executing one model at a time.
hNet: Single-shot 3D shape reconstruction using structured light and h-shaped global guidance network

Retrieving three-dimensional (3D) shape information from a single two-dimensional (2D) image has recently gained enormous attention in a variety of fields. In spite of recent advancements in algorithms and hardware developments, the easy-to-use characteristics and the accuracy of the 3D shape reconstruction are always of great interest. This paper presents a robust 3D shape reconstruction technique that integrates structured-light 3D imaging scheme with deep convolutional neural network (CNN) learning. The structured-light patterns facilitate the featuring process while the CNN modeling surpasses the complexity of the traditional 3D shape reconstructions. In the supervised learning pipeline, the input is either a single fringe-pattern or a single speckle-pattern image, and the output is its corresponding high-accuracy 3D shape label. Unlike the well-received autoencoder-based CNN model, a global guidance network path with multi-scale feature fusion is introduced into the CNN model to improve the accuracy of the 3D shape reconstruction. Experimental evaluations have been conducted to demonstrate the validity and robustness of the proposed technique, which provides a promising tool for ever-increasing scientific research and engineering applications.
Are Machine Learning Cloud APIs Used Correctly?

Machine learning (ML) cloud APIs enable developers to easily incorporate learning solutions into software systems. Unfortunately, ML APIs are challenging to use correctly and efficiently, given their unique semantics, data requirements, and accuracy-performance tradeoffs. Much prior work has studied how to develop ML APIs or ML cloud services, but not how open-source applications are using ML APIs. In this paper, we manually studied 360 representative open-source applications that use Google or AWS cloud-based ML APIs, and found 70% of these applications contain API misuses in their latest versions that degrade functional, performance, or economical quality of the software. We have generalized 8 anti-patterns based on our manual study and developed automated checkers that identify hundreds of more applications that contain ML API misuses.
Reliable Vehicle Data Storage Using Blockchain and IPFS

As the importance of vehicle data increases, it has become very important to safely store them. However, because onboard diagnostics scanners generally used to store vehicle data are IoT devices, security and capacity issues exist to store data safely and efficiently. To address this, we propose a system that stores vehicle data safely and efficiently using blockchain and IPFS. Users can access the system through DApp, an Ethereum-distributed application, and manage their vehicle data. Various experiments have been conducted to demonstrate the superior performance of this system, and the experimental results show its advantages in terms of data-processing speed and cost.
GRA_Net: A Deep Learning Model for Classification of Age and Gender From Facial Images

The problem of gender and age identification has been addressed by many researchers, however, the attention given to it compared to the other related problems of face recognition in particular is far less. The success achieved in this domain has not seen much improvement compared to the other face recognition problems. Any language in the world has a separate set of words and grammatical rules when addressing people of different ages. The decision associated with its usage, relies on our ability to demarcate these individual characteristics like gender and age from the facial appearances at one glance. With the rapid usage of Artificial Intelligence (AI) based systems in different fields, we expect that such decision making capability of these systems match as much as to the human capability. To this end, in this work, we have designed a deep learning based model, called GRA_Net (Gated Residual Attention Network), for the prediction of age and gender from the facial images. This is a modified and improved version of Residual Attention Network where we have included the concept of Gate in the architecture. Gender identification is a binary classification problem whereas prediction of age is a regression problem. We have decomposed this regression problem into a combination of classification and regression problems for achieving better accuracy. Experiments have been done on five publicly available standard datasets namely FG-Net, Wikipedia, AFAD, UTKFAce and AdienceDB. Obtained results have proven its effectiveness for both age and gender classification, thus making it a proper candidate for the same against any other state-of-the-art methods.
Fast Multi-view Discrete Clustering with Anchor Graphs

Generally, the existing graph-based multi-view clustering models consists of two steps: (1) graph construction; (2) eigen-decomposition on the graph Laplacian matrix to compute a continuous cluster assignment matrix, followed by a post-processing algorithm to get the discrete one. However, both the graph construction and eigen-decomposition are time-consuming, and the two-stage process may deviate from directly solving the primal problem. To this end, we propose Fast Multi-view Discrete Clustering (FMDC) with anchor graphs, focusing on directly solving the spectral clustering problem with a small time cost. We efficiently generate representative anchors and construct anchor graphs on different views. The discrete cluster assignment matrix is directly obtained by performing clustering on the automatically aggregated graph. FMDC has a linear computational complexity with respect to the data scale, which is a significant improvement compared to the quadratic one. Extensive experiments on benchmark datasets demonstrate its efficiency and effectiveness.
Accommodating Transformer onto FPGA

Recently, Transformers gradually gain popularity and perform outstanding for many Natural Language Processing (NLP) tasks. However, Transformers suffer from heavy computation and memory footprint, making it difficult to deploy on embedded devices. The field-programmable gate array (FPGA) is widely used to accelerate deep learning algorithms for its advantages. However, the trained Transformer models are too large to accommodate to an FPGA fabric. To accommodate Transformer onto FPGA and achieve efficient execution, we propose an acceleration framework coupling the balanced model compression at the algorithm level and FPGA-implementation optimization at the hardware level. At algorithm level, we adopt a block-balanced pruning and propose an efficient sparse matrix storage format for this pruning technique, named Compressed Block Row (CBR). At the hardware level, we design an accelerator for sparse model. And we also abstract a performance analytic model to evaluate the performance of accelerator. Experiments show that our CBR format perform better than general formats and can significantly save storage space. And our accelerator can achieve $38\times$ and $1.93\times$ speedup compared to other works on CPU and GPU respectively.
Probing Image-Language Transformers for Verb Understanding

Multimodal image-language transformers have achieved impressive results on a variety of tasks that rely on fine-tuning (e.g., visual question answering and image retrieval). We are interested in shedding light on the quality of their pretrained representations -- in particular, if these models can distinguish different types of verbs or if they rely solely on nouns in a given sentence. To do so, we collect a dataset of image-sentence pairs (in English) consisting of 421 verbs that are either visual or commonly found in the pretraining data (i.e., the Conceptual Captions dataset). We use this dataset to evaluate pretrained image-language transformers and find that they fail more in situations that require verb understanding compared to other parts of speech. We also investigate what category of verbs are particularly challenging.
Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation

Language-queried video actor segmentation aims to predict the pixel-level mask of the actor which performs the actions described by a natural language query in the target frames. Existing methods adopt 3D CNNs over the video clip as a general encoder to extract a mixed spatio-temporal feature for the target frame. Though 3D convolutions are amenable to recognizing which actor is performing the queried actions, it also inevitably introduces misaligned spatial information from adjacent frames, which confuses features of the target frame and yields inaccurate segmentation. Therefore, we propose a collaborative spatial-temporal encoder-decoder framework which contains a 3D temporal encoder over the video clip to recognize the queried actions, and a 2D spatial encoder over the target frame to accurately segment the queried actors. In the decoder, a Language-Guided Feature Selection (LGFS) module is proposed to flexibly integrate spatial and temporal features from the two encoders. We also propose a Cross-Modal Adaptive Modulation (CMAM) module to dynamically recombine spatial- and temporal-relevant linguistic features for multimodal feature interaction in each stage of the two encoders. Our method achieves new state-of-the-art performance on two popular benchmarks with less computational overhead than previous approaches.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control

Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for scalability requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.
Effective and Robust Physical-World Attacks on Deep Learning Face Recognition Systems

Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems. Recent studies, however, show that DNNs are vulnerable to adversarial examples, which potentially mislead DNN-based FR systems in the physical world. Existing attacks either generate perturbations working merely in the digital world, or rely on customized equipment to generate perturbations that are not robust in the ever-changing physical environment. In this paper, we propose FaceAdv, a physical-world attack that crafts adversarial stickers to deceive FR systems. It mainly consists of a sticker generator and a convertor, where the former can craft several stickers with different shapes while the latter aims to digitally attach stickers to human faces and provide feedback to the generator to improve the effectiveness. We conduct extensive experiments to evaluate the effectiveness of FaceAdv on attacking three typical FR systems (i.e., ArcFace, CosFace and FaceNet). The results show that compared with a state-of-the-art attack, FaceAdv can significantly improve the success rates of both dodging and impersonating attacks. We also conduct comprehensive evaluations to demonstrate the robustness of FaceAdv.
Emoji as a tool to aid the comprehension of written sarcasm: Evidence from younger and older adults

There is evidence for an age-related decline in the ability to understand non-literal language such as sarcasm. There is also evidence to suggest that devices such as emoticons/emojis may influence sarcasm comprehension in younger adults. However, research examining whether such devices may improve written sarcasm comprehension in older adults is scarce. The present study used an online rating task to investigate the influence of the winking face emoji on both the interpretation and perception of message intent for sarcastic or literal criticism or praise. Results revealed that older adults, in comparison to their younger counterparts, demonstrated deficient ability in interpreting and perceiving sarcastic intent. However, older adults’ interpretation and perception of sarcastic intent were significantly improved when the messages were accompanied by the winking face emoji. This would suggest that the winking face emoji is a clear indicator of sarcastic intent, compensating for the absence of non-verbal cues in written communication, and may play a useful role in successful intergenerational communication.
Deep Learning-based Action Detection in Untrimmed Videos: A Survey

Understanding human behavior and activity facilitates advancement of numerous real-world applications, and is critical for video analysis. Despite the progress of action recognition algorithms in trimmed videos, the majority of real-world videos are lengthy and untrimmed with sparse segments of interest. The task of temporal activity detection in untrimmed videos aims to localize the temporal boundary of actions and classify the action categories. Temporal activity detection task has been investigated in full and limited supervision settings depending on the availability of action annotations. This paper provides an extensive overview of deep learning-based algorithms to tackle temporal action detection in untrimmed videos with different supervision levels including fully-supervised, weakly-supervised, unsupervised, self-supervised, and semi-supervised. In addition, this paper reviews advances in spatio-temporal action detection where actions are localized in both temporal and spatial dimensions. Action detection in online setting is also reviewed where the goal is to detect actions in each frame without considering any future context in a live video stream. Moreover, the commonly used action detection benchmark datasets and evaluation metrics are described, and the performance of the state-of-the-art methods are compared. Finally, real-world applications of temporal action detection in untrimmed videos and a set of future directions are discussed.
Ongoing Face Recognition Vendor Test (FRVT) Part 1: Verification

Human face recognition (Computer science); NIST (National Institute of Standards and Technology)
Review and analysis for the Red Deer Algorithm

In this paper, the Red Deer algorithm (RDA), a recent population-based meta-heuristic algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest principle from the evolutionary algorithms and the productivity and richness of heuristic search techniques. Different variants and hybrids of this algorithm are presented and investigated. All the applications that were solved with this algorithm are presented. It is crucial to analyze the performance of this algorithm, therefore, the paper sheds light on the algorithm unique features and weaknesses covering the applications that are primarily suitable for it. The conclusions are presented, and further recommendations are suggested based on the review and analysis covered. The readers of this paper will have an understanding of the RD algorithm and its variants and, consequently, decide how suitable this algorithm is for their own business, research, or industrial applications.
Blockchain for Businesses: A Scoping Review of Suitability Evaluations Frameworks

Blockchain is an emerging technology that is increasingly being applied in both industrial and academic contexts. Cryptocurrency is a major application of blockchain in the financial sector, but the technology is expected to disrupt other industries. In fact, it has influenced many businesses and reshaped private and public sector activities. Therefore, there is growing interest in blockchain-based solutions, and applications have evolved in finance, insurance, logistics, government, education, and healthcare. Applications built on blockchains benefit from fair access, transparency, and immutability; these properties have attracted business owners and practitioners to explore blockchain opportunities beyond cryptocurrency, motivating them to investigate how they can benefit from the technology and also to evaluate its compatibility with their strategic orientation. Suitability evaluation (or applicability evaluation) has always been a crucial step for the successful adaptation of any innovative technology, including blockchain. Hence, this paper investigates how the current literature has addressed blockchain suitability evaluation for business cases beyond cryptocurrency. A scoping review is presented that examines the evaluation models and frameworks that have been developed to assist decision-makers regarding blockchain adoption. The results indicate that blockchain evaluation methodologies have utilized varied approaches and serve diverse objectives, which are applicable for different technology adoption stages. Through this scoping review, blockchain evaluation initiatives are classified into five categories, and a critical analysis is offered of the evaluation models under each category. As such, this scoping review overviews existing methodologies for blockchain evaluation approaches with a focus on context, identified assessments factors, assessment process, and evaluation dimensions.
Teachers’ Perception in Selecting Virtual Learning Platforms: A Case of Mexican Higher Education during the COVID-19 Crisis

In this project, an analysis is made of the perception of teachers of Higher Education Institutions (HEI) regarding the use of Virtual Learning Platforms (VLP) in the transition from the Traditional Educational Model (face-to-face) to Emergency Remote Teaching (ERT). A statistical analysis of teachers’ views on the academic environment caused by the COVID-19 crisis is carried out for the change of educational scenarios from school to home through VLP, the support for teaching–learning knowledge of VLPs and the received training, and the main problems during the transition period. Through convenience sampling, data was collected for a statistical study using a developed instrument (Data collection was designed through the Google Forms application and distributed to public and private HEI teachers). The results of the study showed that more than 60% of respondents had experience using Moodle, Google Classroom, and Blackboard; 80% of teachers had training from their institution for the use of virtual platforms; and in 60% of cases, higher education institutions allowed them to choose the VLP. In addition, the main issues they faced were connectivity, student attitude, and student attendance at class sessions. Fisher’s test was conducted to determine the relationship in the variables analyzed by identifying that there are differences of teachers in perception depending on age.
A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations

In the era of social media, consumers interact and exchange information, subjective opinions, feelings or thoughts about a given issue, a product or a service, via Web 2.0 technologies. As a result, electronic Word of Mouth, eWOM, has gained increasing influence on the formation of user opinions. Therefore, extracting and analyzing eWOM statements by means of Sentiment Analysis techniques is currently a hot topic that could potentially provide companies with key information about their customers. However, among all the available techniques, choosing the most appropriate one according to the analysis criteria is a real challenge. In this contribution, we first study the main desirable criteria for a sentiment analysis approach and classify the different existing approaches based on these criteria to try and provide a solution to this issue. We then propose a Multi-criteria Decision-Making methodology that selects the most suitable technique for each business case by taking their particular criteria into account.