Reviewerly/exHarmony huggingface dataset:
A Novel Model Combining Transformer and Bi-LSTM for News Categorization

News categorization (NC), the aim of which is to identify distinct categories of news through analyzing the contents, has acquired substantial progress since deep learning was introduced into the natural language processing (NLP) field. As a state-of-art model, transformer’s classification performance is not satisfied compared with recurrent neural network (RNN) and convolutional neural network (CNN) if it does not get pretrained. Based on the transformer model, this article proposes a novel framework that combines bidirectional long short-term memory (Bi-LSTM) network and transformer to solve this problem. In the suggested framework, the self-attention mechanism is substituted with Bi-LSTM to capture the semantic information from sentences. Meanwhile, an attention mechanism model is applied to focus on those important words and adjust their weights to solve the problem of long-distance information loss. With pooling network, the network complexity can be reduced and the main features can be highlighted by halving the dimension of the hidden state. Finally, after acquiring the hidden representation by the above structures, we utilize a contraction network to further capture the long-range associations from a text. Experiments on three large-scale corpora were performed to evaluate the suggested framework, and the results demonstrate that our model outperforms other models such as deep pyramid CNN (DPCNN), transformer.
A Refractive Stereo Structured-Light 3-D Measurement System for Immersed Object

Optical 3-D shape measurement is crucial in modern medicine, industry, and other areas, where structured-light-based stereo methods are especially popular for their beneficial characteristics of high precision and robustness. Current stereo systems are usually designed without concerning the influence of the medium between the camera and the target object. Not much research is available for an object immersed in a container such as a glass water tank, where water turbidity and light ray refraction should be considered as the measurement accuracy will be significantly reduced due to stereo matching failures and geometric errors introduced by refraction. To address these problems, in this article, we propose a novel optical 3-D measurement system for an object immersed in a glass water tank. By projecting the gray-code structured light for dense correspondences, we propose a refractive stereo triangulation (RST) method by tracing light rays from the camera to the target object. The method can effectively reduce the geometric errors between the measured points and their true positions. An integrated refractive stereo structured-light (RSSL) system was built for measuring objects immersed in a glass water tank. Experimental results verify the effectiveness of the proposed method and the system was tested to be well working for an immersed object under different turbidity conditions.
Short text automatic scoring system based on BERT-BiLSTM model

Aiming at the problems of sparse features, polysemy of one word and less context related information in short text automatic scoring, a short text automatic scoring model based on bidirectional encoder representations from transformers - bidirectional long short-term memory (BERT-BiLSTM) is proposed. Firstly, the large-scale corpus is pre-trained with bidirectional encoder representations from transformers (BERT) language model to acquire the semantic features of the general language. Then the semantic features of short text and the semantics of keywords in a specific context are acquired through the short text data for the pre-fine tuning downstream specific tasks set pre-fined by BERT. And then the deep-seated context dependency is captured through bidirectional long short-term memory (BiLSTM). Finally, the obtained feature vectors are input into Softmax regression model for automatic scoring. The experimental results show that compared with other benchmark models of convolutional neural networks(CNN), character-level CNN (CharCNN), long short-term memory (LSTM) and BERT, the short text automatic scoring model based on BERT-BiLSTM achieves the best average value of quadratic weighted kappa coefficient.
A Comparison of Opinion Mining Algorithms by Using Product Review Data

After release of Web 2.0 in 2004 user spawned contents on the internet eminently in abundant review sites, online forums, online blogs, and many other sites.Entire user generated contents are considerable bunches of unorganized text written in different languages that encompass user emotions about one or more entities.Mainly predictive analysis exerts the existing data to forecast future outcomes.Currently, a massive amount of researches are being engrossed in the area of opinion mining, also called sentiment analysis, opinion extraction, review analysis, subjective analysis, emotion analysis, and mood extraction.It can be an utmost choice whilst perceiving the meaning and patterns in prevailing data.Most of the time, there are various algorithms available to work with polling.There are contradictory opinions among researchers regarding the effectiveness of algorithms.We have compared different opinion mining algorithms and presented the findings in this paper.
An Empirical Analysis of Compatibility Issues for Industrial Mobile Games (Practical Experience Report)

Detecting and fixing compatibility issues become increasingly important for mobile game development. The constant evolution of mobile operating systems and the severe fragmentation of mobile devices makes it challenging for game developers to detect and fix compatibility issues in time for various device models. The undetected compatibility issues can ruin the user experience, and cause financial loss to game companies and players. Unfortunately, up to the present, mobile game testing is still rather challenging in general. The pressing compatibility issue of mobile games is largely untouched in the research community so far. To bridge the gap, in this experience paper, we perform an empirical study on common compatibility issues of popular commercial mobile games. In particular, we select four active and representative mobile games with well-documented bug reports, containing over seven million lines of code and over 20,000 commits over the past several years. We successfully create a dataset with complete information about bugs and bug fixing details, to investigate the common compatibility issues and fixing strategies. We performed an in-depth manual inspection of the most common symptoms and root causes of these compatibility issues, and analyzed the common fixing strategies of issues under each root cause category. We believe our findings and implications are useful for developers in addressing compatibility hurdles during the developing process. Our results also provide insights for future research on compatibility issue testing and bug fixing for mobile games.
Realtime Cyber Threat Dataset Generator Over PRBS Block Circuit

Realistic dataset is crucial to assist the development of advanced IDS classifier models. However, the available public IDS dataset has long been criticized of its impracticality to render real time cyber threats. Hence tapping into live network would be a relevant and interesting option. However, it is very unlikely any Telco will allow this to happen due to various business and data security reasons. Thus, the ultimate source of dataset to be studied are from any public domain logged traffic datasets. Pseudo Random Binary Sequence (PRBS) is manmade random signal, which may be an alternative candidate to help in identifying threat in traffic dataset. This paper is exploring the possibility of using PRBS in cyber security mitigation. It starts with designing and testing a few electronic circuits 1)a 127bitsPRBS generator, 2) a 127th bit decoder, 3) a 7bits PRBS generator, 4) a multiplexer and 5) a matched filter in the form of a correlator. The circuits are connected to function as a mini simulation laboratory that generate synthetic composite traffic dataset with threat. The simulation is to examine if matched filter and correlation method can help with threat detection in traffic data. As expected for known static datasets it is possible to detect data and threat signals using simple match filtering. What about the real dynamically random datasets that are unknown to us whether threats exist or not. These results suggest that if the local PRBS generator is designed to be adaptive (or put into hunting mode) towards incoming unknown dataset then the same correlation technique should be able to detect and separate actual traffic data from the threat data signals. This is where AI with deep learning stands a chance to make the PRBS block adaptive to the incoming traffic.
A Formal Model of Metacontrol in Maude

Nowadays smart applications appear in domains spanning from commodity household applications to advanced underwater robotics. These smart applications require adaptation to dynamic environments, changing requirements and internal system errors Metacontrol takes a systems of systems view on autonomous control systems and self-adaptation, by means of an additional layer of control that manipulates and combines the regular controllers. This paper develops a formal model of a Metacontrol architecture. We formalise this Metacontrol architecture in the context of an autonomous house heating application, enabling different controllers to be dynamically combined in order to meet user requirements to a better extent than the individual controllers in isolation. The formal model is developed in the Maude rewriting system, where we show results comparing different scenarios.
Big Brother Is Watching You: A Closer Look at Backdoor Construction

With the advent of Malicious (Peyrin and Wang, Crypto’20), the question of a cipher with an intentional weakness which is only known to its designer has gained its momentum. In their work, the authors discuss how an otherwise secure cipher can be broken by its designer with the help of a secret backdoor (which is not known to the user/attacker). The contribution of Malicious is to propose a cipher-level construction with a backdoor, where it is computationally infeasible to retrieve the backdoor entry despite knowing how the mechanism works. In this work, we revisit the work done by Peyrin and Wang in a greater depth. We discuss the relevant aspects with more clarity, thereby addressing some of the important issues connected to a backdoor construction. The main contribution, however, comes as a new proof-of-concept block cipher with an innate backdoor, named ZUGZWANG. Unlike Malicious, which needs new/experimental concepts like partially non-linear layer; our cipher entirely relies on concepts which are well-established for decades (such as, using a one-way function as a Feistel cipher’s state-update), and also offers several advantages over Malicious (easy to visualise, succeeds with probability 1, and so on). Having known the secret backdoor entry, one can recover the secret key with only 1 plaintext query to our cipher; but it is secure otherwise.
Simplified Server-Aided Revocable Identity-Based Encryption from Lattices

As a new revocation mechanism for identity-based encryption (IBE), server-aided revocable IBE (SR-IBE), firstly proposed by Qin et al. in 2015, achieves remarkable advantages over previous identity revocation techniques. In this primitive, almost all of workloads on the users (i.e., receivers) side can be delegated to an untrusted server which does not possess any secret information, and the users can compute short-term decryption keys alone at any time period without having to communicate with either the private key generator (PKG) or server. In 2016, Nguyen et al. creatively presented the first lattice-based SR-IBE by adopting a “double encryption” mechanism to enable smooth interactions between the sender and the server, as well as between the server and the receiver, while ensuring the confidentiality of encrypted messages. In this paper, inspired by recent work on a new treatment of the identity space and the time period space, we simplify the first construction of lattice-based SR-IBE provided by Nguyen et al., and remove some items from the public parameters and the master secret key. In particular, our scheme is more efficient by reducing the workloads of PKG, the server, the sender and the receivers, simultaneously. At the heart of our new design is a tool called “leveled ciphertexts” that enables constant ciphertexts and simplified encryptions, not linear in the length of user identities and without a burdensome double encryption technique, which serves as a more effective solution to the challenge in turning the pairing-based instantiation of SR-IBE into the world of lattice-based cryptography, and based on the hardness of learning with errors (LWE) problem, we prove that our new scheme is selectively secure in the standard model.
Learning Shadow Correspondence for Video Shadow Detection

Video shadow detection aims to generate consistent shadow predictions among video frames. However, the current approaches suffer from inconsistent shadow predictions across frames, especially when the illumination and background textures change in a video. We make an observation that the inconsistent predictions are caused by the shadow feature inconsistency, i.e., the features of the same shadow regions show dissimilar proprieties among the nearby frames. In this paper, we present a novel Shadow-Consistent Correspondence method (SC-Cor) to enhance pixel-wise similarity of the specific shadow regions across frames for video shadow detection. Our proposed SC-Cor has three main advantages. Firstly, without requiring the dense pixel-to-pixel correspondence labels, SC-Cor can learn the pixel-wise correspondence across frames in a weakly-supervised manner. Secondly, SC-Cor considers intra-shadow separability, which is robust to the variant textures and illuminations in videos. Finally, SC-Cor is a plug-and-play module that can be easily integrated into existing shadow detectors with no extra computational cost. We further design a new evaluation metric to evaluate the temporal stability of the video shadow detection results. Experimental results show that SC-Cor outperforms the prior state-of-the-art method, by 6.51% on IoU and 3.35% on the newly introduced temporal stability metric. Code is available at https://github.com/xmed-lab/SC-Cor .
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping

In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised learning. Two mainstream unsupervised learning schemes are the instance-level contrastive framework and clustering-based schemes. The former adopts the extremely fine-grained instance-level discrimination whose supervisory signal is not efficient due to the false negatives. Though the latter solves this, they commonly come with some restrictions affecting the performance. To integrate their advantages, we design the SMoG method. SMoG follows the framework of contrastive learning but replaces the contrastive unit from instance to group, mimicking clustering-based methods. To achieve this, we propose the momentum grouping scheme which synchronously conducts feature grouping with representation learning. In this way, SMoG solves the problem of supervisory signal hysteresis which the clustering-based method usually faces, and reduces the false negatives of instance contrastive methods. We conduct exhaustive experiments to show that SMoG works well on both CNN and Transformer backbones. Results prove that SMoG has surpassed the current SOTA unsupervised representation learning methods. Moreover, its linear evaluation results surpass the performances obtained by vanilla supervised learning and the representation can be well transferred to downstream tasks.
Beyond Uber: Instantiating Generic Groups via PGGs

The generic-group model (GGM) has been very successful in making the analyses of many cryptographic assumptions and protocols tractable. It is, however, well known that the GGM is “uninstantiable,” i.e., there are protocols secure in the GGM that are insecure when using any real-world group. This motivates the study of standard-model notions formalizing that a real-world group in some sense “looks generic.” We introduce a standard-model definition called pseudo-generic group (PGG), where we require exponentiations with base an (initially) unknown group generator to result in random-looking group elements. In essence, our framework delicately lifts the influential notion of Universal Computational Extractors of Bellare, Hoang, and Keelveedhi (BHK, CRYPTO 2013) to a setting where the underlying ideal reference object is a generic group. The definition we obtain simultaneously generalizes the Uber assumption family, as group exponents no longer need to be polynomially induced. At the core of our definitional contribution is a new notion of algebraic unpredictability, which reinterprets the standard Schwartz–Zippel lemma as a restriction on sources. We prove the soundness of our definition in the GGM with auxiliary-input (AI-GGM). Our remaining results focus on applications of PGGs. We first show that PGGs are indeed a generalization of Uber. We then present a number of applications in settings where exponents are not polynomially induced. In particular we prove that simple variants of ElGamal meet several advanced security goals previously achieved only by complex and inefficient schemes. We also show that PGGs imply UCEs for split sources, which in turn are sufficient in several applications. As corollaries of our AI-GGM feasibility, we obtain the security of all these applications in the presence of preprocessing attacks. Some of our implications utilize a novel type of hash function, which we call linear-dependence destroyers (LDDs) and use to convert standard into algebraic unpredictability. We give an LDD for low-degree sources, and establish their plausibility for all sources by showing, via a compression argument, that random functions meet this definition.
Siamese Transformer Network for Real-Time Aerial Object Tracking

Recently, deep learning (DL) based trackers have attracted tremendous interest for their high performance. Despite the remarkable success, most trackers utilizing deep convolution features commonly neglect tracking speed, which is crucial for aerial tracking on mobile devices. In this paper, we propose an efficient and effective transformer based aerial tracker in the framework of Siamese, which inherits the merits from both transformer and Siamese architectures. Specifically, the outputs from multiple convolution layers are fed into transformer to construct robust features of template patch and search patch, respectively. Consequently, the interdependencies between low-level information and semantic information are interactively fused to improve the ability of encoding target appearance. Finally, traditional depth-wise cross correlation is introduced to generate a similarity map for object location and bounding box regression. Extensive experimental results on three popular benchmarks (DTB70, UAV123@10fps, and UAV20L) have demonstrated that our proposed tracker outperforms other 12 state-of-the-art trackers and achieves a real-time tracking speed of 71.3 frames per second (FPS) on GPU, which can be applied in mobile platform.
FusedGCN: A Systolic Three-Matrix Multiplication Architecture for Graph Convolutional Networks

Machine-learning applications have garnered widespread adoption over the last several years. Graph Neural Networks have been proposed as an extension of machine-learning models to graph-structured data. The training and inference tasks on graph neural networks involve graph convolution operations that can be equivalently expressed as three-matrix multiplications. In this work, we propose FusedGCN, a custom systolic architecture that computes in a fused, i.e., combined, manner the product of three matrices. FusedGCN supports compressed sparse representations and tiled computations, which allow the design to adapt to the available input/output bandwidth without losing the regularity of a systolic architecture. The experimental results show that FusedGCN achieves lower execution times than the current best-performing state-of-the-art architecture for computing representative GCN applications. Most importantly, this result is achieved by consuming only marginally more area/power than a traditional systolic array used for two-matrix multiplications.
Comparative Analysis and Visualization of Breast Cancer using Machine Learning Models

In recent decades, humans especially women are now commonly threatened by breast cancer, which has a high morbidity and fatality rate. It is challenging for doctors to develop a consistent treatment solution because there aren't any reliable prognostic models available. Therefore, it takes time to establish a method that produces the least amount of error in order to improve accuracy. To predict the outcome of breast cancer using various datasets, this publication analyses and contrasts four state-of-the-art models: Support Vector Machine (SVM), Logistic Regression, Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (KNN). On the Google Collaborator platform, all experiments are carried out in a simulation setting. The research's objective is divided into three categories. Prediction of cancer before a diagnosis is the first domain, while The second domain focuses on forecasting diagnosis and therapy, while the third domain is concerned with how treatments will turn out. The proposed work can be utilized to forecast the results of various procedures, and based on the need, relevant techniques can be applied. This study compares and assesses how well the models anticipate the future. Future studies can be conducted to forecast the other various criteria, and research on breast cancer can be grouped according to these other parameters.
Comparison of Machine Learning Algorithms for Performance Evaluation of Photovoltaic Energy Forecasting and Management in the TinyML Framework

The availability of reliable photovoltaic (PV) power forecasting tools is an important factor for the dissemination of this technology. This is true not only for the integration of these difficult to predict sources in large power grids but also for small grids or standalone applications. The concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge computing</i> , through the use of small, low power and inexpensive devices can help to make predictions more localized and feasible also in small size applications. In this article prediction methods based on Artificial Neural Networks (ANNs) models are considered and compared, along with the possibility of reducing their cost in terms of memory and computational power requirements possibly without increasing prediction error. It is shown that quantization and pruning methods, implemented in the AI libraries of a common platform for Microcontroller programming, is a viable solution of this problem. Solar panel aging effects are also considered, and it is shown how the same system used for prediction can be an indicator of reduced plant efficiency.
ABE for Circuits with Constant-Size Secret Keys and Adaptive Security

An important theme in the research on attribute-based encryption (ABE) is minimizing the sizes of secret keys and ciphertexts. In this work, we present two new ABE schemes with constant-size secret keys, i.e., the key size is independent of the sizes of policies or attributes and dependent only on the security parameter $$\lambda $$ . Our constructions feature new ways of combining lattices with pairing groups for building ABE and are proven selectively secure based on LWE and in the generic (pairing) group model. We further show that when replacing the LWE assumption with its adaptive variant introduced in [Quach–Wee–Wichs FOCS ’18], the constructions become adaptively secure.