Reviewerly/exHarmony huggingface dataset:
Robust Vehicle Detection and Counting Algorithm Adapted to Complex Traffic Environments with Sudden Illumination Changes and Shadows

The real-time vehicle detection and counting plays a crucial role in traffic control. To collect traffic information continuously, the access to information from traffic video shows great importance and huge advantages compared with traditional technologies. However, most current algorithms are not adapted to the effects of undesirable environments, such as sudden changes in illumination, vehicle shadows, and complex urban traffic conditions, etc. To address these problems, a new vehicle detection and counting method was proposed in this paper. Based on a real-time background model, the problem of sudden illumination changes could be solved, while the vehicle shadows could be removed using a detection method based on motion. The vehicle counting was built on two types of ROIs-called Normative-Lane and Non-Normative-Lane-which could adapt to the complex urban traffic conditions, especially for non-normative driving. Results have shown that the methodology we proposed is able to count vehicles with 99.93% accuracy under the undesirable environments mentioned above. At the same time, the setting of the Normative-Lane and the Non-Normative-Lane can realize the detection of non-normative driving, and it is of great significance to improve the counting accuracy.
Interval-valued fuzzy reasoning full implication algorithms based on the t-representable t-norm

Interval-valued fuzzy reasoning plays a vital role in intelligent systems with the performance of effectively reducing the loss of fuzzy information and reflecting the vagueness and uncertainty in information processing. However the existing reasoning algorithms were developed based on some special interval-valued t-norms which limits the usability and adaptation of these algorithms. This study proposes general reasoning algorithms on the basis of interval-valued fuzzy sets, that is the interval-valued fuzzy reasoning triple I algorithms based on the left-continuous t-representable t-norm TT1,T2. Furthermore, the interval-valued R-type triple I solutions of the interval-valued fuzzy reasoning triple I algorithms are given. We show that the proposed algorithms possess the reducibility. Finally, some robustness results of the interval-valued fuzzy reasoning triple I algorithms based on the left-continuous interval-valued t-representable t-norm are proved.
Automation of Traffic Control System Using Image Morphological Operations

Now a day’s traffic problems are increasing day by day and transport traffic is becoming a serious problem in the world. Various traffic monitoring systems have been developed. This paper presents the research on controlling the real-time traffic using various image processing techniques in which the pictures are captured using the webcam of various lanes of roads where traffic takes place. The counting of transport vehicles in each picture is computed using image processing techniques in the Matlab tool and the timer is allocated to lane based on a number of vehicles counted in the specific picture of the lane for showing the green signal to pass the vehicle. In this model LED’s are used to show the green and red signals and seven segments are used to show the decrementing timer of signal green.
Bangla Numeral Recognition from Speech Signal Using Convolutional Neural Network

Speech recognition is a process where an acoustic signal is converted to text or words or commands and recognizing the speech. In this paper, a Bangla numeral recognition system from the speech signal is developed utilizing Convolutional Neural Network (CNN). In the proposed system, a speech dataset of ten isolated Bangla digits has been developed consists of 6000 utterances (5 utterances for every 120 speakers) and a feature extraction procedure is performed to elicit significant features from the speech signals using Mel Frequency Cepstrum Coefficient (MFCC) analysis. Then, CNN is trained with the features of the speech signal as input. The efficiency of the proposed system is tested on the dataset developed for this purpose, and acquire 93.65% recognition accuracy. The proposed system is also compared with other existing methods of Bangla numeral speech recognition and outperforms most of the existing systems and proves the superiority of itself.
Emo2Vec

Sentiment analysis or opinion mining for subject information extraction from the text has become more and more dependent on natural language processing, especially for business and healthcare, since the online products and service reviews affect the consuming behaviors. Word embeddings that can map the words to low-dimensional vector representations have been widely used in natural language processing tasks. But the word embeddings based on context such as Word2Vec and GloVe fail to capture the sentiment information. Most of existing sentiment analysis methods incorporate emotional polarity (positive and negative) to improve the sentiment embeddings for the emotion classification. This article takes advantage of an emotional psychology model to learn the emotional embeddings in Chinese first. In order to combine the semantic space and an emotional space, we present two different purifying models from local (LPM) and global (GPM) perspectives based on Plutchik's wheel of emotions to add the emotional information into word vectors. The two models aim to improve the word vectors so that not only the semantically similar words but also the sentimentally similar words can be closer than before. The Plutchik's wheel of emotions model can give eight-dimensional vector for one word in emotional space that can capture more sentiment information than the binary polarity labels. The obvious advantage of the local purifying model is that it can be fit for any pretrained word embeddings. For the global purifying model, we can get the final emotional embeddings at once. These models have been extended to handle English texts. The experimental results on Chinese and English datasets show that our purifying model can improve the conventional word embeddings and some proposed sentiment embeddings for sentiment classification and multi-emotion classification.
Music Recommendation System Based on Facial Emotion Recognition

Face recognition technology has widely attracted attention due to its enormous application value and market potential. It is being implemented in various fields like security system, digital video processing and many such technological advances. Additionally, music is the form of art, which is known to have a greater connection with a person’s emotion. It has got an unique ability to lift up one’s mood. Relatively, this paper focuses on building an efficient music recommendation system which determines the emotion of user using Facial Recognition techniques. The algorithm implemented would prove to be more proficient than the existing systems. Moreover, on a larger dimension, this would render salvage of time and labor invested in performing the process manually. The overall concept of the system is to recognize facial emotion and recommend songs efficiently. The proposed system will be both time and cost efficient.
Verifiable Inner Product Encryption Scheme

In the standard setting of functional encryption (FE), we assume both the Central Authority (CA) and the encryptors to run their respective algorithms faithfully. Badrinarayanan et al. [ASIACRYPT 2016] proposed the concept of verifiable FE, which essentially guarantees that dishonest encryptors and authorities, even when colluding together, are not able to generate ciphertexts and tokens that give “inconsistent” results. They also provide a compiler turning any perfectly correct FE into a verifiable FE, but do not give efficient constructions. In this paper we improve on this situation by considering Inner-Product Encryption (IPE), which is a special case of functional encryption and a primitive that has attracted wide interest from both practitioners and researchers in the last decade. Specifically, we construct the first efficient verifiable IPE (VIPE) scheme according to the inner-product functionality of Katz, Sahai and Waters [EUROCRYPT 2008]. To instantiate the general construction of Badrinarayanan et al. we need to solve several additional challenges. In particular, we construct the first efficient perfectly correct IPE scheme. Our VIPE satisfies unconditional verifiability, whereas its privacy relies on the DLin assumption.
5G and the Future of Security in ICT

This work analyzes the most significant root cause categories of Telecoms security incidents over the recent years, prominent initiatives to mitigate the identified cyber security threats and associated risks, and joint efforts on cyber security standards and certification frameworks, ongoing globally. The benefits of 5G requirements, technologies, new threat landscape, system assets and security control zone, and enhanced solutions to product, deployment and operation, and application security are then presented. Reduction of national dependency on any one supplier -irrespective of its country of origin -and a more competitive, sustainable and diverse Telecoms supply chain, along with proper risk mitigation and transparency measures is the most effective solution to 5G, and beyond, cyber security.
Clifford Gate Optimisation and T Gate Scheduling: Using Queueing Models for Topological Assemblies

Clifford gates play a role in the optimisation of Clifford+T circuits. Reducing the count and the depth of Clifford gates, as well as the optimal scheduling of T gates, influence the hardware and the time costs of executing quantum circuits. This work focuses on circuits protected by the surface quantum error-correcting code. The result of compiling a quantum circuit for the surface code is called a topological assembly. We use queuing theory to model a part of the compiled assemblies, evaluate the models, and make the empiric observation that at least for certain Clifford+T circuits (e.g. adders), the assembly's execution time does not increase when the available hardware is restricted. This is an interesting property, because it shows that T gate scheduling and Clifford gate optimisation have the potential to save both hardware and execution time.
Oblivious Tight Compaction In O(n) Time with Smaller Constant

Oblivious compaction is a crucial building block for hash-based oblivious RAM. Asharov et al. recently gave a O(n) algorithm for oblivious tight compaction [2]. Their algorithm is deterministic and asymptotically optimal, but the implied constant is $${\gg }2^{111}$$ . We give a new algorithm for oblivious tight compaction that runs in time $${<}23913.17n + o(n)$$ . As part of our construction, we give a new result in the bootstrap percolation of random regular graphs.
Improved Generative Adversarial Network-Based Super Resolution Reconstruction for Low-Frequency Measurement of Smart Grid

There is a universal trend toward a data-driven smart grid, which aims to realize two-way communication of energy flow and data flow between various agents across power generation side, transmission& distribution side, electricity retailors and end users.However, the low frequency electrical measurement data accumulated over a long period of time is insignificant for intelligent agents.This paper presents a machine learning method for reconstructing the low frequency electrical measurement data in smart grid.Firstly, the electrical measurement data is converted into electrical images, and then the low frequency electrical measurement data is reconstructed into high frequency electrical measurement data by generative adversarial network to improve the training stability, Wasserstein distance is introduced into the reconstruction mechanism.In addition, by designing the deep residual network based generator, the deep convolutional network based discriminator as well as the perception loss function, the reconstruction accuracy and the high-frequency detail reduction ability are improved.The proposed method is tested on three publicly available datasets and compared with the traditional data reconstruction method, justifying that this method not only can restore high-frequency details with less error, but also can be generalized to different datasets at one location and to datasets at different locations with satisfactory accuracy.
Cloud-based Motion Plan Computation for Power-Constrained Robots

We introduce a method for splitting the computation of a robot’s motion plan between the robot’s low-power embedded computer, and a high-performance cloud-based compute service. To meet the requirements of an interactive and dynamic scenario, robot motion planning may need more computing power than is available on robots designed for reduced weight and power consumption (e.g., battery powered mobile robots). In our method, the robot communicates its configuration, its goals, and the obstacles to the cloud-based service. The cloud-based service takes into account the latency and bandwidth of the connection between it and the robot and computes and returns a motion plan within the time frame necessary for the robot to meet requirements of a dynamic and interactive scenario. The cloud-based service parallelizes construction of a roadmap, and returns a sparse subset of the roadmap giving the robot the ability to adapt to changes between updates from the server. In our results, we show that with typical latency and bandwidth limitations, our method gains significant improvement in the responsiveness and quality of motion plans in interactive scenarios.
Matching Component Analysis for Transfer Learning

We introduce a new Procrustes-type method called matching component analysis to isolate components in data for transfer learning. Our theoretical results describe the sample complexity of this method, and we demonstrate through numerical experiments that our approach is indeed well suited for transfer learning.
An Ensemble Approach for Extractive Text Summarization

Voluminous information spread with an ever-increasing number of articles, links and videos to choose from have made us struggle to make informed decisions quickly. The importance of semantic density has grown considerably. Utilizing machines to help understand and quickly give a pathway to informed decisions has been the emphasis of the computing world for many years now. Automatic text summarization is a common problem where voluminous text needs to be summarized either in an abstractive or an extractive way. So, machine learning algorithms need to be developed that can shorten longer texts automatically and deliver accurate summaries. Many machine learning models have been used by researchers across the globe but results haven’t reached the level of the desired accuracy. This work embodies experimentation based on Logistic Regression, Neural Network, Decision Tree, Random Forest, Naive Bayes, XGBoost, and SVM models, results thus obtained have been compared and finally, an ensemble approach is proposed, which yields better results. Experimentation has been performed using the standard dataset of BBC news. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics is used to validate our research claim which showcased significant improvements utilizing 1-gram, bigrams and longest common subsequence-based statistics.
Linearly-Homomorphic Signatures and Scalable Mix-Nets

Anonymity is a primary ingredient for our digital life. Several tools have been designed to address it such as, for authentication, blind signatures, group signatures or anonymous credentials and, for confidentiality, randomizable encryption or mix-nets. When it comes to complex electronic voting schemes, random shuffling of authenticated ciphertexts with mix-nets is the only known tool. However, it requires huge and complex zero-knowledge proofs to guarantee the actual permutation of the initial ciphertexts in a privacy-preserving way. In this paper, we propose a new approach for proving correct shuffling of signed ElGamal ciphertexts: the mix-servers can simply randomize individual ballots, which means the ciphertexts, the signatures, and the verification keys, with an additional global proof of constant size, and the output will be publicly verifiable. The security proof is in the generic bilinear group model. The computational complexity for the each mix-server is linear in the number of ballots. Verification is also linear in the number of ballots, but independent of the number of rounds of mixing. This leads to a new highly scalable technique. Our construction makes use of linearly-homomorphic signatures, with new features, that are of independent interest.
Annotation and Benchmarking of a Video Dataset under Degraded Complex Atmospheric Conditions and Its Visibility Enhancement Analysis for Moving Object Detection

Detection of moving objects in outdoor environments is an extremely researched topic. However, studies on moving object detection in complex atmospheric/weather conditions are limited, primarily because of the absence of any relevant benchmark dataset. To address this disparity, we introduce a novel benchmark video dataset entitled “Extended Tripura University Video Dataset (E-TUVD)” which is a diverse dataset of complex atmospheric/weather conditions. Currently, E-TUVD is the largest video dataset for moving object detection under degraded atmospheric/weather conditions. The dataset comprises 147 video clips spanning 1-5 minutes in duration of each video clips. Because of the requirement of evaluating any object detection model, this study emphasizes on generation of ground-truth images of salient moving objects on E-TUVD. Using this dataset, we assessed the performance of several state-of-the-art algorithms, considering both the ability to detect moving objects and visibility enhancement under such complex conditions. The method with the best performance was used to investigate the effectiveness of visibility enhancement of atmospheric/weather degraded image sequences for accurate moving object detection. Results and analysis reveal that effective enhancement can significantly improve the ability of detection algorithms under degraded atmospheric/weather conditions to resemble the true properties of moving objects in terms of pixel oriented binary masks.
A Convolutional Neural Network Model to Detect Illegitimate URLs

The internet has become a global means of communication in all aspects of our lives. Using the internet for social activities, financial transactions, e-banking and shopping have become simple and safe to a high extent. Global online retail business is growing dramatically every year. On the other side, illegitimate fraud websites have been growing exponentially due to the increased number of e-businesses and internet users, hence, the plethora of fraud detection systems. Fraudsters always try to trap internet users to collect and steal their sensitive information using tricky techniques. Phishing is one way to trick internet users and direct them to access false Uniform Resource Locators (URLs) in an attempt to steal usernames, passwords and credit cards details. Discovering an intelligent way to determine false websites from true ones is a challenging problem. In this paper, we propose a deep learning convolutional neural network (CNN-1D) model to detect illegitimate URLs. To evaluate the performance of the model, we carried out few experiments using a benchmarked dataset. We used two evaluation measures: accuracy and the area under the receiver operating characteristic (ROC) curve (AUC). The proposed CNN-1D model was able to achieve good performance for predicting the unseen URLs and detecting illegitimate websites. In the testing phase, the classifier achieved an accuracy rate of 94.31% and an overall performance (AUC) rate of 91.23%.
RFP-Net: Receptive field-based proposal generation network for object detection

Recently, object detection has achieved great improvements due to deep CNNs. In this paper, we propose a novel proposal generation network named RFP-Net by mimicking human visual system for high-quality proposals generation. Specifically, RFP-Net takes receptive fields (RFs) as reference boxes to remove many hyper-parameters of anchor boxes that have large sensibility to object detection results. During network training, we select positive samples using an effective RF (eRF) rule instead of the Intersection-over-Union (IoU) rule, which only requires the centroid of a ground truth box to be within the eRF region. This renders RFP-Net learn the representation of region proposals not limited to be of a fixed range of scales and accurately localize the bounding boxes of region proposals around the eRF. RFP-Net also solves the imbalance problem between negative and positive samples with less computational cost. The proposed RFP-Net significantly improves multiply state-of-the-art two-stage and multi-stage detectors. For example, it achieves 43.1% AP by combined it with Cascade RCNN on MS COCO dataset, outperforming previous approaches.
Secure and efficient in-process monitor (and library) protection with Intel MPK

The process reference monitor is a common technique to enforce security policies for application execution. Reference monitors can be used to detect attacks, enforce access control, check program integrity and even transform program state. Deciding where the monitor resides involves a trade-off between strong monitor isolation and low switching overheads. Running the monitor in the same address space as the protected/traced application (in-process monitors) allows for low overhead but raises isolation concerns. Thus, existing work place monitors in a separate address space, which leads to expensive monitor invocation latencies. We present MonGuard, a system in which a high-performance in-process monitor is efficiently isolated from the rest of the application. To that aim, we leverage the Intel Memory Protection Key (MPK) technology to enforce execute-only memory, combined with code randomization to protect and hide the monitor. MonGuard instruments around sensitive instructions to further prevent possible code reuse attacks. The carefully constructed monitor call gate switches the monitor memory permission in a context-sensitive way. We have built a prototype of MonGuard mostly as a loader extension and implemented a multi-variant execution (MVX) monitor. The evaluation shows MonGuard performs faster than the out-of-process monitor approach.
A general approach to define binders using matching logic

We propose a novel definition of binders using matching logic, where the binding behavior of object-level binders is directly inherited from the built-in exists binder of matching logic. We show that the behavior of binders in various logical systems such as lambda-calculus, System F, pi-calculus, pure type systems, can be axiomatically defined in matching logic as notations and logical theories. We show the correctness of our definitions by proving conservative extension theorems, which state that a sequent/judgment is provable in the original system if and only if it is provable in matching logic, in the corresponding theory. Our matching logic definition of binders also yields models to all binders, which are deductively complete with respect to formal reasoning in the original systems. For lambda-calculus, we further show that the yielded models are representationally complete, a desired property that is not enjoyed by many existing lambda-calculus semantics. This work is part of a larger effort to develop a logical foundation for the programming language semantics framework K (http://kframework.org).