Reviewerly/exHarmony huggingface dataset:
Beyond Honest Majority: The Round Complexity of Fair and Robust Multi-party Computation

Two of the most sought-after properties of Multi-party Computation (MPC) protocols are fairness and guaranteed output delivery (GOD), the latter also referred to as robustness. Achieving both, however, brings in the necessary requirement of malicious-minority. In a generalised adversarial setting where the adversary is allowed to corrupt both actively and passively, the necessary bound for a n-party fair or robust protocol turns out to be $$t_a + t_p < n$$, where $$t_a,t_p$$ denote the threshold for active and passive corruption with the latter subsuming the former. Subsuming the malicious-minority as a boundary special case, this setting, denoted as dynamic corruption, opens up a range of possible corruption scenarios for the adversary. While dynamic corruption includes the entire range of thresholds for $$(t_a,t_p)$$ starting from $$(\lceil \frac{n}{2} \rceil - 1, \lfloor n/2 \rfloor )$$ to $$(0,n-1)$$, the boundary corruption restricts the adversary only to the boundary cases of $$(\lceil \frac{n}{2} \rceil - 1, \lfloor n/2 \rfloor )$$ and $$(0,n-1)$$. Notably, both corruption settings empower an adversary to control majority of the parties, yet ensuring the count on active corruption never goes beyond $$\lceil \frac{n}{2} \rceil - 1$$. We target the round complexity of fair and robust MPC tolerating dynamic and boundary adversaries. As it turns out, $$\lceil n/2 \rceil + 1$$ rounds are necessary and sufficient for fair as well as robust MPC tolerating dynamic corruption. The non-constant barrier raised by dynamic corruption can be sailed through for a boundary adversary. The round complexity of 3 and 4 is necessary and sufficient for fair and GOD protocols respectively, with the latter having an exception of allowing 3 round protocols in the presence of a single active corruption. While all our lower bounds assume pair-wise private and broadcast channels and are resilient to the presence of both public (CRS) and private (PKI) setup, our upper bounds are broadcast-only and assume only public setup. The traditional and popular setting of malicious-minority, being restricted compared to both dynamic and boundary setting, requires 3 and 2 rounds in the presence of public and private setup respectively for both fair as well as GOD protocols.
Detection of motion-compensated frame-rate up-conversion via optical flow-based prediction residue

To increase the temporal continuity of low frame-rate videos, Motion-Compensated Frame-Rate Up-Conversion (MC-FRUC), which is a special frame based video editing manipulation, can be employed to synthesize new intermediate frame between two successive frames. However, MC-FRUC can also be utilized to fake high frame-rate videos causing the wastes of storage space or users misleading. Although existing MC-FRUC detectors can obtain desirable performance for uncompressed videos or compressed videos with high perceptual quality, theirs performance degrades when test videos are encoded with higher quantization parameters. To deal with this problem, this paper presents a new optical flow-based detection method. Our method is divided into four steps. First, the forward or backward optical flow map is estimated by optical flow algorithm between the candidate frame, and its previous frame or following frame, respectively. Second, the prediction residue is calculated on the basis of these estimated optical flow maps to eliminate the side effect of texture information in the non-motion regions and the motion regions. Third, Markov statistic features are extracted. Finally, these features are fed into the ensemble classifier for identification of interpolated frames. We test different types of MC-FRUC methods under higher quantization parameters, and the results have shown the effectiveness of the proposed method compared with the existing relative ones.
Social Network Sentiment Analysis and Message Clustering

Till today, classification of documents into negative, neutral, or positive remains a key task within the analysis of text tonality/sentiment. There are several methods for the automatic analysis of text sentiment. The method based on network models, the most linguistically sound, to our viewpoint, allows us take into account the syntagmatic connections of words. Also, it utilizes the assumption that not all words in a text are equivalent; some words have more weight and cast higher impact upon the tonality of the text than others. We see it natural to represent a text as a network for sentiment studies, especially in the case of short texts where grammar structures play a higher role in formation of the text pragmatics and the text cannot be seen as just “a bag of words”. We propose a method of text analysis that combines using a lexical mask and an efficient clustering mechanism. In this case, cluster analysis is one of the main methods of typology which demands obtaining formal rules for calculating the number of clusters. The choice of a set of clusters and the moment of completion of the clustering algorithm depend on each other. We show that cluster analysis of data from an n-dimensional vector space using the “single linkage” method can be considered a discrete random process. Sequences of “minimum distances” define the trajectories of this process. “Approximation-estimating test” allows establishing the Markov moment of the completion of the agglomerative clustering process.
Single Arabic Document Summarization Using Natural Language Processing Technique

This paper presents a method based on natural language processing (NLP) for single Arabic document summarization. The suggested method based on the extractive method to select the most valuable information in the document. However, working with Arabic text is considered as a challenging task, this chapter tries to produce an accurate result by using some of NLP techniques. The proposed method is formed from three phases, the first one work as a pre-processing phase to unify synonyms terms, stemming, remove punctuation marks and remove text decoration. Consequently, it produces the features vectors and scores these features to start to select the clauses with the highest scores then marks it as important clauses. The suggested method's results are compared versus the traditional methods. In this context, two human experts summarized all the datasets manually in order to prepare a strong compare and effective evaluation of the suggested method. In the evaluation phase, some of the performance measures include accuracy, precision, recall, f-measure, and Rouge measure are used. The experimental results denoted that the suggested method showed a competitive execution compared with the human experts in summarization ratio as well as in the accuracy of the produced document.
Comparison of 3D Object Detection Based on LiDAR Point Cloud

3D object detection and scene understanding are the key technologies for autonomous driving scenarios. Due to the differences in configuration and datasets used by each 3D object detection algorithm, it is difficult to evaluate the performance of each method. In this work, we provide a comparison of the advanced 3D object detection networks based on LiDAR point cloud in recent two years and analyze each network structure in detail. For the open-sourced networks, we reproduce them on KITTI dataset benchmark with following their original algorithms. Meanwhile, in order to provide more powerful results, we also utilize nuScenes dataset to retrain the networks as mentioned above. The experimental results show that the performance of the networks with point cloud and images as input is better than that of a single input network.
Surface Normals and Shape From Water

In this paper, we introduce a novel method for reconstructing surface normals and depth of dynamic objects in water. Past shape recovery methods have leveraged various visual cues for estimating shape (e.g., depth) or surface normals. Methods that estimate both compute one from the other. We show that these two geometric surface properties can be simultaneously recovered for each pixel when the object is observed underwater. Our key idea is to leverage multi-wavelength near-infrared light absorption along different underwater light paths in conjunction with surface shading. We derive a principled theory for this surface normals and shape from water method and a practical calibration method for determining its imaging parameters values. By construction, the method can be implemented as a one-shot imaging system. We prototype both an off-line and a video-rate imaging system and demonstrate the effectiveness of the method on a number of real-world static and dynamic objects. The results show that the method can recover intricate surface features that are otherwise inaccessible.
Email Anti-Phishing Detection Application

Phishing is one of many computer security attacks, in which the attacker tries to trick people into fake website that is masqueraded to look and feel like the authentic website, thus prompting the victim to share sensitive information or install hidden malware on their computers. Phishing known as method which stealing personal and financial data that convince people to participate on unwittingly money laundering. Phishing link dispersing are associate with a number of methods, this paper focuses on email message as a means to send phishing website links, which spoof or mimic, banks, credit card and companies or other ecommerce. The purpose of this research is to design and develop a tool to detect the source code of phishing website which attached to email by using decision tree algorithm. In order to improve the protection of users’ information from the fake website. Anti-phishing detection is suggested to overcome the problems through the following features. The first feature of anti-phishing detection login system by using user’s email and password. Second feature detect phishing website which attached on user’s email by using decision tree algorithm. Lastly, on phishing website will be detected and generate a report to the user. The system testing shows high reliability and the users’ feedback shows ease of use as well as learning..
Intention Interleaving Via Classical Replanning

The BDI architecture, where agents are modelled based on their belief, desires, and intentions, provides a practical approach to developing intelligent agents. One of the key features of BDI agents is that they are able to pursue multiple intentions in parallel, i.e. in an interleaved manner. Most of the previous works have enabled BDI agents to avoid negative interactions between intentions to ensure the correct execution. However, to avoid execution inefficiencies, BDI agents should also capitalise on positive interactions between intentions. In this paper, we provide a theoretical framework where first-principles planning (FPP) is employed to manage the intention interleaving in an automated fashion. Our FPP approach not only guarantees the achievability of intentions, but also discovers and exploits potential common sub-intentions to reduce the overall cost of intention execution. Our results show that our approach is both theoretically sound and practically feasible. The effectiveness evaluation in a manufacturing scenario shows that our approach can significantly reduce the total number of actions by merging common sub-intentions, while still accomplishing all intentions.
A Selective Overview and Comparison of Robust Mixture Regression Estimators

Summary Mixture regression models have been widely used in business, marketing and social sciences to model mixed regression relationships arising from a clustered and thus heterogeneous population. The unknown mixture regression parameters are usually estimated by maximum likelihood estimators using the expectation–maximisation algorithm based on the normality assumption of component error density. However, it is well known that the normality‐based maximum likelihood estimation is very sensitive to outliers or heavy‐tailed error distributions. This paper aims to give a selective overview of the recently proposed robust mixture regression methods and compare their performance using simulation studies.
Semi-supervised Graph Embedding for Multi-label Graph Node Classification

The graph convolution network (GCN) is a widely-used facility to realize graph-based semi-supervised learning, which usually integrates node, features, and graph topologic information to build learning models. However, as for multi-label learning tasks, the supervision part of GCN simply minimizes the cross-entropy loss between the last layer outputs and the ground-truth label distribution, which tends to lose some useful information such as label correlations, so that prevents from obtaining high performance. In this paper, we propose a novel GCN-based semi-supervised learning approach for multi-label classification, namely ML-GCN. ML-GCN first uses a GCN to embed the node features and graph topologic information. Then, it randomly generates a label matrix, where each row (i.e., label vector) represents a kind of labels. The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN. That is, all labels and nodes are embedded in a uniform vector space. Finally, during the model training of ML-GCN, label vectors and node vectors are concatenated to serve as the inputs of the relaxed skip-gram model to detect the node-label correlation as well as the label-label correlation. Experimental results on several graph classification datasets show that the proposed ML-GCN outperforms four state-of-the-art methods.
Word Embeddings for Comment Coherence

During the evolution of software, it could happen that the information in the comments and in the associated source code are not aligned, so hampering the execution of software evolution and maintenance tasks. This kind of misalignment is known as lack of coherence and it can happen for several reasons, e.g., programmers modify the intent of source code while executing a maintenance task without updating its comment accordingly. We study the problem of detecting a lack of coherence between comments and source code by exploiting Word Embeddings (WEs). We present four models based on WE and tested these models using six different WE variants through an experiment conducted on a publicly available dataset. Results are compared against a baseline. The most important outcome is: the considered models and WE variants are more efficient in terms of execution time while maintaining performance very close to the baseline. The explanation for such an improvement is that WEs are able to concentrate the important information in a more compact input representation.
Using Human-Agent Teams to Purposefully Design Multi-Agent Systems

With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.
Implementation of a Blockchain-Based Event Reselling System

We propose a blockchain-based reseller system that ensures the ownership of the products, prevents multiple selling, and guarantees the transfer of the ownership. The system employs mutual collateral method to prevent any dishonest behavior by both seller and buyer. This system is built on Ethereum blockchain to maintain the public record of transactions and uses ERC-721 tokens to define the traded goods. Sellers must confirm their identity with a valid MetaMask account and pay a first-time registration fee. The smart contract utilizes the OpenZeppelin library's Ownable. sol contract to implement the transfer of ownership securely. Users interact with the blockchain through a web interface that holds a MySQL database. In this paper, we present the system architecture, database schemas, operating sequence, sample code segments, and testing methods with an example of event ticket reselling. This system can create a secure, transparent, and trusted reselling service.
Interactivity Effects on Single-Handed Interaction

The goal of this study was to examine the effects of interaction techniques (e.g., swiping and tapping) and the range of thumb movements on interactivity, engagement, attitude, and behavioral intention in single-handed interaction by focusing on interactions with mobile devices such as smartphones. This study adopted the perspective of the hybrid definition of interactivity, which includes the interactivity effect outcomes mediated by perceived interactivity. A 2 (technological features: swiping and tapping) × 2 (range of thumb movement: wide and narrow) between-participant experiment was conducted. The results showed the ranges of thumb movement to have significant effects on perceived interactivity, engagement, attitude, and behavioral intention, whereas no effects were observed for interaction techniques. A narrow range of thumb movement had more influence on the interactivity outcomes, rather than a wide range of thumb movement. The implications of the finding were discussed.
Enhanced Urban Clustering in VANETs Using Online Machine Learning

Clustering in Vehicular Ad-Hoc Networks (VANETs) is essential to mitigate different challenges and meet the required quality of communications. However, most of the available clustering protocols were designed for highways, and thus become unstable in realistic urban environments with many intersections. In this paper, a Clustering Adaptation Near Intersection (CANI) approach is proposed to ensure clustering stability at intersections. This approach exploits Online Sequential Extreme Learning Machine (OS-ELM) to predict the behavior of the vehicles near an intersection and adapt the clusters accordingly. The main advantage of the developed OS-ELM prediction model is its ability to continuously learn and update in real time. After being validated, the proposed adaptation approach is included in a highway clustering scheme. The resultant clustering protocol is compared to other schemes in a realistic urban environment, and shows significant stability and efficiency performance improvement.
The Role of Transliteration in the Process of Arabizi Translation/Sentiment Analysis

Arabizi is a form of written Arabic which relies on Latin letters, numerals and punctuation rather than Arabic letters. In literature most of the works are concentrated in the study of Arabic neglecting the study of Arabizi. To conduct automatic translation and sentiment analysis, some approaches tend to handle it like any other language while others use a transliteration phase which converts Arabizi into Arabic script. In this context, the main purpose of this study is to determine the utility of Arabizi transliteration in improving automatic translation and sentiment analysis results. We introduce a rule-based automatic transliteration system. Then we apply this system to transliterate a collection of messages before proceeding to machine translation and sentiment analysis tasks. To evaluate the importance of transliteration on these tasks, we also present the construction of a set of lexical resources, such as: a parallel corpus between Arabizi and Modern Standard Arabic (MSA) constructed manually, a sentiment lexicon built automatically and revised manually, and an annotated sentiment corpus constructed automatically based on the sentiment lexicon. We also apply a set of algorithms and models dedicated to machine translation and sentiment analysis, including a number of shallow and deep classifiers as well as different embedding-based models for feature extraction. The experimental results show a consistent improvement after applying transliteration achieving performance results up to 13.06 for automatic translation using the BLEU score and up to 92% for sentiment classification using the F1-score. This study allows to affirm that transliteration is a key factor in Arabizi handling.
Smart Glove and Hand Gesture-based Control Interface For Multi-rotor Aerial Vehicles

This paper introduces an adaptable human-robot interface that uses two types of human-computer interactions: an image processing technique for a right-hand gesture recognition and a smart glove for left-hand commands. A fixed number of gestures is used for specific commands to the vehicle (takeoff, land, hover, etc.), while the smart glove is used for the vehicle motors control. A single shot multi-box detector (SSD) model is used for a hand detection. After removing the cluttered background, the region of interest (RoI) is fed to a convolutional neural network (CNN) for right-hand gesture recognition. We propose three concurrent validation layers including a human-based validation. The validation layers allow the system to adapt to various users including different skin colors and hand shapes. Four flex sensors and a motion processing unit (MPU) are used in the smart glove to measure the bending ratio of each finger and the roll angle of the left hand. These signals are used for a left-hand gesture recognition as well as generation of continuous control signals such as throttle and angle commands of the vehicle. Extensive experimental results are presented that validate the proposed control methods.
A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as “objects” to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.