Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of the Permutation Based Algorithm SpoC

In this paper we present an analysis of the SpoC cipher, a second round candidate of the NIST Lightweight Crypto Standardization process. First we present a differential analysis on the sLiSCP-light permutation, a core element of SpoC. Then we propose a series of attacks on both versions of SpoC, namely round-reduced differential tag forgery and message recovery attacks in the related-key, related-nonce scenario, as well as a time-memory trade-off key-recovery attack on the full round version of Spoc-64. Finally, we present an observation regarding the constants used in the sLiSCP-light permutation.
Image Borders Detection Based on the Weight Model Analysis and the Morphological Gradient Operation

The paper describes an approach to the image borders detection that is based on the use of the weight model and the operation of the morphological gradient calculating. The proposed method relates to multiscale image segmentation methods that use the Haar orthogonal wavelet transform. The border detection procedure includes smoothing the original image, building the weight model of the image (weight image), smoothing the weight image, setting the threshold weight value, performing threshold transformation of the weight image to form a binary image, and performing gradient morphological processing of the binary image. The described method allows segmenting the original image to extract information about the borders. This information is used to calculate attributes and their analysis in computer vision systems.
Segmentation Algorithm of the Valid Region in Fisheye Images Using Edge and Region Information

In this paper, we propose a method to segment the valid region of fisheye images. First, we construct an objective function with three terms, which are the region driving term, the edge driving term and the length regularization term. Second, we minimize this objective function by a modified gradient descent method to find the best segmentation result. Our method can achieve valid region segmentation by making use of both region information and edge information. Experiments show that the proposed method can deal with blurred edges, halation noise and incomplete valid region problems.
Fusion Target Attention Mask Generation Network For Video Segmentation

Video segmentation aims to segment target objects in a video sequence, which remains a challenge due to the motion and deformation of objects. In this paper, we propose a novel attention-driven hybrid encoder-decoder network that generates object segmentation by fully leveraging spatial and temporal information. Firstly, a multi-branch network is designed to learn feature representation from object appearance, location and motion. Secondly, a target attention module is proposed to further exploit context information from learned representation. In addition, a novel edge loss is designed which constraints the model to generate salient edge features and accurate segmentation. The proposed model has been evaluated over two widely used public benchmarks, and experiments demonstrate its superior robustness and effectiveness as compared with the state of the arts.
Drone-Based Vehicle Flow Estimation and its Application to Traffic Conflict Hotspot Detection at Intersections

Drones can provide a wider field of view, high mobility and flexibility for monitoring and analyzing traffic flows and safety conditions. In case of a perpendicular viewing angle to the ground, there will be a very less occlusion that can occur and make vehicle tracking be easier. Thus, a drone-based solution will be better for traffic conflict hotspot detection at an interaction. However, due to its observation far from the ground, limited battery time, and bandwidth, this solution should be edge-based and have a good recognition rate in small object detection. However, current edge-based SoTA (state-of-the-art) methods are weak in a small object detection. We propose CoBiF net (Concatenated Bi-Fusion feature pyramid network), a one-stage object detection model for a real-time small object detection, which consists of SPP (spatial pyramid pooling), FE (Feature Extractor), CF (Concatenated Feature) block, and BFM (Bottom-up Fusion Module). CoBiF net is memory-and-bandwidth saving for the most edge devices. Extensive experiments on UA VDT benchmark show the proposed method achieved the SoTA results for the small object detection task in terms of accuracy and efficiency.
Learning of Key Pose Evaluation for Efficient Multi-contact Motion Planner

It is necessary to use not only foot but also hand, knee and other body parts to support body weight for locomotion in uneven terrain. Such multi-contact motion planning is an important research topic including lots of previous works; however, a problem of computational speed of planning is still remaining. In this paper, we propose a learning-based algorithm to speed up the planning. The algorithm reduces replanning of contact states by learning an evaluation function of key pose to reach goal. We investigated the learning performance by comparing three neural network configurations and two activation function. This research aims at achieving robust robotics system in unknown environments.
VLSI Implementation of Color Demosaicing Algorithm for Real-Time Image Applications

Demosaicing algorithm is emerging as the one and only promising technique in digital image processing by reason of providing high-quality image enhancement with low power and low delay. The demosaicing algorithm consists of edge detector, median filter, and image to hexadecimal converter. The gear for this reason should be portable, and the measurement technique need to be quick, successful of imparting take a look at effects within a very short time. The plan is based on VLSI for the hardware implementation using MATLAB and Xilinx software. Compared with previous techniques the power consumption can be linearly reduced by 8% or 90.6%, respectively.
Understanding software changes

In modern software engineering, developers have to work with constantly evolving, interconnected software systems. Understanding how and why these systems and their dependencies between each other change is therefore an essential step in improving or maintaining them. For this, it is important to know what changed and how these changes influence the system. Most currently used tools that help developers to understand source code changes either use the textual representation of source code, allowing for a coarse-grained overview, or use the AST (abstract syntax tree) representation of source code to extract more fine-grained changes. We plan to improve the accuracy and classification of the extracted source code changes and to extend them by analysing the fine-grained changes of source code dependencies. We also propose a dynamical analysis of the impact of the previously extracted changes on performance metrics. This helps to understand what changes caused a certain change in program behaviour. We plan to use and combine this information to generate accurate and detailed change overviews that bridge the gap between existing coarse-grained solutions and the raw changes contained in the code, aiming to reduce the developers' time spent reading changed code and help them to quickly understand the changes between two versions of source code.
Multiobjective Forecasting Model Based on the Interval Discrete Type-2 Fuzzy Sets and Genetic Algorithm

The forecasting task for the short time series has been investigated. The main problem of this task is the availability of a small amount of relevant information. Such tasks can be considered as the tasks in the conditions of uncertainty, fuzziness of the initial data. We suggest to develop the multiobjective forecasting model based on the interval discrete type-2 fuzzy sets. As the criteria which should be minimized, it is proposed to use the average relative forecasting error and the tendency mismatch indicator. The search for the optimal parameters values of the forecasting model is implemented using the genetic algorithm. The examples showing the possibilities of the proposed forecasting model have been discussed.
SAIF: A Correction-Detection Deep-Learning Architecture for Personal Assistants

Intelligent agents that can interact with users using natural language are becoming increasingly common. Sometimes an intelligent agent may not correctly understand a user command or may not perform it properly. In such cases, the user might try a second time by giving the agent another, slightly different command. Giving an agent the ability to detect such user corrections might help it fix its own mistakes and avoid making them in the future. In this work, we consider the problem of automatically detecting user corrections using deep learning. We develop a multimodal architecture called SAIF, which detects such user corrections, taking as inputs the user's voice commands as well as their transcripts. Voice inputs allow SAIF to take advantage of sound cues, such as tone, speed, and word emphasis. In addition to sound cues, our model uses transcripts to determine whether a command is a correction to the previous command. Our model also obtains internal input from the agent, indicating whether the previous command was executed successfully or not. Finally, we release a unique dataset in which users interacted with an intelligent agent assistant, by giving it commands. This dataset includes labels on pairs of consecutive commands, which indicate whether the latter command is in fact a correction of the former command. We show that SAIF outperforms current state-of-the-art methods on this dataset.
A Novel User Authentication Protocol Using Biometric Data for IoT Networks

Internet-based client authentication protocols must be strengthened to reduce attacks and security vulnerabilities that threaten the performance of apps in fast Internet distribution and cloud computing. For secure access to services, IoT safety is thus of critical importance. High safety encryption with dual factors will be provided. This article offers a mobile user authentication with flexible biometric authentication and a core entry management framework for IoT services. The proposed system provides mutual encryption and an integral arrangement that ensures safe access to cloud servers. The legislation preserves anonymous user identity against the eavesdroppers, guarantees the security of the session key, and responds to the challenges.
Mobile Application Development: A Preliminary Study

Websites are no longer at the eye of users nowadays. With smartphone technology advancement, everything can be done without barriers and limitations. Most of the operation of a smartphone is using mobile applications, i.e., calendar, utility, games, sports management, health care monitoring, and many more. Not limit to that, users are also able to use mobile applications to manage their data accordingly. However, due to hectic life, people tend not to manage to grab and go things, especially food. There is no surprise why these food delivery services are high on demand nowadays. These food delivery services operated more than 12 hours daily and at quite extensive coverage areas in the city. To the extent, students are among those who badly need the food delivery service and other relevant services. Motivation of this study is when the students have limitation especially in having their meals and other stuff, i.e. groceries, laundry, printing and more. Due to those limitations, students tend to find alternative, i.e. food delivery services, grocery delivery service and more. Therefore, this study intends to get know the demands of students on the food services and also other services that they looking for. The study adopted quantitative method with convenience sampling technique. From this study, it is clearly shown that the respondents not only preferred to have food delivery service but more, i.e., laundry, printing, binding, and grocery goods. With the findings, this study will be able to expand and proceed to the next phase which designing and developing the services mobile application.
Circuit Techniques for Efficient Implementation of Memristor Based Reservoir Computing

Reservoir computing is a framework of computation designed with the intention of easing the training of recurrent neural networks. Physical implementation of these reservoirs plays a crucial role in enhancing this advantage by improving its processing speed and reducing hardware training costs. In this paper, we present a hardware architecture for efficient and compact implementation of memristor based reservoirs (liquid state machines specifically). The proposed system consists of a reconfigurable architecture such that any given reservoir topology can be implemented on it. It also consists of a memristor crossbar based readout layer that is trained using supervised spike-timing-dependent plasticity (STDP) techniques. The presented techniques are simple and require simple hardware for their implementation and hence reduce area overhead for training-in-hardware of physical reservoir computing systems.
Improving the Performance of Ontological Querying by using a Contextual Approach

In the paper we present the results of experiment we performed to determine whether a contextual approach may be used to increase the performance of querying a knowledge base. For the experiments we have used a unique setting where we put much effort in developing a contextual and a non-contextual ontology which are as much close counterparts as possible. To achieve this we created a contextual version of a non-contextual ontology and reformulated the set of competency questions to reflect the contextual structure of the newly created knowledge base. The results of the experiment strongly suggest that using contexts might be advantageous for improving performance, and also show the further ways of development of the approach.
Early Warning Method of Power Grid Meteorological Disasters Based on Scene Recognition

The current meteorological disaster is one of the initial causes of grid failure. This paper proposes a power grid meteorological disaster forecasting method based on scene classification and recognition for the complex relationship between various meteorological factors and power grids. In this paper, hazard factors are divided into three categories: meteorological factors, equipment factors, and geographical environmental factors. Based on meteorological raw data and power grid historical data, stacked auto-encoder network is used to perform unsupervised feature extraction and supervised fine-tuning to realize the recognition of meteorological scenes, and establish a coupling relationship between factors and power grid faults. The contraction is added to constrain the auto-encoder network, effectively suppress the noise of the input data, and improve the robustness of the auto-encoder network. The calculation analysis shows that the method in this paper can comprehensively and accurately establish the relationship between disasters and power grid faults. It can accurately predict whether the factors will cause grid faults and fault scenarios, and provide more targeted information for the disaster prevention work of the grid.
Why will my question be closed?

Closing a question on a community question answering forum such as Stack Overflow is a highly divisive event. On one hand, moderation is of crucial importance in maintaining the content quality indispensable for the future sustainability of the site. On the other hand, details about the closing reason might frequently appear blurred to the user, which leads to debates and occasional negative behavior in answers or comments. With the aim of helping the users compose good quality questions, we introduce a set of classifiers for the categorization of Stack Overflow posts prior to their actual submission. Our binary classifier is capable of predicting whether a question will be closed after posting with an accuracy of 71.87%. Additionally, in this study we propose the first multiclass classifier to estimate the exact reason of closing a question to an accuracy of 48.55%. Both classifiers are based on Gated Recurrent Units and trained solely on the pre-submission textual information of Stack Overflow posts.
Self-Learning with Stochastic Triplet Loss

Deep learning has offered significant performance improvements on several pattern recognition problems. However, the well-known need for large amounts of labeled data limits applicability and performance where those are not available. Hence, this paper proposes an adaptation of the triplet loss for self-learning with entirely unlabeled data, where there is uncertainty in the generated triplets. The methodology was applied to off-the-person electrocardiogram-based biometric authentication and unconstrained face identity verification tasks, including stress experiments designed to simulate more difficult circumstances. Despite the uncertainty related to the use of unlabeled data, the method was mostly capable of avoiding negatively affecting the model's performance. The promising results show the proposed method can be a viable alternative to supervised learning in cases where only unlabeled data are available. The method is especially suitable for training with continuous stream-based datasets such as on person re-identification in video streams and continuous electrocardiogram-based biometrics.
Stochastic Convolutional Recurrent Networks

Recurrent neural network (RNN) has been widely used for sequential learning which has achieved a great success in different tasks. The temporal convolutional network (TCN), a variant of one-dimensional convolutional neural network (CNN), was also developed for sequential learning in presence of sequence data. RNN and TCN typically captures long-term and short-term features in temporal or spatial domain, respectively. This paper presents a new sequential learning, called the convolutional recurrent network (CRN), which fulfills TCN as an encoder and RNN as a decoder so that the global semantics as well as the local dependencies are simultaneously characterized from sequence data. To facilitate the interpretation and robustness in neural models, we further develop the stochastic modeling for CRN based on variational inference. The merits of CNN and RNN are then incorporated in inference of latent space which sufficiently produces a generative model for sequential prediction. Experiments on language model shows the effectiveness of stochastic CRN when compared with the other sequential machines.
Positioning Participant Engagement in Participatory Design

The approach of Participatory Design (PD) is the direct involvement of people as participants in the process of shaping design artifacts. Thus the facilitation of participant engagement is a priority for designers and researchers within this field. However, there are few reviews of how PD research interprets and employs the concept of engagement. In this paper, we shall first briefly introduce the related concepts. Then provides a systematic literature review of engagement on 81 PD related (PD, co-design, co-creation, etc.) publications, concerning its diverse perspectives, engaged participants, interpretations, and facilitation. We provide the attributes of participant engagement to further define this concept, as well as a guideline for the strategy of facilitating it. In the end, gaps within the literature have been pointed out as the challenges and opportunities for further work.
Areas of Focus for Cloud Security Providers Assessment

Cyber security threats are becoming important in cloud computing environment. To decrease this risk there is need to develop assessment model of cloud service providers that can support the holistic approach to vendor selection. Article deals with the initial phase of this research which is oriented towards the identification of the main areas of focus (components) of this assessment and their mutual relationship.
Multi-criteria analysis involving Pareto-optimal misclassification tradeoffs on imbalanced datasets

On binary classification, the goal of minimizing the false positive and false negative rates creates a conflict, being impossible to optimize both simultaneously. This challenge is even more significant on imbalanced classification datasets since an incorrect choice of the relative relevance of each objective on the optimization can lead to ignoring, or poorly learning the minority class. The proposal of this work takes into account the existing conflict among the learning losses of the classes, and use a deterministic multi-objective optimization method, called MONISE, to create a set of solutions with diverse misclassification tradeoffs among the classes. Since accuracy is no longer a proper criterion for imbalanced datasets, we had to resort to multiple criteria to report the performance: each classifier, proposed or competitors, was selected and reported using the same metrics. We used F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , kappa and g-mean for general evaluation of performance and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</sub> s (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/16</sub> , F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sub> , F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sub> ) to emulate a shifting decision maker preference from precision to recall; all comparisons were made using a Friedman test with Finner posthoc test. However, when we take into account multiple metrics without any prior knowledge, it may become impossible to pinpoint the best method, since the evaluation criteria may also be in conflict. Again, to solve this, we resorted to a Friedman test with a non-dominated ranking. With this multi-criteria analyses, we conclude that explicitly considering multiple objectives on the optimization can guide to promising results.
A Novel Algorithm to Classify Hand Drawn Sketches with Respect to Content Quality

In this paper, the methodology of a novel algorithm called Counting Key-Points algorithm (CKP) is presented. The algorithm can be used during classification of same type of hand drawn sketches, where content quality is important. In brief, the algorithm uses reference pictures set to form vocabulary of key points (with descriptors) and counting how many times those key points appeared on other images, to decide the image content quality. CKP was tested on Draw-a-Person test images, drawn by primary school students, and reached 65% of classification accuracy. The results of the experiment show that the method is applicable and can be improved with further researches. The classification accuracy of CKP was compared to other state-of-art hand drawn image classification methods, to show superiority of the algorithm. As the dataset needs further studies to improve the prediction accuracy, it would be released to the community.
Occlusion-Guided Vertical Retargeting For Stereoscopic Images Based On Pixel Fusion

To implement vertical retargeting for stereoscopic images, this paper proposes an occlusion-guided stereoscopic image retargeting method via the pixel fusion technique. Traditional seam searching-based methods cannot construct valid horizontal seam pairs due to the existence of occluded regions in stereoscopic images and thus fail to implement vertical retargeting. To solve this issue, we propose a novel horizontal seam coupling strategy guided by the occlusion regions that appear on both sides of stereoscopic images. Horizontal seams were able to be laid across the occluded and occluding regions with their geometric consistency maintained. Another important contribution of our method is incorporating occluding masks into energy optimization. The experimental results show that our method can achieve promising performances in both visual experience and depth preservation.