Reviewerly/exHarmony huggingface dataset:
High-throughput area-delay-efficient systolic multiplier over GF(2 ) for a class of trinomials

The paradigm of Edge Computing in Internet of Things (IoT) demands high-throughput implementations of IoT edge devices. Security algorithms used in these devices heavily use GF(2m) multiplications. Systolic structures for GF(2m) multipliers can offer high throughput rates along with other features such as regularity, concurrency, and modularity. Many systolic architectures for GF(2m) multiplier were proposed in the literature to achieve low area and time complexities. In this paper, we propose a high-throughput and low area-delay complexity systolic structure for the hardware realization of polynomial basis multiplication in GF(2m) for a class of trinomials. The complexity analysis shows that the proposed architecture results in achieving high throughput and less area-delay complexity compared with the existing polynomial basis multipliers in the literature. When compared with the best available multipliers, the multiplier realized using the proposed architecture achieves throughput improvement by 42% and reduction in area-delay product by 7% for m=409. Application specific integrated circuit (ASIC) implementations of the proposed multiplier and the best available multipliers confirm that the proposed multiplier outperforms the other multipliers. Hence, the proposed high-throughput area-delay-efficient multiplier can be employed in IoT edge devices.
Modeling Cryptographic Protocols Using Computer Algebra Systems

We present the IT solutions for modeling of cryptographic protocols for educational purposes and compare three approaches. The first approach is based on protocol implementation provided by computer algebra system Sage, the second is based on the MPEI algebraic processor, and the third implementation uses combination of Sage and MPEI algebraic processor. Sage provides excellent implementations of some typical algebraic elements used for cryptographic protocol execution as well as examples of their usage. But Sage does not support remote modeling of cryptographic protocol interactions, while MPEI algebraic processor does. The article proposes an integration of these two systems, which will allow us to use Sage for remote execution of cryptographic protocols via the Internet. Moreover, the algebraic processor can provide access to a more detailed study of the Sage. The importance of studying information security in the context of industrial systems and in particular cryptographic primitives by future IT specialists is emphasized.
The Problem of Ensuring Cross-border Personal Data Transfer and Methods for Its Solving

The article considers the problem of cross-border transfer of information containing personal data of students of branches of higher educational institutions in other countries. A review of regulatory documents in the field of regulation of cross-border transfer of personal data is made. Existing methods and software and hardware for transmitting personal data over computer networks are analyzed. A hardware and software for cross-border data transfer between the university and its branches in other countries is proposed. It is pointed out the need to prepare internal university regulations in the field of cross-border data transmission.
Creating and Capturing Artificial Emotions in Autonomous Robots and Software Agents

This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS is able to create and capture artificial emotions during interactions with its environment, and we describe the underlying mechanisms for this. The control system also realizes the capturing of knowledge about its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, represents these artificial emotions. For this, we devise a formalism which enriches the traditional factual knowledge in knowledge graphs with the representation of artificial emotions. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes the user’s orders. The environment of this user assistant consists of autonomous service agents. The execution of user’s orders requires interaction with these autonomous service agents. These interactions lead to artificial emotions within the assistant. The first experiments show that it is possible to realize an autonomous agent with plausible artificial emotions with ARTEMIS and to record these artificial emotions in its Agent Knowledge Graph. In this way, autonomous agents based on ARTEMIS can capture essential knowledge that supports successful planning and decision making in complex dynamic environments and surpass emotionless agents.
Semi-Blind Super-Resolution with Kernel-Guided Feature Modification

Non-blind single image super-resolution(SISR) methods have demonstrated their high efficiency and effectiveness on synthesized images. However, these non-blind methods assume that the blur kernel is predefined or hand-crafted, which degrades the performances on real-world images. To deal with real-world images, we propose a semi-blind super-resolution (SR) method where the blur kernel is unknown. We first design a Kernel Predictor(KP) to predict the blur kernel from the input images with a well-designed kernel set. Then we follow the meta-learning and design the Meta Feature Modification(MFM) module to modify the image features. The weight of the modification layer, which is the core component of the MFM module, is generated with the predicted blur kernel. With the generated weight, the modification layer modifies the image features with the guidance of the predicted kernel. Extensive experiments on synthesized and real-world images show that the proposed method achieves superior performance in the blind SR problem.
Advances in computer–human interaction for detecting facial expression using dual tree multi band wavelet transform and Gaussian mixture model

In human communication, facial expressions play an important role, which carries enough information about human emotions. Last two decades, it becomes a very active research area in pattern recognition and computer vision. In this type of recognition, there is a drawback of how to extract the features because of its dynamic nature of facial structures, which are extracted from the facial images and to predict the level of difficulties in the extraction of the facial expressions. In this research, an efficient approach for emotion or facial expression analysis based on dual-tree M-band wavelet transform (DTMBWT) and Gaussian mixture model (GMM) is presented. Different facial expressions are represented by DTMBWT at various decomposition levels from one to six. From the representations, DTMBWT energy and entropy features are extracted as features for the corresponding facial expression. These features are analyzed for the recognition using GMM classifier by varying the number of Gaussians used. Japanese female facial expression database which contains seven facial expressions; happy, sad, angry, fear, neutral, surprise and disgust are employed for the evaluation. Results show that the framework provides 98.14% accuracy using fourth-level decomposition, which is considerably high.
State Representation Learning For Effective Deep Reinforcement Learning

Recent years have witnessed the great success of deep reinforcement learning (DRL) on a variety of vision games. Although DNN has demonstrated strong power in representation learning, such capacity is under-explored in most DRL works whose focus is usually on optimization solvers. In fact, we discover that the state feature learning is the main obstacle for further improvement of DRL algorithms. To address this issue, we propose a new state representation learning scheme with our Adjacent State Consistency Loss (ASC Loss). The loss is defined based on the hypothesis that there are fewer changes between adjacent states than that of far apart ones, since scenes in videos generally evolve smoothly. In this paper, we exploit ASC loss as an assistant of RL loss in the training phase to boost the state feature learning. We conduct evaluation on Atari games and MuJoCo continuous control tasks, which demonstrates that our method is superior to OpenAI baselines.
Control-Based Design of a DELTA Robot

In the present paper, a control-based design methodology is developed in order to create a DELTA robot with the best accuracy performance for a dedicated controller. The proposed control-based design methodology takes into account the accuracy performance of the controller in the design process to get the optimal geometric parameters of the robot. Three types of controllers are envisaged for the control of the motions of the DELTA robot: leg-direction-based visual servoing, line-based visual servoing and image moment visual servoing. Based on these three controllers, positioning error models taking into account the error of observation coming from the camera are developed. Then, design optimization problems are formulated in order to find the optimal geometric parameters and camera placement for the DELTA robot for each type of controller. Co-simulations of the robots optimized for the three types of controllers are finally performed in order to check the accuracy performance of the three robots.
Deep manifold clustering based optimal pseudo pose representation (DMC-OPPR) for unsupervised person re-identification

Person re-identification (re-ID) is highly complex in a diverse surveillance environment. The existing person re-ID methods are evaluated as a closed set problem with limited environmental variation. It is highly challenging to estimate the diverse poses of a dynamically crowded environment using the traditional unsupervised person re-ID methods. To resolve this issue of handling complex diverse poses and camera angles, a contextual incremental multi-clustering based unsupervised person re-ID method have been proposed. Cam-pose based optimal similarity distance threshold is determined to label the unlabeled person re-ID images efficiently. Frequent intra and inter-camera pseudo pose sequences are represented with optimal distance threshold. This resolves the over-fitting issue created by the dominant samples of an identity and reduces the source-target domain gap. The experimental results show the supremacy of our proposed method over the existing unsupervised person re-ID methods in handling complex poses and camera angles in an incremental self-learning diverse surveillance environment.
Learning to Build Accurate Service Representations and Visualization

With the boom of Web services, there is a growing need for visualizing service ecosystems to help people browse services and understand their functionalities and positions in the systems. One foundational step of building a proper visualization is to ensure accurate representations for the comprising services. However, it is not a trivial task as service profiles may not be sufficient for two significant reasons. First, while the services themselves being used in various scenarios, their profiles may not always precisely reflect all of them. Second, service profiles usually comprise quite a few universal background terms that cannot distinguish services. To address these two issues, we apply machine learning techniques to incrementally learn service representations in a whole. A tailored topic model is developed, named Service Representation-Latent Dirichlet Allocation (SR-LDA). The core idea is to learn more comprehensive and up-to-date information about services from the profiles of the involved service compositions (i.e., mashup profiles), while introducing a global filter to identify and filter out background terms. Both quantitative and qualitative experiments on a real-world dataset demonstrate that the proposed SR-LDA builds higher-quality service representations comparing with baselines. We further generate a knowledge map to visualize a service ecosystem based on the learned service representations. Such a knowledge map directly leads to the detection of four typical functionality patterns of Web services and serves the purpose of mashup creation.
Deep Semantic Adversarial Hashing Based on Autoencoder for Large-Scale Cross-Modal Retrieval

Thanks to the powerful feature learning capabilities of deep learning, some studies have introduced GANs into the cross-modal hashing. However, The GAN-based hashing methods are generally unstable and difficult to train in the process of adversarial learning. To address this problem, we propose a novel AutoEncoder Semantic Adversarial Hashing for cross-modal retrieval (AESAH). Specifically, under the guidance of semantic multi-label, two types of adversarial autoencoder networks (inter-modality and intra-modality) are adopted to maximize the semantic relevance and maintain the invariance of cross-modal. Under semantic supervised, the adversarial modules guide the feature learning process, thus the modal relationship in both the common feature space and the common hamming space is maintained. Furthermore, in order to preserve the inter-modal correlation of all similar item pairs is higher than those of dissimilar ones, we use an inter-modal invariance triplet loss and a classification prediction loss to maintain similarity.Comprehensive experiments were carried out on two commonly used cross-modal datasets, compared with several existing cross-modal retrieval methods, AESAH has better retrieval performance.
Nearly optimal static Las Vegas succinct dictionary

Given a set S of n (distinct) keys from key space [U], each associated with a value from Σ, the static dictionary problem asks to preprocess these (key, value) pairs into a data structure, supporting value-retrieval queries: for any given x∈ [U], valRet(x) must return the value associated with x if x∈ S, or return ⊥ if x∉ S. The special case where |Σ|=1 is called the membership problem. The “textbook” solution is to use a hash table, which occupies linear space and answers each query in constant time. On the other hand, the minimum possible space to encode all (key, value) pairs is only OPT:= ⌈lg2(
Performance Analysis of Security Algorithms

Computers have become more and more potent over the years, and consequently, it has become more comfortable and easier to break encryption and hashing algorithms. Hence, it is essential to study the performance of these algorithms and analyze them, and possibly come up with better algorithms based on the knowledge we have gained from the analysis. In this paper, a comprehensive literature study of the conventional encryption and hashing algorithms is done, followed by a practical comparison of the time-efficiency and CPU usage of these algorithms, two important performance evaluation parameters.
High Dimensional Data Processing in Privacy Preserving Data Mining

In business intelligence data is an essential feature in decision making. An incomplete or lake of information can damage the entire project ideas. Therefore sometimes different business dimensions are collaborating their sensitive and personal data for enhancing decisional ability. During this, the dataset is significantly growing in dimensions. Therefore it is much intense to find a method by which the higher dimensional data can be handled. This paper contributes two key directions of the PPDM (privacy-preserving data mining), first a survey conducted on the various PPDM models to understand the working and requirements of the PPDM systems. In addition to an experimental comparative study among PCA, k-PCA and Correlation coefficient based feature selection or dimensionality reduction is conducted. On the basis of experimental observations, the PCA and k-PCA feature selection techniques are degrading the classification accuracy as compared to correlation coefficient based classification. Therefore, in further system design and implementation, the correlation coefficient is used to handling a huge quantity of data dimensions.
Inspiring radar from quantum-enhanced lidar

The detection of objects under large background conditions is a problem of fundamental interest in sensing. In this talk we theoretically analyze a prototype target detection protocol, the quantum time correlated (QTC) detection protocol, with spontaneous parametric down-converted photon-pair sources. The QTC detection protocol only requires time-resolved photon counting detection, which is phase-insensitive and therefore suitable for optical target detection. As a comparison to the QTC detection protocol we also consider a classical phase-insensitive target detection protocol based on intensity detection. We formulated the target detection problem as a total probe photon transmission estimation problem. We carry out experiments using a semiconductor waveguide source. The experimental results agree very well with the theoretical prediction. In particular, we find that in a high-level environment noise and loss, the QTC detection protocol is able to achieve comparable to the classical protocol target detection performance but with 10–100 fold lower required time on target detection in terms of ROC curve metric. The performance of the QTC detection experiment setup could be further improved with a higher transmission of the reference photon and better detector time uncertainty. Furthermore, unlike classical target detection and ranging protocol, the probe photons in our QTC detection protocol are completely indistinguishable from the background noise and therefore useful for covert ranging applications. Finally, our technological platform is highly scalable and tunable and thus amenable to large scale integration necessary for practical applications.
Classification of Security for System Vulnerabilities

The result of this publication is a presentation of a risk assessment and monitoring tool in computer systems at the network infrastructure, operating systems and applications level. The utility uses data from the Nessus Vulnerability Scanner and a database of publicly known NVD computer vulnerabilities. The main goal was to create a functional software application that, based on input data, would provide user-friendly system risk assessment results that were reproducibly measurable and comparable. The final application was created on the Linux platform, but thanks to the multiplatform programming languages used it is executable on all known platforms.
Embracing Novel ICT Technologies to Support the Journey From Camp to Job

As the numbers of refugees and migrants keep increasing at a fast pace, their social inclusion remains a challenge. The cornerstone of their inclusion is their integration in the labour market which comes with certain hurdles such as the language barrier, the difficulty in assessing and certifying their skills and many more. Although many attempts have led to the development of online platforms, all these address part of the journey from their arrival in a foreign country to their inclusion in the labour market and in the host society. We present a novel platform that aims to provide migrants with a) assistance in discovering and assessing their hard and soft skills by employing AI technologies, b) recommendations of appropriate job sectors and positions based on their profile and c) providing information about multiple practical aspects of their life. The proposed platform additionally delivers information to authorities, companies and NGOs through data analytics and supports them in reaching migrants.
Improved Ada Boost Classifier for Sports Scene Detection in Videos: from Data Extraction to Image Understanding

Improved Ada Boost classifier for the sports scene detection in videos: from data extraction to image understanding is proposed in this paper. Virtual reality technology is the basic condition for the development of VR sports simulation system, and the accurate simulation of all kinds of competitive sports is completed on this basis. This technique can provide technical reference for coaches and athletes, on the basis of which the training means are deepened continuously, while improving the training effect, ensuring that the athletes do not suffer physical injury in the training. Traditional scene recognition methods generally use the low-level features or high-level features. The advantages of these methods are simple and easy to implement, however, this paper uses the Ada Boost model for the efficient analysis. The simulation results have proven the performance.
Research on Optimizing the Recovery Strategy of a Distribution Network Communication System Under Extreme Disasters

Abstract Extreme disasters, such as typhoons, cause great damage to distribution network communication systems, affecting the perception of disaster situations and the repair speed of the distribution networks. To realize the rapid recovery of a distribution network, an optimal recovery strategy of the distribution network communication system is proposed in this paper from the perspectives of predisaster prediction and postdisaster rush repair. First, based on multivariate data, the space-time mesh fusion method is used to predict the fault position of the distribution network communication system, guiding the accurate allocation of emergency repair resources, and the fault position is corrected using man-made statistics after a disaster. Then, combining fiber optics, mobile nets and emergency communication vehicles, rush repair rules for the distribution network communication system after extreme disasters are put forward, and the shortest route search algorithm is used to solve the minimum rush repair time under the rules, which is the optimal rush repair scheme for the distribution network communication system after an extreme disaster. Finally, the proposed strategy is applied to a practical topology, and the results demonstrate that the strategy is reasonable and effective.
Coflow Scheduling with Performance Guarantees for Data Center Applications

Data-parallel applications run on cluster of servers in a datacenter and their communication triggers correlated resource demand on multiple links that can be abstracted as coflow. They often desire predictable network performance, which can be passed to network via coflow abstraction for application-aware network scheduling. In this paper, we propose a heuristic and an optimization algorithm for predictable network performance such that they guarantee coflows completion within their deadlines. The algorithms also ensure high network utilization, i.e., it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms via trace-driven simulation and show that they admit 1.1× more coflows than the Varys scheme while meeting their deadlines.
Path Planning for Multiple Energy Constrained Unmanned Aerial Vehicles to Cover Multiple Regions

In many UAV applications, such as land assessment, pasture management, and search and rescue, UAVs are often required to scan multiple regions. How to plan the paths for the UAVs to ensure mission success and minimize the cost is the first question arises in these applications. In this paper, we investigate this interesting problem and consider the scenario where multiple UAVs with limited energy are assigned to fully cover multiple convex polygonal regions. To solve this problem, we first provide its mathematical formulation and introduce some approximations to simplify the problem. We then propose a modified genetic algorithm to solve the problem efficiently, which is featured by a new chromosome design, new crossover and mutation operators, and a constraint-aware fitness function. Comparative simulation results demonstrate the good performance of the proposed approach.