Reviewerly/exHarmony huggingface dataset:
User Preferences Elicitation in Bilateral Automated Negotiation Using Recursive Least Square Estimation

The negotiating agents are trying to reach a quality agreement during the process of automated negotiation. While each agent tries to improve its own utility, the agreement yields when the opponent reach in an acceptable utility as well. Therefore, learning the opponent's preference during the negotiation is a challenging area of research. The opponent preferences modeled by two parameter vectors: the importance of negotiation issues, and the scoring value of each negotiation issue. In this study, the opponent model is updated by using an incremental recursive least square estimator. As time passes, the estimator reaches calculates the more accurate outcomes. By examining different negotiation domains, the computational experiments show the proposed method outperforms the recent studies.
Toward a carnivalesque analysis of hacking: A qualitative study of Iranian hackers

This study sought to analyze Iranian hackers' lived experiences based on the concept of the carnival of crime in cultural criminology. Fifty-four in-depth semi-structured interviews were conducted with young hackers. Moreover, their behaviors were observed along with those of other hackers. The data were coded through a thematic analysis using MAXQDA software. The study's findings indicated that cyberspace had created a carnivalesque utopia that allowed the young Iranian hackers to experience the pleasure of crime. By trespassing the rational frameworks of the modern world and altering the trends of knowledge creation as a means for subjugation, Iranian hackers contribute to the meaning-construction policies of those in power and challenge authoritarian aesthetics by invading their electronic domain. It was also found that the carnivalesque lives of hackers provided them with a variety of opportunities for disrupting the rhythms of production and creating an underground carnivalesque economy based on their level of knowledge.
The role and importance of teachers in the distance learning system

Distance learning is a planned learning that does not require the physical presence of students and teachers in one place, which means that they are spatially distant from each other. In distance learning, which is organized for both students and adults, the teacher is an indispensable participant. Teachers are expected to follow the innovations and better themselves in accordance with current changes in order to educate students to live in the current moment. A teacher who teaches at a distance should not only have educational, communicative, digital, methodical, and organizational skills but also high digital competence. Teachers` digital competencies are necessary for using modern educational technologies and distance learning. The digital competence enables teachers to work with modern information and communication technologies in order to achieve their goals in the context of their work. It is of great importance that teachers have the ability to search for, gather, and critically analyse information, but also, skills to use multimedia information, the ability to access, search, and use the internet services needed for continuous professional development. The development of teachers` digital competencies today is necessary for the application of digital technologies in the educational process. The paper will discuss distance learning, educating teachers for distance learning as well as developing teachers` digital competencies. The aim of this work is to draw teachers` attention and enable them to realize specific characteristics of distance learning and to gain knowledge in the field of modern educational technologies, to keep improving permanently so as to apply the acquired knowledge in distance learning and therefore improve the educational process itself.
On the Invertibility of a Voice Privacy System Using Embedding Alignment

This paper explores various attack scenarios on a voice anonymization system using embeddings alignment techniques. We use Wasserstein-Procrustes (an algorithm initially designed for unsupervised translation) or Procrustes analysis to match two sets of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> -vectors, before and after voice anonymization, to mimic this transformation as a rotation function. We compute the optimal rotation and compare the results of this approximation to the official Voice Privacy Challenge results. We show that a complex system like the baseline of the Voice Privacy Challenge can be approximated by a rotation, estimated using a limited set of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> -vectors. This paper studies the space of solutions for voice anonymization within the specific scope of rotations. Rotations being reversible, the proposed method can recover up to 62% of the speaker identities from anonymized embeddings.
BSR-TC: Adaptively Sampling for Accurate Triangle Counting over Evolving Graph Streams

Triangle counting is a fundamental graph mining problem, widely employed in various real-world application scenarios. Given the large scale of graph streams and limited memory space, it is feasible to achieve the estimation of global and local triangles by sampling. Existing streaming algorithms for triangle counting can be generalized into two categories: Reservoir-based methods and Bernoulli-based methods. The former use a fixed memory budget, whose size is difficult to set for accurate estimation without any prior knowledge about graph streams. The latter sample edges by a specified probability, but memory budget is uncontrollable for following a binomial distribution. In this work, we propose a novel and bounded-sampling-ratio algorithm for both global and local triangle counting, called BSR-TC, by adaptively resizing memory budget upwards over evolving graph streams. Specifically, our proposed single-pass BSR-TC can gain more advantage than the state-of-the-art algorithms over the continuous growth of graph streams. Experimental results show that BSR-TC achieves accuracy of at least 99.8% for global triangles, when the ratio of initial memory budget against whole graph streams [Formula: see text] and given [Formula: see text], respectively.
An End-to-End Chinese Accent Classification Method

Accent classification plays a vital role in the study of automatic speech recognition and the history of language development. Due to the complexity of Chinese structure and the diversity of Chinese accents, general accent classification methods cannot be effectively used for Chinese accent classification. This article proposes an end-to-end classification method. This method first learns a filter bank with variable window width from the original waveform. Secondly, the distinguishable features are extracted according to the different time-frequency aggregations in the voice signal. Finally, we use the temporal convolutional attention network to solve insufficient contextual information for Chinese accent classification to achieve accent feature classification. The effectiveness of this method is evaluated on the Common voice corpus. The results show that the method has better generalization performance. Compared with the baseline method, the English and Chinese speech data set accuracy increased by 6.27% and 26.11%, respectively.
Robotics as Assistive Technology for Treatment of Children with Developmental Disorders—Example of Robot MARKO

In this paper we present the development of a robot designed as an assistive technology in therapy of children suffering from developmental disorders, tested on example of cerebral palsy. To achieve desired results the therapy should start as early as possible and it should be as intensive as possible. However, the therapeutic exercises are tiresome, boring and sometimes painful for children, and therefore the children are usually not motivated to undergo such therapy. This problem is particularly hard if patients are very young children. The earlier a treatment begins and the more intensive it is, the results are better. The anticipated benefit of such robot is to support the therapist efforts to conduct specific therapeutic exercises by establishing affective attachment of the child to the robot. Our aim was to develop robot to motivate the children to perform the therapist’s instructions, and to perform more exercises than they usually do.
Science, Technology and Innovation Measurements in the Maghreb Union (AMU) and the Impact of Indicators: The Institutional Dimension

The role of science, technology and Innovation (STI) as an engine of development has been globally recognized. This needs the formulation and regular reviews of policies using good and viable indicators. However, in most African countries, policies suffer from the negligible importance of indicators in the eyes of policymakers. The five Maghreb countries (Algeria, Libya, Tunisia, Morocco and Mauritania) have been putting significant efforts to improve publications and patents on the ground that they best translate the effectiveness of STI policies. The findings show that real progress has been made in recent years on these indicators in the region following the policies adopted. Looking more in depth at the Algerian situation, the results indicate that STI indicators have been able to transform policy-making through learning, and international cooperation this progress has been slowed down by institutional constraints and limited inclusiveness. Our contribution is to highlight the importance of having a proper institution in STI indicators construction and usage in African countries. The methodology used secondary data and an in-depth case analysis through the observant participant approach.
Size Does Matter: Overcoming Limitations during Training when using a Feature Pyramid Network

State-of-the-art object detectors need to be trained with a wide variety of data in order to perform well in real-world problems. Training-data-diversity is very important to achieve good generalization. However, there are scenarios where we have training data with certain limitations. One such scenario is when the objects of the testing set have a different size (discrepancy) from the objects used during training. Another scenario is when we have high-resolution images with a dimension that is not supported by the model. To address these problems, we propose a novel pipeline that is able to handle high-resolution images by cropping the original image into sub-images and put them back in the end. Also, in the case of the discrepancy of object sizes, we propose two different techniques based on scaling the image up and down in order to have an acceptable performance. In addition, we also use the information from the Feature Pyramid Network to remove false-positives. Our proposed methods overcome state-of-the-art data augmentation policies and our models can generalize to different object sizes even though limited data is provided.
Sentiment analysis of messages on Twitter related to COVID-19 using deep learning approach

The widespread situation of the Coronavirus-19 (COVID-19) pandemic is a tangible and pressing concern. Many changes in terms lifestyle are necessary to reduce the chance of infection. While citizens have gone through different emotions, they would share their thought and interactions on social media, especially on Twitter. COVID-19 related messages can imply social emotion. This study performs sentiment analysis on tweets and annotated them into six classes of positive and negative feelings consist of anger, disgust, fear, sadness, joy, and surprise. We analyzed both textual information and historical data. We collected 120,642 unique tweets datasets between 1 January 2020 and 30 June 2021. We compared the performance of five neural network models which are multi-layer perceptron, RNN, LSTM, Bidirectional LSTM, and GRU with several metrics consists of accuracy, F1 score, precision, and recall. The results show that LSTM model has the highest accuracy score at 77.4% while GRU has the best F1 score at 77.13%. These models could be used to monitor the movement of negative emotions. In addition, we provide interesting insights from sentiment analysis with tweet data and historical reported of infected cases, and vaccination data.
Integral Attacks on Pyjamask-96 and Round-Reduced Pyjamask-128

In order to provide benefits in the areas of fully homomorphic encryption (FHE), multi-party computation (MPC), post-quantum signature schemes, or efficient masked implementations for side-channel resistance, reducing the number of multiplications has become a quite popular trend for the symmetric cryptographic primitive designs. With an aggressive design strategy exploiting the extremely simple and low-degree S-box and low number of rounds, Pyjamask, the fundamental block cipher of the AEAD with the same name, has the smallest number of AND gates per bit among all the existing block ciphers (except LowMC or Rasta which work on unconventional plaintext/key sizes). Thus, although the AEAD Pyjamask stuck at the second round of the NIST lightweight cryptography standardization process, the block cipher Pyjamask itself still attracts a lot of attention. Not very unexpectedly, the low degree and the low number of rounds are the biggest weakness of Pyjamask. At FSE 2020, Dobraunig et al. successfully mounted an algebraic and higher-order differential attack on full Pyjamask-96, one member of the Pyjamask block cipher family. However, the drawback of this attack is that it has to use the full codebook, which makes the attack less appealing. In this paper, we take integral attacks as our weapon, which are also sensitive to the low degree. Based on a new 11-round integral distinguisher found by state-of-the-art detection techniques, and combined with the relationship between round keys that reduces the involved keys, we give the key recovery attack on the full Pyjamask-96 without the full codebook for the first time. Further, the algebraic and higher-order differential technique does not work for Pyjamask-128, the other member of the Pyjamask block cipher family. To better understand the security margin of Pyjamask-128, we present the first third-party cryptanalysis on Pyjamask-128 up to 11 out of 14 rounds.
A truncated deep neural network for identifying age groups in real time images

Recent research works have been focussing on estimating age from facial images. Age estimation from faces basically involves two sub-processes: extracting features and estimating learning function. Age classification from an input image is the task at hand in this project; age will be classified into 3 categories: 1) Toddler, 2) Teen, 3) Adult. Classifying age automatically from an image has been widely used in our day-to-day lives, particularly in the listed fields: biometrics, surveillance systems, and commercial kiosks. The purpose of this study is to categorize facial images based on their age. Prominently, previously existing research works were performed on contrived and unreal images curated in laboratories. Those images did not correctly portray the distinctions and fluctuations that are evident in real human faces. This paper uses deep convolutional neural networks (CNN) on the available data to overcome the above discussed challenge.
Predicting YOLO Misdetection by Learning Grid Cell Consensus

Despite the immense performance improvement of deep learning-based object detection, the state-of-the-art object detection systems are still prone to misdetections. This work presents a method to predict such misdetections at run-time by using a small network, referred to as ConsensusNet, to learn the correlation patterns or consensus of neighboring detections before non-maximum suppression (NMS). Based on such correlations, ConsensusNet predicts if there are misdetection failures. The proposed method is experimentally evaluated considering single person class from COCO dataset and using YOLOv3 as the object detection system. It shows the proposed method can achieve accuracy of 84.6% and the performance measured in other metrics are also promising. To the best of our knowledge, ConsensusNet is the first network reported for predicting misdetections in object detection.
Impact of image compression on CNN performance metrics for CPS nodes at the Arctic Tundra

In cyber-physical systems (CPS), data can be col-lected and pre-processed at the edge nodes before being sent to a back-end server. However, for environments like the Arctic tundra, both energy and data networks are constrained. The nodes must save energy to have long operational lifetime from a single battery charge. Consequently, the size of the data to be sent should be reduced as much as possible without losing the information needed to perform the analysis at the back-end. This paper evaluates the effects of data size reduction at edge nodes on the analysis performance at a back-end server. The use-case considers edge nodes deployed in the Arctic tundra to take pictures of animals. These images are then transmitted to a back-end server to determine the species of the animals using a convolutional neural network (CNN). We identify 17 functions for reducing the data size of an image series. We run several combinations on a series of images collected on the tundra. Our experimental results show a possible reduction in the number of bytes required to represent the images between 90.2% and 92.1% with decrease in mean precision and mean recall by less than 0.03. Re-scaling images is required to reach large size reductions on the image series.
Adversarial Perturbations for Evolutionary Optimization

Sampling methods are a critical step for model-based evolutionary algorithms, their goal being the generation of new and promising individuals based on the information provided by the model. Adversarial perturbations have been proposed as a way to create samples that deceive neural networks. In this paper we introduce the idea of creating adversarial perturbations that correspond to promising solutions of the search space. A surrogate neural network is “fooled” by an adversarial perturbation algorithm until it produces solutions that are likely to be of higher fitness than the present ones. Using a benchmark of functions with varying levels of difficulty, we investigate the performance of a number of adversarial perturbation techniques as sampling methods. The paper also proposes a technique to enhance the effect that adversarial perturbations produce in the network. While adversarial perturbations on their own are not able to produce evolutionary algorithms that compete with state of the art methods, they provide a novel and promising way to combine local optimizers with evolutionary algorithms.
How fuzzy are L2 phonological representations?

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Network Data Remanence Side Channel Attack on SPREAD, H-SPREAD and Reverse AODV

Side Channel Attacks (SCAs) was first introduced by Paul Kocher in 1996 to break the secret key of cryptographic algorithms using the inherent property of the implementation along with the mathematical structure of the cipher. These categories of attacks become more robust as they do not require any mathematical cryptanalysis to retrieve the key. Instead, they exploit the timing measurements, power consumption, leaked electromagnetic radiation of the software/hardware platforms to execute key-dependent operations for the cipher. This, in turn, aids the adversary to gather some additional information about the computation. The overall concept of leaking secrets through side channel has been extended for Wireless Sensor Networks (WSNs) that implements Secret Sharing (SS) Scheme to exchange secrets between two nodes across multiple paths. Now in the idealized network model, it is assumed that for such SS schemes, all the paths between the two communicating nodes are atomic and have the same propagation delay. However, in the real implementation of TCP/IP networks, the shares propagate through every link and switch sequentially. Hence the attacker can probe any number of paths or switches to get the residual shares from previous messages that still exist in the network even when a new message is being sent. This kind of side channel vulnerability is known as Network Data Remanence (NDR) attacks. In this paper, we specifically target two SS schemes named Secure Protocol for Reliable Data Delivery (SPREAD) and Hybrid-Secure Protocol for Reliable Data Delivery (H-SPREAD), and an on-demand routing protocol named Path Hopping Based on Reverse AODV (PHR-AODV) to launch NDR based side channel attacks on the WSNs. We then show two specific categories of NDR attacks; a) NDR Blind and b) NDR Planned on the schemes mentioned above. We use an in-house C++ library to simulate our proposed attacks, and the experimental results reveal that the impact of NDR Blind attacks is negligible for these schemes, whereas the probability of data recovery for NDR Planned attacker proportionally increases with the path length.
Synthetic Wireless Signal Generation for Neural Network Algorithms

As the use of wireless communication devices increases, so does the need for effective signal generation techniques. We show how different methods of waveform generation with the same signal-to-noise ratios result in varied accuracy performance for modulation recognition when using neural networks (NNs). Our research indicates that two aspects of waveform generation significantly change NN behavior with equivalent SNRs. First, generating purely random IQ constellation points adversely impacts classification accuracy, as real radios do not have completely random constellation points. We illustrate that NNs can use the intermediate IQ samples to improve classification accuracy. Second, we exhibit that adding pure additive white Gaussian noise (AWGN) results in different accuracies compared to using channel distortion models, which include fading, multipath, and phase shifts. We show that relying on SNR alone to characterize signals can result in misleading and incorrect performance evaluations of NNs when applied to radio performance. Our work also demonstrates that care must be taken when applying channels models to simulate noise, as NNs can use specific channel distortion features to identify a modulation.
Vulnerability Scanning and Management of University Computer Network

University computer Network (UCN) being open, large and diverse in nature has become highly prone to vulnerabilities. Proper scanning and management of vulnerabilities is required to ensure timely remediation and removal of vulnerabilities. Vulnerabilities poses a great danger to network security if they are not dealt at regular intervals. Timely scanning, assessment and remediation of vulnerabilities is extremely important in a UCN to keep the network secure from malicious entities. To ensure network security, an effective Vulnerability Management (VM) System is required. Current paper discusses about importance of vulnerability management in a UCN and also elaborate about tools used for scanning and assessment of vulnerabilities in a UCN. Implementation of scanning tools was done on a UCN and scan results were analyzed to identify weaknesses or vulnerabilities in the network. Based on the scan results of scanning tools, the decision about mitigation of vulnerabilities can be taken according to priority. An effective and efficient VM framework is crucial for preventing cybersecurity breaches as it checks for new vulnerabilities on regular basis and also provide remediations or solutions to deal with the vulnerabilities. For the current work, scanning tools Nmap and Nexpose were used to identify vulnerabilities in a UCN. Nmap is used for information gathering of network and Nexpose is used for scanning the network for vulnerability detection.