Reviewerly/exHarmony huggingface dataset:
A Collective Action Simulation Platform

In this paper, we discuss some types of expectation that contribute to the behaviour of social agents, and investigate the role that these social expectations can play in the resolution of collective action problems. We describe our Collective Action Simulation Platform (CASP), a framework that allows us to integrate the Java-based Repast Simphony platform with a Prolog-based event calculus interpreter. This allows us to run simulations of agents who make reference to social expectations when reasoning about which actions to perform. We demonstrate the use of CASP in modelling a simple scenario involving agents in a collective action problem, showing that agents who are informed by social expectations can be led to cooperative behaviour that would otherwise be considered “non-rational”.
Is blockchain a suitable technology for ensuring the integrity of data shared by lighting and other building systems?

Increasing amounts of data are available from lighting and other building systems. In commercial buildings, these data can be used to improve system and energy performance, detect and diagnose faults, and facilitate maintenance. Building data are not only of interest to owners and operators of the building systems, however. Owners and operators of similar buildings, manufacturers of building systems, utilities, and city agencies also have interesting use cases. Energy data can, for example, be used to verify the performance of energy-conservation measures, issue renewable-energy certificates, and financially settle grid services. Increased data sharing, however, significantly expands the cyber-attack surface, creating new challenges. In this work, blockchain is explored as an option for ensuring the integrity of data that are shared by lighting and other building systems. Blockchain fundamentals and variants are briefly reviewed, and value propositions relevant to building systems are discussed. A recently developed blockchain applicability framework (BAF) that builds upon and addresses the limitations of previous applicability models is also briefly reviewed. The BAF is used to assess the suitability of blockchain over other technologies or approaches for building-data applications, using emerging connected lighting systems as an example use case.
A Benchmark Terrorist Face Recognition Database

Terrorism remains to be among the most significant global dangers by 2020. A considerable literature has grown around the theme of the fight against terrorism. However, to the best of our knowledge, no research work has been performed on terrorist face images caught in real-world and uncontrolled conditions. In this context, we propose in this paper a terrorist suspect face recognition database. Following substantial studies and analysis on some terrorist attacks that have occurred since 2013, faces of terrorists have been collected from the net. The LATIS-PACTE-PROFILER database is made available to the research community. For validation and benchmarking purposes, we propose a face identification approach based on the HOG features and the SVM classifier.
Differentially Private Sketches for Jaccard Similarity Estimation

This paper describes two locally-differential private algorithms for releasing user vectors such that the Jaccard similarity between these vectors can be efficiently estimated. The basic building block is the well known MinHash method. To achieve a privacy-utility trade-off, MinHash is extended in two ways using variants of Generalized Randomized Response and the Laplace Mechanism. A theoretical analysis provides bounds on the absolute error and experiments show the utility-privacy trade-off on synthetic and real-world data. A full version of this paper is available at http://arxiv.org/abs/2008.08134.
Review of Trends in Automatic Human Activity Recognition Using Synthetic Audio-Visual Data

An in-depth study of knowledge and technologies was made related to the various scientific, technical, and industrial domains necessary for the acquisition of skills and capabilities for the design and development of a multisensory fusion system for vehicle cockpits. After an extensive literature review, it was possible to determine the baselines of the solution to be developed and obtain a pipeline prototype.
Remote Education Applications in the Technical University of Sofia

We describe in detail the experience of the Technical University of Sofia in switching to remote teaching as soon as anti-epidemic restrictions was imposed. Remote teaching platform available are tested and implemented in real educational environment on the examples of regular lectures and exams as well as admission exams.
An Integrated IoT-Blockchain Implementation for End-to-End Supply Chain

In industrial sectors, the supply chain management (SCM) systems have been getting smarter by accepting and deploying of evolutionarily advanced technologies, for example, the Internet of Things (IoT). IoT made a supply chain more efficient than ever before; IoT analytical tools enable the organizations to manage and react to all important issues and events of the supply chain in well and decent ways. However, the increasingly high demands and complex structure of supply chain systems, including the growing number of participants and operations, are the main concerns to managing the business effectiveness. IoT is a solution to this problem. IoT uses its smart technologies to manage and trigger the supply chain events and operations, and therefore enables an effective supply chain delivery; however, to keep privacy and security in complex, a scaled structure of the supply chain is a challenge for IoT centralized platform. This paper indents to deploy a proof of concept, end-to-end supply chain management (E2ESCM) system for asset delivery (i.e., food products), employing the combined features of IoT and Blockchain. With IoT-Blockchain, we can surely develop and later examine the end-to-end product delivery; IoT devices are used to get instant information, and Blockchain is used to record every transaction occurs in supply ecosystem onto its secure ledger system; the record is always immutable, and participants participating into a distributed network are authorized to maintain record onto a ledger. Therefore, employing IoT-Blockchain, the study examines a significant level of security, traceability, and transparency among participants within the supply chain ecosystem.
A Study on the Impact of Intradomain Finetuning of Deep Language Models for Legal Named Entity Recognition in Portuguese

Deep language models, like ELMo, BERT and GPT, have achieved impressive results on several natural language tasks. These models are pretrained on large corpora of unlabeled general domain text and later supervisedly trained on downstream tasks. An optional step consists of finetuning the language model on a large intradomain corpus of unlabeled text, before training it on the final task. This aspect is not well explored in the current literature. In this work, we investigate the impact of this step on named entity recognition (NER) for Portuguese legal documents. We explore different scenarios considering two deep language architectures (ELMo and BERT), four unlabeled corpora and three legal NER tasks for the Portuguese language. Experimental findings show a significant improvement on performance due to language model finetuning on intradomain text. We also evaluate the finetuned models on two general-domain NER tasks, in order to understand whether the aforementioned improvements were really due to domain similarity or simply due to more training data. The achieved results also indicate that finetuning on a legal domain corpus hurts performance on the general-domain NER tasks. Additionally, our BERT model, finetuned on a legal corpus, significantly improves on the state-of-the-art performance on the LeNER-Br corpus, a Portuguese language NER corpus for the legal domain.
Position Paper: Analyzing the Impacts of Facial Recognition

Considering the lack of consensus on the deployment of facial recognition technologies, many organizations, including public bodies, NGOs and private companies have alerted public opinion and called for a broad debate on facial recognition. We believe that such a debate is indeed necessary. However, in order to be really productive, it is essential to ensure that arguments can be expressed and confronted in a rigorous way. The main objective of this position paper is to help set the terms of this debate on a solid basis. To this aim, we present an incremental and comparative risk-analysis methodology for facial recognition systems. The methodology introduces, for a better separation of concerns, four levels of analysis: the purpose, the means, the use of facial recognition and its implementation. We discuss each of these levels and illustrate them with examples based on recent developments. Interested readers can find more details, in particular about the use of ethical matrices to facilitate the analysis, in an extended version of this position paper published as an Inria report [].
Architectural Feature Re-Modularization for Software Product Line Evolution

Extensive maintenance leads to the Software Product Line Architecture (PLA) degradation over time. When there is the need of evolving the Software Product Line (SPL) to include new features, or move to a new platform, a degraded PLA requires considerable effort to understand and modify, demanding expensive refactoring activity. In the state of the art, search-based algorithms are used to improve PLA at package level. However, recent studies have shown that the most variability and implementation details of an SPL are described in the level of classes. There is a gap between existing approaches and existing practical needs. In this work, we extend the current state of the art to deal with feature modularization in the level of classes by introducing a new search operator and a set of objective functions to deal with feature modularization in a finer granularity of the architectural elements, namely at class level. We evaluated the proposal in an exploratory study with a PLA widely investigated and a real-world PLA. The results of quantitative and qualitative analysis point out that our proposal provides solutions to properly re-modularize features in a PLA, being preferred by practitioners, in order to support the evolution of SPLs.
Secure multi-gigabit optical link design for high energy physics experiment with acceleration of more secure RC4 variant in reconfigurable platform

Many cryptographic attacks are capable to break RC4. Still RC4 becomes alive in crypto community with several variants. Insufficient key schedule is the most important weakness of RC4 where the first bytes of output reveals information about the key. The loop-holes can be corrected by discarding initial portion of the output stream [1] which is known as RC4-dropN (N is a multiple of 256, such as 768 or 1024). Here we shows RC4 can be made more secured if an additional RC4-like Post-KSA Random Shuffling (PKRS) process is introduced between KSA and PRGA. The PKRS process is tuned to form as many S-boxes as required by particular design architectures involving multiple coprocessors, each one undertaking byte-by-byte processing. Following a recent idea [2, 3] the speed of execution of each processor is also enhanced by another fold if the byte-by-byte processing is replaced by a scheme of processing two consecutive bytes together. Adopting some new innovative concepts, three hardware design architectures are proposed in a suitable FPGA embedded system involving 1, 2 and 4 coprocessors functioning in parallel and a study is made on accelerating RC4 by processing bytes in byte-by-byte mode achieving throughputs from 1-byte-in-1-clock to 4-bytes-in-1-clock. The hardware designs are appropriately upgraded to accelerate RC4 further by processing 2 consecutive RC4 bytes together and it has been possible to achieve a maximum throughput of 8-bytes per clock in Xilinx Virtex-5 LX110t FPGA [4] architecture followed by secured communication High Energy Physics (HEP) application that preserves the DC-balancing of the link and permits forward error correction (FEC) along with encryption.
DeepBT and NLP Data Augmentation Techniques: A New Proposal and a Comprehensive Study

Data Augmentation methods – a family of techniques designed for synthetic generation of training data – have shown remarkable results in various Deep Learning and Machine Learning tasks. Despite its widespread and successful adoption within the computer vision community, data augmentation techniques designed for natural language processing (NLP) tasks have exhibited much slower advances and limited success in achieving performance gains. As a consequence, with the exception of applications of back-translation to machine translation tasks, these techniques have not been as thoroughly explored by the wider NLP community. Recent research on the subject also still lacks a proper practical understanding of the relationship between data augmentation and several important aspects of model design, such as hyperparameters and regularization parameters. In this paper, we perform a comprehensive study of NLP data augmentation techniques, comparing their relative performance under different settings. We also propose Deep Back-Translation, a novel NLP data augmentation technique and apply it to benchmark datasets. We analyze the quality of the synthetic data generated, evaluate its performance gains and compare all of these aspects to previous existing data augmentation procedures.
Understanding Voice Search Behavior: Review and Synthesis of Research

This article reviews the recent user-oriented studies on voice search interaction and aims to understand how users perform and perceive voice search. With a systematic data collection and screening process, twenty-seven publications were included. We found that the current studies sampled predominantly young and well-educated population. Four topics of interest in voice search were identified, i.e., query characteristics, query strategy, spoken conversational search, user’s perception. The result reveals the influences of voice-based modality on users’ search behavior and perceptions and the review concludes with potential directions of research on voice search interaction.
Generate Adversarial Examples by Nesterov-momentum Iterative Fast Gradient Sign Method

At present, the security of neural networks has attracted more and more attention, and the emergence of adversarial examples is one of the problems. The gradient-based attack algorithm is a representative attack algorithm. Among the gradient attack algorithms, the momentum iterative fast gradient sign method (MI-FGSM) is currently an efficient and typical attack algorithm. However, this method will cause the gradient to advance too fast and accelerate too much. In this article, we propose an attack algorithm based on Nesterov-momentum called Nesterov-momentum iterative fast gradient sign method (NMI-FGSM). Nesterov-momentum makes a correction when the gradient is updated to avoid moving too fast. Experiments show that our algorithm performs well and has achieved a high success rate. At the same time, under the same attack success rate, the perturbation value of the adversarial examples generated by our algorithm is smaller.
Efficient Blockchain System based on Proof of Segmented Work

The use of Blockchain in cryptocurrency introduces a technology that acts as an unswervingly growing ledger with the capability to keep an everlasting record of all the transactions in a secure, chronological and immutable system. It removes dependency on central financial services, essentially removing the middle-man from the transaction. Bitcoin holds the highest share in the cryptocurrency market. However, Bitcoin has three major problems which are excessive power consumption, confirmation time, and fair reward distribution. Hence, we propose a system (FaircoinBD) where we focus on reducing energy consumption by limiting participation, increasing propagation speed and fair reward distribution. We have also compared our proposed system with Bitcoin and Ether in terms of energy consumption and have reached the conclusion, that if Bitcoin and Ethereum network used our proposed model there would be a 83.33% and 80.0012% reduction of energy consumption respectively.
Algorithms for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e99" altimg="si118.svg"><mml:mi>q</mml:mi></mml:math>-ary error-correcting codes with limited magnitude and feedback

Berlekamp and Zigangirov completely determined the capacity error function for binary error correcting codes with noiseless feedback. It is still an unsolved problem if the upper bound for the capacity error function in the non-binary case of Ahlswede, Lebedev, and Deppe is sharp. We consider wraparound channels with limited magnitude and noiseless feedback. We completely determine the capacity error function for all q-ary wraparound channels with a magnitude of level r. All of our algorithms use partial noiseless feedback. Furthermore, a special case of the problem is equivalent to Shannon's zero-error problem.
Ensemble-Based Deep Metric Learning for Few-Shot Learning

Overfitting is an inherent problem in few-shot learning. Ensemble learning integrates multiple machine learning models to improve the overall prediction ability on limited data and hence alleviates the problem of overfitting effectively. Therefore, we apply the idea of ensemble learning to few-shot learning to improve the accuracy of few-shot classification. Metric learning is an important means to solve the problem of few-shot classification. In this paper, we propose ensemble-based deep metric learning (EBDM) for few-shot learning, which is trained end-to-end from scratch. We split the feature extraction network into two parts: the shared part and exclusive part. The shared part is the lower layers of the feature extraction network and is shared across ensemble members to reduce the number of parameters. The exclusive part is the higher layers of the feature extraction network and is exclusive to each individual learner. The coupling of the two parts naturally forces any diversity between the ensemble members to be concentrated on the deeper, unshared layers. We can obtain different features from the exclusive parts and then use these different features to compute diverse metrics. Combining these multiple metrics together will generate a more accurate ensemble metric. This ensemble metric can be used to assign labels to images of new classes with a higher accuracy. Our work leads to a simple, effective, and efficient framework for few-shot classification. The experimental results show that our approach attains superior performance, with the largest improvement of \(4.85\%\) in classification accuracy over related competitive baselines.
Enhancement of Resulting Image Search Engine (ERISE) by Content-Based Image Retrieval System

The execution of (ERISE) framework depends on proficient feature extraction and exact recovery of comparative enhancement of resulting images. This paper represents a brief investigation of the main techniques utilized for every image recovery, whereas indicating the importance of this rising innovation. Due to the alarming growth of the Web and the brightly high volume of information, we extend the method of CBIR - Content-Based Image Retrieval system by adding an extra dimension of enhancement. The point of this paper is also to create a framework design to back querying for exceptionally huge image databases with user-specified distance measures that can be utilized for a wide assortment of datasets in the domain of image enhancement. A large number of image query results image retrieval by query image but image quality may affect sometimes. That's why it's much important to enhance the image quality for better usage of an image when needed. The methodology illustrates the authenticity of this current methodology's convenience by differentiating out its efficiency from current methodologies.
CP-ABE with Hidden Access Policy and Outsourced Decryption for Cloud-Based EHR Applications

Electronic health record (EHR) stores not only the patient's health-related data but also the sensitive individual information. Consequently, data security and access privacy are the greatest concern of EHR applications while sharing the data through the cloud. Ciphertext policy attribute-based encryption (CP-ABE) is one of the popular one-to-many encryption schemes, which helps to attain fine-grained access control in cloud domain. In CP-ABE, since the access policy is attached to the ciphertext, anyone with the ciphertext can see the data owner's policy that may cause privacy leakage. In this paper, we proposed a CP-ABE cryptosystem that supports access policy obfuscation and outsourced decryption enabling efficient and secure operations in cloud environment. Linear secret sharing (LSS) scheme is used for supporting any monotonic access structures, thereby improving the access policy's expressiveness. We have incorporated prime-order bilinear group and matrix-based LSS scheme that increases the computational efficiency of the cryptosystem. Finally, we have evaluated the performance of our CP-ABE cryptosystem using practical implementation utilizing Charm framework.
Dialogical Guidelines Aided by Knowledge Acquisition: Enhancing the Design of Explainable Interfaces and Algorithmic Accuracy

Understanding expert domain knowledge may inform the design of explainable interfaces that convey comprehensible information by “mirroring” the explanation practice of domain experts. Likewise, scrutinizing expert domain knowledge is pivotal to guarantee data quality and enhance algorithmic accuracy, by zooming in on the types of data and information that constitute relevant and reliable representations in a given domain. Against this backdrop, the paper revitalizes the field of knowledge acquisition and presents easily applicable user-centered and value-oriented dialogical guidelines to unravel domain knowledge with the aim of enhancing the design of explainable interfaces and algorithmic accuracy. While it might seem counter-intuitive to revisit the field of knowledge acquisition in the era of machine learning and deep learning, there are plenty of cases in which AI systems, trained on biased data, have led to epistemological deficiencies with morally harmful consequences. In order to improve the data preparation and modelling stage in the development of ML models, this paper suggests that AI developers could benefit from the pragmatic application of manageable dialogical guidelines aided by knowledge acquisition to cultivate shared understanding between AI developers and domain expert end users.
Illumination Harmonization with Gray Mean Scale

Illumination harmonization is an important problem for high-quality image composite. Given the source image and the target background, it aims to transform the foreground appearance that it looks in the same lighting condition as the target background. Because the ground-truth composite image is difficult to get, previous works can use only synthetic datasets, which however, provide with only artificially adjusted and limited inputs. In this paper we contribute to this problem in two aspects: 1) We introduce a semi-automatic approach to capture the ground-truth composite in real scenes, and then create a dataset that enables faithful evaluation of image harmonization methods. 2) We propose a simple yet effective harmonization method, namely the Gray Mean Scale (GMS), which models the foreground appearance transformation as channel-wise scales, and estimates the scales based on gray pixels of the source and the target background images. In experiments we evaluated the proposed method and compared it with previous methods, using both our dataset and previous synthetic datasets. A new benchmark thus is established for illumination harmonization in real environments.