Reviewerly/exHarmony huggingface dataset:
BIA: A Blockchain-based Identity Authorization Mechanism

The abuse of personal identity information is one of the most serious problems worldwide. Most social services or businesses use the identity authorization to confirm their validity and legality and the copies of users' identity certification are usually recorded by the service providers. It is easy to leak the users' identity information due to the untrustworthy service provider or single-point security failure, and various social problems are then caused. To deal with such problems, this paper proposes a Blockchain-based Identity Authorization mechanism (BIA). First, an Identity Authorization Module (IAM) is devised, which reads the identity certificate and transform the identity plaintext to ciphertext under the authorization by the user's identity certificate entity and password. IAM guarantees the security of identity information by keeping its plaintext offline. Second, a Business Contract Module (BCM) is designed, which provides a general smart contract framework for identity authorization that can be adopted by most of social services or businesses. Third, a double-chain blockchain infrastructure is developed, whereby the encrypted identity information and service smart contracts are respectively recorded in the tamper-resistant, non-repudiable, and publicly verifiable way. Finally, a prototype system has been developed to verify the security, feasibility and effectiveness of the proposed BIA.
Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense

We evaluate the ability of Bert embeddings to represent tense information, taking French and Chinese as a case study.In French, the tense information is expressed by verb morphology and can be captured by simple surface information.On the contrary, tense interpretation in Chinese is driven by abstract, lexical, syntactic and even pragmatic information.We show that while French tenses can easily be predicted from sentence representations, results drop sharply for Chinese, which suggests that
Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services

Knowledge graphs are proving to be an increasingly important part of modern enterprises, and new applications of such enterprise knowledge graphs are still being found. In this paper, we report on the experience with the use of an automatic knowledge graph system called Saffron in the context of a large financial enterprise and show how this has found applications within this enterprise as part of the “Conversation Concepts Artificial Intelligence” tool. In particular, we analyse the use cases for knowledge graphs within this enterprise, and this led us to a new extension to the knowledge graph system. We present the results of these adaptations, including the introduction of a semi-supervised taxonomy extraction system, which includes analysts in-the-loop. Further, we extend the kinds of relations extracted by the system and show how the use of the BERTand ELMomodels can produce high-quality results. Thus, we show how this tool can help realize a smart enterprise and how requirements in the financial industry can be realised by state-of-the-art natural language processing technologies.
User Feedback and Ranking in-a-Loop: Towards Self-Adaptive Dialogue Systems

Accurate skill retrieval is a key factor for the success of modern conversational AI agents. The major challenges lie in the ambiguity in human spoken language and the wide spectrum of candidate skills. In this paper, we make the first attempt to attack the problem by implementing a user feedback enhanced reranking strategy, and propose a self-adaptive dialogue system (AdaDial) for conversational AI agents. In AdaDial, we consider estimating user feedback and adjusting ranking strategy into a "closed-loop". In particular, we propose a scalable schema for user feedback estimation and a feedback enhanced reranking model with customized feature encoding, target attention based feature assembling, and multi-task learning. As a result, AdaDial achieves self-adaptivity at both individual- and system-levels. Online experimental results demonstrate that AdaDial could not only retrieve desired skills for different users in different scenarios, but also correct its regular strategy according to negative feedback. AdaDial has been deployed on a large-scale conversational AI agents with tens of millions daily queries, and is bringing continued positive impacts on user experience.
Information Model of Data Management in Network Online Educational Systems

In connection with the reform of the national education system, large educational complexes (LEC) become the main structural unit [1]. Large educational complexes can unite educational organizations of different levels [2], as a result, management processes become more complex, which, in turn, requires the construction of new models for generating effective management decisions. The object of research is all possible relationships and processes of functioning of educational organizations within a large educational complex. The subject of the research is the models and algorithms of rating evaluation of LEC activity as one of the tools for managing its effectiveness. Practical significance of the research: proposed an information model for managing rating indicators of the entire educational complex based on an integrated assessment of educational processes of each educational organization (EO) included in the complex. The results of the authors research will allow the management bodies to make an effective impact to improve the performance of each EO. Results were verified in the public and professional Association - the inter-district Board of Directors of educational organizations in Moscow No. 10 and in the state educational institution School No. 1389 [3].
Converging Credit-based and Reactive Datacenter Transport using ECN and RTT

There are various transport protocols developed to mitigate the congestion caused by burst traffic and thousands of concurrent flows in data center networks (DCNs). These protocols can be classified into two kinds: proactive protocols and reactive protocols. Reactive protocols are common to current DCNs and they deal with the network congestion after congestion arises. However, as the common link speed gradually increases from 10 Gbps to 100 Gbps, reactive protocols, such as DCQCN, encounter a significant problem of slowly responding to the network congestion. In contrast, proactive protocols, especially credit-based congestion control, literally prevent the network from congestion before it occurs, which can also provide zero data loss, fast convergence, and low buffer occupancy.Due to the above advantages, the gradual deployment of credit-based protocols into DCNs is a visible task in the future. However, in the real deployment scenarios, it is hard to guarantee that one protocol can be deployed in every host at one time. Thus, when deploying credit-based protocols into DCNs incrementally, the network will converted to a multi-protocol state and face several fundamental challenges: (i) unfairness, (ii) high buffer occupancy and (iii) heavy tail delay. In this paper, we propose a multi-signal credit-based protocol, called MCP, aiming for converging reactive and credit-based protocols in DCNs. To the best of our knowledge, MCP is the first to leverage both explicit congestion notification and round trip time information to detect the network congestion in data queue and redefine the feedback control of the credit-based protocols. We also compared the performance of feedback control algorithms based on different congestion signals. Our evaluation shows that MCP effectively addresses the unfair link allocation problem and converges with reactive protocols rapidly. In addition to that, MCP achieves high utilization, low buffer occupancy and fast convergence at the same time.
Multi-Authority Attribute Based Encryption With Policy-hidden and Accountability

In this paper, an attribute-based encryption scheme with policy hidden and key tracing under multi-authority is proposed. In our scheme, the access structure is embedded into the ciphertext implicitly and the attacker cannot gain user's private information by access structure. The key traceability is realized under multi-authority and collusion is prevented. Finally, based on the DBDH security model, it is proved that this scheme can resist the plaintext attack under the standard model.
New and Simple Offline Authentication Approach using Time-based One-time Password with Biometric for Car Sharing Vehicles

Car sharing provides consumers a flexible peer-to-peer service or station service. However, the connectivity problems are pervasive in remote areas and places with multi-path obstructions with no clear line-of-sight (LoS). In this scenario, availability of the network can be intermittent and is not always guaranteed, especially for untethered wireless networks consisting of mobile vehicles. A conventional online authentication scheme; therefore, is not an effective solution when it comes to securing the vehicles. Also, the malicious attackers could gain access to the vehicles using a replay of the user signal, that is known as a “replay attack” In order to provide an effective authentication approach, we propose an offline authentication approach based on a Time-based One-time Password (TOTP) algorithm. OTP is chosen due to its protection against the notorious replay attack that is popular against keyless start vehicles. It also utilized an additional security biometric factor to enhance the security of the driver's authentication. The new proposed scheme is divided into online and offline schemes to provide a secure solution. The novelty is that it can enable the authorized drivers to securely start and operate during offline duration just by using their mobile devices. The other car-sharing maintenance operations including registration, booking, telematics monitoring, and location tracking can be performed or synchronized whenever the network is back in connection and reachable within the wireless coverage area.
Analytical Hierarchy Process (AHP) to Determine the Sustainable Drainage System Used for Flood Management in Central Karanganyar Regency

Abstract A sustainable drainage system is one of the right methods in dealing with floods in cities, including in Karanganyar. The parameters of technology selection problems planned with the Analytic Hierarchy Process (AHP) are technical, economic, environmental, social/cultural, and technology: infiltration wells with drainage channels, rainwater harvesting with drainage channels, and dry swale. Rainfall data is used in this research. The area used is planning, budget planning, and AHP method steps with hierarchical compilation, pairwise comparisons, priority values, testing consistency, and data obtained from technology selection questionnaires filled with related institutions. The most influential criteria in the choice drainage system technology are environmental criteria 49%, technical 33%, economy 12%, and social/cultural 5%. The priority technology is rainwater harvesting with drainage channels with a percentage of 37.3%, infiltration wells with drainage channels 35.2%, and dry swale 27.4%.
Movie Rating System based on Blockchain

Entertainment industry is growing at a very rapid pace where a huge amount of money is being put into the making of the films. The success or failure of a movie is determined by the box office collection. The box office collection is dependent on various factors like director, actors, actresses, technicians, production house, musicians, marketing, etc. But yet it is highly influenced by the reviews and feedbacks given by the people, critics, media, etc. Due to the latest trends in the marketing field, digital media is used as a form of recommendation system where users read the reviews about a movie, or the ratings rated before making any decision to watch a movie. The existing problem that are identified with these reviews is that nowadays bots are being deployed to increase the reviews, and also fake reviews, negative reviews are also a major concern since most of the reviews are given by people without watching the movies. These problems create huge financial loss to the company, the people associated with it and also the movie industry as a whole. So in order to overcome the existing problems we are considering using blockchain technology as the future of entertainment industry. Since blockchain is a distributed network we can use it to store the reviews given by the user which is nontamperable and this in turn can help rate a movie correctly. Because of the genuine reviews stored in blockchain, movie goers can choose the correct film and make a good film successful thereby increasing the box office collection of the movie.
IPatch: a remote adversarial patch

Abstract Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame. In the past, it has been shown how an attacker can fool these models by placing an adversarial patch within a scene. However, these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the image. In this paper, we introduce a new type of adversarial patch which alters a model’s perception of an image’s semantics. These patches can be placed anywhere within an image to change the classification or semantics of locations far from the patch. We call this new class of adversarial examples ‘remote adversarial patches’ (RAP). We implement our own RAP called IPatch and perform an in-depth analysis on without pixel clipping on image segmentation RAP attacks using five state-of-the-art architectures with eight different encoders on the CamVid street view dataset. Moreover, we demonstrate that the attack can be extended to object recognition models with preliminary results on the popular YOLOv3 model. We found that the patch can change the classification of a remote target region with a success rate of up to 93% on average.
Reactive Key-Loss Protection in Blockchains

We present a novel approach for blockchain asset owners to reclaim their funds in case of accidental private-key loss or transfer to a mistyped address. Our solution can be deployed upon failure or absence of proactively implemented backup mechanisms, such as secret sharing and cold storage. The main advantages against previous proposals is it does not require any prior action from users and works with both single-key and multi-sig accounts. We achieve this by a 3-phase \(Commit() \rightarrow Reveal() \rightarrow Claim() - or - Challenge()\) smart contract that enables accessing funds of addresses for which the spending key is not available. We provide an analysis of the threat and incentive models and formalize the concept of reactive KEy-Loss Protection (KELP).
Blockchain in Internet of Things: a Systematic Literature Review

Internet of Things (IoT) devices are rapidly increasing in acceptance and proliferation such as smart home devices, wearable devices and industrial systems; all of those devices are connected simultaneously in centralized client/server model and requires to be authenticated through the server, for that the security issues become more challengeable. So, moving the IoT system into the decentralized path is the right solution to get rid of those security issues. One of the best common decentralization systems is a Blockchain (BC), which can address a lot of IoT issues, and as especially access control issues. In this paper, and through the processes of automated search and selection, this study obtains some studies publications in the field BC in IoT Recently published; and we conducted an extensive literature review to understand status of applying BC based solutions for IoT. In addition, we also highlighted issues pertaining to IoT, and displaying the studies contributions, and methodologies used to get rid of those issues pertaining to IoT by using BC features, this studies indicates through their Findings that the research in BC is becoming more prominent and requires more effort in developing new methodologies and framework to integrate BC.
Parallel Privacy-Preserving Shortest Paths by Radius-Stepping

The radius-stepping algorithm is an efficient, parallelixable algorithm for finding the shortest paths in graphs. It solved the problem in Δ-Stepping algorithm, which has no known theoretical bounds fur general graphs. 1" this paper, we describe a parallel privacy-preserving method for finding SingleSource Shortest Paths (SSSP). Our optimized method is based on the Radius-Stepping algorithm. The method is implemented on iop of the Secure Multiparty Computation (SMC) Sharemiiid platform. We have reshaped the radius-stepping algorithm to work on vectors representing the graph in a SIMD manner, in order to enable a fast execution using the secret-sharing based SMC protocol set of Sharemind. The results of the real implementation show an efficient method that reduced the execution time hundreds of times iii comparison with a standard case of the privacy-preserving radius-stepping and Δ-Stepping algorithms.
Study of Improved Median Filtering using adaptive window architecture

Over the past few years computer vision has become the essential aspect of modern era of technology. This computer vision is manly based on image processing whereas the image processing includes three important aspects as image filtering, image compression & image security. The image filtering can be achieved by using various filtering techniques but the PSNR & operating frequency are the most challenging aspects of image filtering. This paper mainly focused on overcoming the challenges appears while removing the salt & pepper noise with conventional median filtering by developing improved adaptive moving window architecture median filter & comparing its performance to have improved performance in terms of PSNR & operating frequency.
Blockchain based decentralized knowledge sharing system - Jigsaw

Knowledge is formalized information that is cited or used in logical inference. The growth of knowledge sharing in the field of education helps people understand and grow their potential. Individuals are willing to share knowledge when they are certain to benefit from reciprocity or build a reputation. Individuals spend enormous amounts of time, resources and scarifications to gain knowledge. Individuals are currently sharing their expertise through forums, vlogs, videos, and trainings. Nevertheless, there is a variety of untrusted and unvalidated knowledge available for a subject from the recipient's point of view. To grasp what they need, it requires multiple resources. There are no direct benefits for people who make, comment or vote for these resources. In this research, the researcher implemented a distributed knowledge sharing system centered on Blockchain to allow multiple individuals to contribute their knowledge by building a resource that is moderated, verified, and community structured. Each creator, commenter and voter of knowledge will receive rewards, and the knowledge invested will gain it forever. The researchers used cryptography and knowledge economy in addition to stellar blockchain to make this process secure and more trustable. In this paper, the proposed system is realistic, efficient and has an ever-earning model.
M3

This paper proposes M3, an end-to-end system that dynamically distributes memory resources among competing applications to maximize their overall performance. Today's data center workloads, can adapt to a wide range of memory sizes, and they are built on complex software stacks.
Improving preference elicitation in a conversational recommender system with active learning strategies

Conversational Recommender Systems are gaining more and more attention in the last years. They are characterized by the ability of establishing a multi-turn dialog with the user. Since those systems generally work in a cold-start situation, most of the conversation is devoted to the preference-elicitation step. However, in order to generate good recommendations, the user profile should be as rich as possible, which requires great user effort. In this paper, we investigate the application of Active Learning techniques for improving the preference elicitation step in a Conversational Recommender System. We compared different state-of-the-art techniques, and carried out a user study with 192 users in order to assess their effectiveness both in terms of recommendation accuracy and user effort. Results demonstrated that integrating item selection strategies based on item popularity improves the quality of the recommendations in terms of Hit Rate and nDCG, compared to a strategy based only on user-provided preferences.
Center Prediction Loss for Re-identification

The training loss function that enforces certain training sample distribution patterns plays a critical role in building a re-identification (ReID) system. Besides the basic requirement of discrimination, i.e., the features corresponding to different identities should not be mixed, additional intra-class distribution constraints, such as features from the same identities should be close to their centers, have been adopted to construct losses. Despite the advances of various new loss functions, it is still challenging to strike the balance between the need of reducing the intra-class variation and allowing certain distribution freedom. Traditional intra-class losses try to shrink samples of the same class into one point in the feature space and may easily drop their intra-class similarity structure. In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples. The prediction error is then regarded as a loss called Center Prediction Loss (CPL). Unlike most existing metric learning loss functions, CPL involves learnable parameters, i.e., the center predictor, which brings a remarkable change in the properties of the loss. In particular, it allows higher freedom in intra-class distributions. And the parameters in CPL will be discarded after training. Extensive experiments on various real-world ReID datasets show that the proposed loss can achieve superior performance and can also be complementary to existing losses.
Bug Report Summarization using Believability Score and Text Ranking

During the maintenance phase of software development, bug reports provide software developers with important information. However, bug reports often include complex and long discussions. Therefore, concise and accurate summaries can help developers save the time for reading the full contents of bug reports. Several researchers have proposed summarizing bug reports. However, none of them proposed combining two different scores for measuring how important each sentence is among the developers' comments. In this paper, we propose an unsupervised bug report summarization which combines believability score and text ranking score for measuring the degree to which a sentence is important, in order to generate high-quality summaries. The experimental results over a public dataset show that our method outperforms the state-of-the-art method in terms of summary quality.