Reviewerly/exHarmony huggingface dataset:
Models of University Interaction with the Economy and Social Sphere of the Region

The purpose of this work is to summarize and typologize ways and forms of organizing the interaction between the domestic system of higher education and regional markets. The authors focus on the forms of interaction organization between higher education institutions and labor market at the regional level associated with their regional specifics. The basic principles of interaction between regional systems of higher education and business community (focus on meeting the needs of the economy, collegial, open and mutually beneficial nature of cooperation) and the existing experience of various subjects of the Russian Federation are discussed, taking into account the specifics of their own regional situation. The article dwells on the characteristics of the current level of innovation and entrepreneurial activity of Russian universities, systematizes the practices coordinating the needs of the economy and the organization of higher education in the field of personnel and technological support of the regional economy. The authors discuss the models of joint activities of universities, business communities and regional authorities institutionalization in the interests of socio-economic development of the subject of the Russian Federation, as well as two major components of the impact of higher education on the economy and social sphere of the region, namely the adequate staffing for the development of the region and the commercialization of R &amp; D / R &amp; D products of universities in the innovation sphere of the regions. It is shown that the level of regional involvement of universities in the development of the business environment, projects and programs that operate in the interests of local communities is different, and its institutionalization is diversified. In this connection, it is proposed to harmonize the needs of the economy and the activities of universities in the field of meeting personnel demands and developing new production technologies, combining the efforts of the executive authorities and specialized intermediary structures. The article argues the necessity of the formation of a specialized interface in the structure of a higher education institution that ensures the orientation of scientific and educational activities of higher education institutions towards the prospective demands of the regional economy.
Communication Lower Bounds for Statistically Secure MPC, With or Without Preprocessing

We prove a lower bound on the communication complexity of unconditionally secure multiparty computation, both in the standard model with $$n=2t+1$$ parties of which t are corrupted, and in the preprocessing model with $$n=t+1$$ . In both cases, we show that for any $$g \in \mathbb {N}$$ there exists a Boolean circuit C with g gates, where any secure protocol implementing C must communicate $$\varOmega (n g)$$ bits, even if only passive and statistical security is required. The results easily extends to constructing similar circuits over any fixed finite field. This shows that for all sizes of circuits, the O(n) overhead of all known protocols when t is maximal is inherent. It also shows that security comes at a price: the circuit we consider could namely be computed among n parties with communication only O(g) bits if no security was required. Our results extend to the case where the threshold t is suboptimal. For the honest majority case, this shows that the known optimizations via packed secret-sharing can only be obtained if one accepts that the threshold is $$t= (1/2 - c)n$$ for a constant c. For the honest majority case, we also show an upper bound that matches the lower bound up to a constant factor (existing upper bounds are a factor $$\lg n$$ off for Boolean circuits).
Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines

Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines.
Research on Information Security Situation Awareness System Based on Big Data and Artificial Intelligence Technology

In order to solve the problem that it is difficult for current information security awareness system to accurately detect massive data security in time, the information security situation awareness system is optimized based on the background of big data network environment and artificial intelligence technology. According to the operation process and framework of the information security situation awareness system, the hardware configuration of the system is optimized and the synchronous operation mechanism of artificial intelligence multi-data security awareness is standardized. At the same time, the information security situation inference algorithm is improved based on the data support vector algorithm. By extracting the universal security features of the data source and setting the information security situation awareness standard parameters according to the extracted results, the system software structure is designed in combination with the information security situation awareness standard parameters, and the steps of comparison and repair based on the security feature parameters are added to the information security situation awareness process. Finally, the optimal design of the information security situation awareness system was completed. Experiments show that the information security situation awareness system based on big data and artificial intelligence technology has significantly improved the operation efficiency and accuracy compared with the traditional system, and can effectively fulfill the security detection requirements for massive data.
On-Body Tangible Interaction: Using the Body to Support Tangible Manipulations for Immersive Environments

Recent technological advances in immersive devices open up many opportunities for users to visualize data in their environments. However, current interactive solutions fail at providing a convenient approach to manipulate such complex immersive visualizations. In this article, we present a new approach to interact in these environments, that we call On-Body Tangible interaction (OBT): using the body to physically support the manipulation of an input device. The use of the body to support the interaction allows the user to move in his environment and avoid the inherent fatigue of mid-air interactions. In this paper, we explore the use of a rolling device, which fits well on-body interaction thanks to its form factor and offers enough degrees of freedom (DoF) for data manipulation. We first propose a new design space for OBT interactions, and specifically on the forearm. Then we validate the feasibility of such an approach through an experiment aimed at establishing the range, stability and comfort of gestures performed with the device on the forearm. Our results reveal that on-body tangible interaction on the forearm is stable and offers multiple DoFs with little fatigue. We illustrate the benefits of our approach through sample applications where OBT interactions are used to select and execute space-time cube operations.
Hypertext as Method

Historically, there has been a tendency to consider hypertext as a type of system, perhaps characterized by provision of links or other structure to users. In this paper, we consider hypertext as a method of inquiry, a way of viewing arbitrary systems. In this view, what are traditionally called "navigational hypertext systems" might be considered as information retrieval systems, "spatial hypertext systems" as brainstorming systems, etc., while their "hypertext" nature results from the way in which such systems are conceived, developed, and/or presented. The benefit of such a shift is the ability to apply this hypertextual method of inquiry to systems not normally considered part of the hypertext community. In this paper, we specifically apply this view to artificial intelligence, and examine how this application can be productive.
Quantum tomography of light states by photon-number-resolving detectors

We show that data from homodyne-like detection based on photon-number-resolving (PNR) detectors may be effectively exploited to reconstruct quantum states of light using the tomographic reconstruction techniques originally developed for homodyne detection based on photodiodes. Our results open new perspectives to quantum state reconstruction, and pave the way to the use of PNR-based homodyne-like detectors in quantum information science.
A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks

Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text.However, complex Question Answering (QA) typically requires multi-hop reasoning -i.e. the integration of supporting facts from different sources, to infer the correct answer.This paper proposes Document Graph Network (DGN), a message passing architecture for the identification of supporting facts over a graph-structured representation of text.The evaluation on HotpotQA shows that DGN obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning.
Improvement of Residual Attention Network for Image Classification

The existing residual attention network (RAN) method mainly utilizes the deeper network layer for the image objects which are to be classified. However, when the network depth is simply increased, it will lead to gradient dispersion (or explosion) effect. To address the problem, we propose a new improvement method of residual attention network for image classification, which applies several upsampling schemes to the RAN process, i.e., the stacked network structure extraction, and the bottom-up and top-down feedforward attention for residual learning. In the experiments, we have given comparisons of different network structures and different upsampling methods that are applied to the RAN. The proposed improvement method achieves state-of-the-art image classification performance on two benchmark datasets including CIFAR-10 (4.23$$\%$$ error) and CIFAR-100 (21.15$$\%$$ error). Compared with the traditional RAN method, the proposed improvement method can improve the accuracy of image classification to some extent.
CrowdMed: A Blockchain-Based Approach to Consent Management for Health Data Sharing

The need for greater health data sharing is widely acknowledged in the healthcare industry. The provision of healthcare services and attempts to harness Big Data analytics are adversely affected by the fragmentation of medical data across healthcare providers. However, attempts to encourage greater data sharing have been unsuccessful due to privacy concerns and lack of motivation for patients to share their data. We propose CrowdMed, a medical data management framework to facilitate greater data sharing, empowering patients with full control over access to their medical data. The framework is fully transparent, ensuring that patients have full knowledge over how who has access to their data, and how their data is being used. Using blockchain as the foundation for consent management, patients can trust that only parties with permission can view their data. Permissions are also finely specified, and can be revoked or modified according to the patient’s wishes. Following these permissions, data is transferred automatically between parties, conveniently and efficiently alleviating the problem of data fragmentation. CrowdMed also addresses the lack of motivation for data sharing, incentivising patients to share more of their data for research purposes through reward tokens and an innovative pricing mechanism. The framework integrates fully with existing data storage architecture, aiding in its widespread adoption.
‘Museum Escape’: A Game to Increase Museum Visibility

A peripheral museum with a nevertheless important collection of ancient items, wished to increase its visibility and digital presence in order to become more known and attract new visitors. Among the different solutions designed and implemented, we also designed a series of games of different kinds (i.e. mini vs complex games) for different purposes, like profiling of potential visitors and advertising the venue in social media. Based on lessons learnt from physical games played at museums of the same type in the same region, we designed and implemented a museum escape game. The game is presented here, to provide implementation details regarding the concept, the game mechanics, the interface design, the technical details, as well as the game art. We conclude with primary user testing and future steps that include plans for creating museum escape games with different technologies, like augmented reality and virtual reality.
Toward Automated Classification of B-Acute Lymphoblastic Leukemia

We present an automated approach of classifying a stain-normalized white blood cell as being either a malignant (B-ALL) cell or a healthy (HEM) cell. We apply various data augmentations to the images in order to compensate for the class imbalance. As our primary approach, we use the ResNeXt101 Convolutional Neural Network architecture and “cut” it at various points in the network. We show that an ensemble of the same ResNeXt101 model trained for varying durations and a change in the decision threshold boundary lead to better performance as compared to individual model performances. Our final ensemble model leads to a weighted F1 score of 0.825 on the online preliminary test set. After training on the entire training data and validating in the preliminary test set, ResNeXt50 and ResNeXt101 achieve weighted F1 scores of 0.857 and 0.849 on the preliminary test set, respectively.
Clustering based Privacy Preserving of Big Data using Fuzzification and Anonymization Operation

Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results into limitation of data reconstruction while securing the sensitive data. This paper presents a clustering based privacy preservation probabilistic model of big data to secure sensitive information..model to attain minimum perturbation and maximum privacy. In our model, sensitive information is secured after identifying the sensitive data from data clusters to modify or generalize it.The resulting dataset is analysed to calculate the accuracy level of our model in terms of hidden data, lossed data as result of reconstruction. Extensive experiements are carried out in order to demonstrate the results of our proposed model. Clustering based Privacy preservation of individual data in big data with minimum perturbation and successful reconstruction highlights the significance of our model in addition to the use of standard performance evaluation measures.
Electronic and photonic counting statistics as probes of non-equilibrium quantum dynamics

Abstract We analyse the full counting statistics (FCS) of photons flowing in and out of a microwave cavity coupled to a voltage-biased Josephson junction. Tunnelling of Cooper pairs generates a coherent flow of photons into the cavity whilst at the same time photons can also leak out incoherently. We use a very general unitary transformation method to demonstrate that there is a simple connection between the FCS of the charges and the photons in the long time limit, revealing that all the cumulants of the coherent and the incoherent processes match in that limit. We also explore some of the interesting features in the counting statistics of the charges and photons which arise from the strongly nonlinear dynamics of the system. These include very narrow distributions associated with the emergence of coherent transport and regimes where counting of either an odd or an even number of photons leaving the cavity can result in strongly non-classical cat states within the cavity.
Symmetries and entanglement of stabilizer states

Stabilizer states constitute a set of pure states which plays a dominant role in quantum error correction, measurement--based quantum computation, and quantum communication. Central in these applications are the local symmetries of these states. We characterize all local symmetries of arbitrary stabilizer states and provide an algorithm which determines them. We demonstrate the usefulness of these results by showing that the additional local symmetries find applications in entanglement theory and quantum error correction.
Smart offloading technique for CP-ABE encryption schemes in constrained devices

Nowadays, we are attending to the wide proliferation of IoT devices in various computing environments. Actually, this trend is the natural way to extend the M2M communication. However, these devices are facing serious resource constraints. To mitigate this problem, the huge amount of the exchanged data is collected from these devices for being stored and processed by the Cloud. Furthermore, since the Cloud is honest but curious, it may reveal personal information about users’ habits owning these devices and can even lead to user profiling. Consequently, security mechanisms should be deployed at different levels to preserve data privacy so that only authorized users can gain access to the smallest piece of data according to the collection purpose. By assuming that the Cloud-is-honest-but-curious, we can not provide full or similar access to different untrusted Cloud services. Therefore, we should define the relevant privacy level implementing the data access control for each Cloud service according to some criteria. In this context, CP-ABE is a promising solution addressing this problem. This novel attribute-based public key encryption system provides a flexible fine-grained access control to data for any data requestor. However, performing all the related cryptographic operations on such devices is practically infeasible because of the resource constraints. For alleviating all the computation burden on these resource-limited devices, several schemes have been proposed. In this work, we propose a smart offloading technique that switches dynamically from full encryption to partial encryption according to a wise decision strategy considering the available resources and some crucial parameters like the number of attributes and the size of the data being encrypted. The relevant decision is based on a machine learning algorithm. To the best of our knowledge, this is the first paper proposing an adaptive CP-ABE scheme for constrained device optimizing the overall available resources.
Automatic Construction of a Large-Scale Speech Recognition Database Using Multi-Genre Broadcast Data with Inaccurate Subtitle Timestamps

As deep learning-based speech recognition systems are spotlighted, the need for large-scale speech databases for acoustic model training is increasing. Broadcast data can be easily used for database construction, since it contains transcripts for the hearing impaired. However, the subtitle timestamps have not been used to extract speech data because they are often inaccurate due to the inherent characteristics of closed captioning. Thus, we propose to build a large-scale speech database from multi-genre broadcast data with inaccurate subtitle timestamps. The proposed method first extracts the most likely speech intervals by removing subtitle texts with low subtitle quality index, concatenating adjacent subtitle texts into a merged subtitle text, and adding a margin to the timestamp of the merged subtitle text. Next, a speech recognizer is used to extract a hypothesis text of a speech segment corresponding to the merged subtitle text, and then the hypothesis text obtained from the decoder is recursively aligned with the merged subtitle text. Finally, the speech database is constructed by selecting the sub-parts of the merged subtitle text that match the hypothesis text. Our method successfully refines a large amount of broadcast data with inaccurate subtitle timestamps, taking about half of the time compared with the previous methods. Consequently, our method is useful for broadcast data processing, where bulk speech data can be collected every hour.
Stochastic local operations with classical communication of absolutely maximally entangled states

Absolutely maximally entangled (AME) states are maximally entangled for every bipartition of the system. They are crucial resources for various quantum information protocols. We present techniques for verifying that two AME states are equivalent concerning stochastic local operations and classical communication (SLOCC). The conjecture that for a given multipartite quantum system all AME states are SLOCC equivalent is proven false. We also show that the existence of AME states with minimal support of six or more particles results in the existence of infinitely many such non-SLOCC-equivalent states. Moreover, we present AME states which are not SLOCC equivalent to the existing AME states with minimal support.