Reviewerly/exHarmony huggingface dataset:
Augmented Therapy with Online Support Groups

Support groups are often formed in hospitals and clinics to enable group therapy. A support group consists of patients suffering from a same disease. Manual formation of support groups has three drawbacks. First, it is “local”, i.e., a support group for a specific type of cancer in a local hospital may contain patients with different symptoms and treatments. Discussions in such heterogeneous groups are not necessarily useful for their members. Second, support groups are often “static” and do not meet emerging needs of patients. Third, there may not be enough motivation in patients to join such groups. In this paper, we use the social Web to envision a framework for the automatic formation of dynamic support groups. Our framework consists of several components to build support groups, motivate patients to join, and keep them engaged in those groups.
Reduction in number of constraints in max-margin early event detectors

Early action detection aims to detect the action as soon as possible, and has a variety of applications. In the field of computer vision, the first learning formulation on early event detection, the max-margin early event detector(MMED), is investigated by Hoai and Torre [1, 2]. In this study, the purpose of the proposed approach is to reduce the large number of constraints generated in the MMED method. The basic idea is to remove the redundancy among every three consecutive constraints in the MMED method. The number of constraints is reduced from O(IL3) to O(IL2) where I is the number of training examples and L is the length of the example time series. Proof and the experimental results are provided to show the correctness and feasibility of the proposed approach.
Why Dose Layer-by-Layer Pre-training Improve Deep Neural Networks Learning?

Deep perceptron neural networks are capable of implementing a hierarchy of successive nonlinear conversions. But training these neural networks by conventional learning methods such as the error back-propagation is faced with serious obstacles owing to local minima. The layer-by-layer pre-training method has been recently proposed for training these neural networks and has shown considerable performance. In the pre-training method, the complex problem of training deep neural networks is broken down into some simple sub-problems in which some corresponding single-hidden-layer neural networks are trained through the error back-propagation algorithm. In this chapter, the theoretical principles regarding how this method effectively improves the training of deep neural networks are discussed, and the maximum discrimination theory is proposed as a proper framework for analysis of training convergence in these neural networks. Subsequently, discriminations of inputs in different layers of two similar deep neural networks, one of which is directly trained through the conventional error back-propagation algorithm and the other through layer-by-layer pre-training method, are compared, and results confirm the validity of the proposed framework.
Research on Human Behavior Recognition Based on Convolutional Neural Network

The traditional human behavior recognition technology mainly includes sign point action recognition technology and recognition technology based on motion sensor parameters. The former’s error is very large and the latter’s recognition speed is slow as well as the accuracy is very low. In this paper, a method of recognition of depth learning behavior based on convolutional neural network is proposed to identify different behaviors (jogging, walking, running, upstairs, downstairs, sitting) with different location of mobile phone (arm, waist, pocket, wrist), collecting a large amount of data by using the built-in sensor of the mobile phone. The data are standardized, normalized and window segmentation, and then the data are divided into testing set and training set. Establish a convolutional neural network learning model to extract local feature structure and combine supervised learning mode, use training set for training, and then use testing set for classifying and evaluating. Through experiments, in the common movements and different placement positions, the accuracy is more than 94%, and the effect of high speed and high accuracy to identify human behavior is achieved.
The innovator scholar, talented pedagogue, creative person (on the 85-th anniversary of Arkady Sokolov)

Professor Arkady Sokolov, Ph. D. (Pedagogy) is a prominent scholar. The author highlights themes and publications unique for Sokolov: the future for libraries, library science and bibliography, humanism, intelligencia and library intellectuals, social informatics, social communications, philosophy of information. In his works, prof. Sokolov gives his special attention to the library profession, librarians, library education, the libraries on the whole. “Retrospektiva…” digest are A. Sokolov’s special project. The author also reflects on Sokolov’s innovative academic social initiatives: in 1967-1984 he headed the first in the nation informatics chair In Leningrad State Institute of Culture; founded St. Peterburg library society, the first one inRussia. The Professor’s giant flock numbers 44 scientists. Sokolov’s biographical information is given; the journals where he is an editorial board member are named; disciplines in which he delivers lectures; his titles are named. Arkady Sokolov’s outstanding personality is also depicted. In conclusion, the main bibliography (56 titles) is given.
Lipuite, a new manganese phyllosilicate mineral from the N'Chwaning III mine, Kalahari Manganese Fields, South Africa

Abstract A new phyllosilicate mineral, lipuite (IMA2014-085), has been discovered from the N'Chwaning III mine, Kalahari Manganese Fields, Northern Cape Province, Republic of South Africa. It occurs as platy, tabular, or granular crystals and veined agglomerate in association with Mn-bearing sugilite, taniajacoite, pectolite, richterite, norrishite and namansilite. Lipuite is dark red–brown with vitreous lustre, red streak, an estimated Mohs hardness of 5 and the measured density is 2.83(3) g/cm 3 . It is biaxial (+) and characterised by bright red to dark red colour in thin section with measured refractive indices in white light: α = 1.635(1), β = 1.653(1), γ = 1.670(1) and 2V = 86(2)°. The Raman spectra of lipuite are composed of over 21 bands at 109, 146, 162, 183, 206, 244, 288, 342, 362, 455, 496, 520, 552, 613, 669, 886, 930, 971, 1097, 3487 and 3540 cm –1 . The empirical formula from microprobe analyses is (based on total number of cations = 27.5 and structural refinement): K 1.12 Na 8.16 (Mn 4.77 Fe 0.07 ) Σ4.84 Mg 0.44 [Si 11.97 O 30 (OH) 4 ](PO 4 ) 0.94 O 2 (OH) 2 ·4H 2 O. The idealised formula is: KNa 8 Mn 3+ 5 Mg 0.5 [Si 12 O 30 (OH) 4 ](PO 4 )O 2 (OH) 2 ·4H 2 O. Lipuite is orthorhombic, space group Pnnm , a = 9.080(3), b = 12.222(3), c = 17.093(5) Å, V = 1897.0(9) Å 3 and Z = 2. The strongest powder X-ray diffraction peaks [d, Å ( I ) ( hkl )] are: 9.965(40)(011), 2.938(33)(310), 2.895(100)(311), 2.777(38)(224), 2.713(53)(320), 2.483(32)(126), 2.086(35)(046) and 1.534(40)(446). The crystal structure of lipuite is characterised by sheets of SiO 4 tetrahedra that are linked together along [010] by K + , Na + , Mn 3+ , Mg 2+ and P 5+ cations, as well as hydrogen bonds. These tetrahedral sheets consist of 14-membered rings of SiO 4 tetrahedra that zigzag along [100]. The two independent Mn 3+ cations are both octahedrally coordinated. They form five-membered, edge-shared octahedral clusters between the SiO 4 tetrahedral sheets. Lipuite represents a rather unique structure type and its silicate tetrahedral sheets can be considered a derivative of the silicate sheets in mica.
Ellipsis and Computational Linguistics

Abstract This chapter considers approaches to ellipsis within computational linguistics. It begins with the structure of the ellipsis site—a topic that has received little attention in computational linguistics. There are two prominent accounts of the recovery of ellipsis: that of Lappin and McCord (1990) and Dalrymple et al. (1991). The topic of licensing follows. This topic has not been directly addressed in the computational literature, but the chapter covers two related issues of direct computational interest: the identification and generation of ellipsis occurrences. This is followed by three additional topics: identifying the antecedent, dialogue, and text mining.
Cloud-Based Data-Sharing Scheme Using Verifiable and CCA-Secure Re-encryption from Indistinguishability Obfuscation

A cloud-based re-encryption scheme allows a semi-trusted cloud proxy to convert a ciphertext under delegator’s public-key into a ciphertext of delegatee’s. However, for an untrusted cloud proxy, as the re-encryption program was outsourced on the cloud, the cloud can debug the program and might have illegal activities in practice, such as monitoring the program executing, returning an incorrect re-encryption ciphertext, or colluding with the participants to obtain the sensitive information. In this work, we propose a construction of cloud-based verifiable re-encryption by incorporating new cryptographic primitives of indistinguishability obfuscation and puncturable pseudorandom functions, which can achieve the master-secret security even if the proxy colludes with the delegatee. Furthermore, our scheme can provide the white-box security in re-encryption procedure to implement the sensitive-data protection in the presence of white-box access, and it resists on chosen-ciphertext attacks in both the first-level encryption and the second-level encryption. The decryption is very efficient since it only requires several symmetric PRF operations, which can be deployed and applied in the light-weight security device such as Mobile Phones (MPs), Wireless Body Area Networks (WBANs) and nodes in Internet-of-Things (IoTs).
Grouped attribute strength-based image retrieval

Visual attributes, as a bridge between humans and machines, have played an important role in various applications. We focus on attribute-based image retrieval. Unlike early works that either initiate the image search via binary attributes or refine the search results via relative attributes, we propose to realize image retrieval using grouped attribute strength, which are learned from both binary attributes and relative attributes. First, early methods are employed to predict the binary attributes and sort the relative attributes in descending order in different situations. Second, we group the sorted images corresponding to each attribute and combine the learned binary and relative attributes to learn the grouped attribute strength of the images, which result in a new attribute feature vector for each image. Finally, we apply the learned grouped attribute strength to different retrieval models to realize our image retrieval tasks. We demonstrate the approach on LFW-10 and Shoes datasets and show its clear advantages over traditional binary attribute-based retrieval methods.
Use case repository framework based on machine learning algorithm to analyze the software development estimation with intelligent information systems

The success of information system process depends on accuracy of software estimation. Estimation is done at initial phase of software development. It requires a collection of all relevant required information for estimating the software effort. In this paper, a methodology is proposed to maintain a knowledgeable use case repository to store the use cases of various projects in several software project-related domains. This acts as a reference model to compare similar use cases of similar types of projects. The use case points are calculated and using this, schedule estimation and effort estimation of a project are calculated using the formulas of software engineering. These values are compared with the estimated effort and scheduled effort of a new project under development. Apart from these, the effective machine learning technique called neural network is used to measure how accurately the information is processed by use of case repository framework. The proposed machine learning-based use case repository system helps to estimate and analyze the effort using the machine learning algorithms.
A Countermeasure against DPA on SIMON with an Area-Efficient Structure

Differential power analysis (DPA) is an effective side channel attack method, which poses a critical threat to cryptographic algorithms, especially lightweight ciphers such as SIMON. In this paper, we propose an area-efficient countermeasure against DPA on SIMON based on the power randomization. Firstly, we review and analyze the architecture of SIMON algorithm. Secondly, we prove the threat of DPA attack to SIMON by launching actual DPA attack on SIMON 32/64 circuit. Thirdly, a low-cost power randomization scheme is proposed by combining fault injection with double rate technology, and the corresponding circuit design is implemented. To the best of our knowledge, this is the first scheme that applies the combination of fault injection and double rate technology to the DPA-resistance. Finally, the t-test is used to evaluate the security mechanism of the proposed designs with leakage quantification. Our experimental results show that the proposed design implements DPA-resistance of SIMON algorithm at certain overhead the cost of 47.7% LUTs utilization and 39.6% registers consumption. As compared to threshold implementation and bool mask, the proposed scheme has greater advantages in resource consumption.
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A non-increasing sequence π=(d1,…,dn) of nonnegative integers is said to be graphic if it is realizable by a simple graph G on n vertices. A graphic sequence π=(d1,…,dn) is said to be potentially 3Cℓ-graphic if there is a realization of π containing cycles of every length r, 3 ≤ r ≤ ℓ. Li et al. proposed a problem about giving a criteria of potentially 3Cℓ-graphic sequences. For ℓ=5,6, Chen et al. investigated this problem and showed that if dℓ≥ℓ2, then π is potentially 3Cℓ-graphic. In this paper, we extend the above results of Chen et al. for ℓ=5,6 to the general case ℓ ≥ 5, and prove that for every integer ℓ ≥ 5, if dℓ≥ℓ2, then π is potentially 3Cℓ-graphic.
A Genetic Deep Learning Model for Electrophysiological Soft Robotics

Deep learning methods are modelled by means of multiple layers of predefined set of operations. These days, deep learning techniques utilizing un-supervised learning for training neural networks layers have shown effective results in various fields. Genetic algorithms, by contrast, are search and optimization algorithm that mimic evolutionary process. Previous scientific literatures reveal that genetic algorithms have been successfully implemented for training three-layer neural networks. In this paper, we propose a novel genetic approach to evolving deep learning networks. The performance of the proposed method is evaluated in the context of an electrophysiological soft robot-like system, the results of which demonstrate that our proposed hybrid system is capable of effectively training a deep learning network.
Comparing TensorFlow Deep Learning Performance and Experiences Using CPUs via Local PCs and Cloud Solutions

Deep Learning is a multilevel learning tool that helps better understand information from various sources. For those who are newly entering the Deep Learning domain, the use of TensorFlow is an optimal way to enhance and learn about neural network designs and a way to deep dive into machine learning. Even though TensorFlow is early with its inception, it is however quickly becoming the direction in which many researchers are using for their open source machine-learning framework. The research, we take advantage of, is the capabilities of TensorFlow to enhance the processing of a substantial dataset that shows the growth and accuracy of cells of embryos in incubation. A typical approach to execute such a massive dataset, today’s researchers would first go to directly to the cloud to accomplish this output. From the experiments that ran in this paper, we concluded that an individual afresh to Deep Learning will not always need to proceed to a large GPU cloud solution setup for optional results. The research presented, garners the usage of local PC CPUs, in which we conclude, introduce a beginner to deep learning, less frustration, easier setup time, and better cost initiatives, than one would get if adopting a cloud solution.
Service technologies in academic libraries (case study of I. S. Turgenev Orel State University Scientific Library)

Innovative technologies for providing the scope of library information services are discussed. Users satisfaction with the products/services quality within the services strategy of Orel State University Scientific Library was monitored. The study goals were to investigate into the new format of staff operation, to refine criteria and evaluation indicators, and to specify services and practices. The quality features were measured and compared to the ideal model of library and information services. It is found that the service dominant idea is consonant with the staff’s and users’ orientation toward open, secure, friendly working environment. The author remarks to the staff's business activity, increasing the Library’s rates in the external environment. She proves that the service technologies make the efficient instrument of the humanistic mission rather than obtaining of the maximum extrabudgetary funding. The author proposes several key management solutions, i.e. stimulation of staff’s culture; development of corporate professional cooperation in information exchange; generation of inhouse intellectual content; intensification of user awareness forms; methodological support; monitoring; promotion of the existing resources and dissemination of information products. The obtained empirical data make the foundation for increasing effectiveness of services.
Hashing to G2 on BLS pairing-friendly curves

When a pairing e : G 1 x G 2 → G T , on an elliptic curve E defined over F q , is exploited in a cryptographic protocol, there is often the need to hash binary strings into G 1 and G 2 . Traditionally, if E admits a twist Ẽ of order d, then G 1 = E (F q )⋂ E [ r ], where r is a prime integer, and G 2 = Ẽ(F q k/d )⋂ Ẽ [ r ], where k is the embedding degree of E w.r.t. r. The standard approach for hashing a binary string into G 1 and G 2 is to map it to general points P∈E ( F q ) and P′ ∈ Ẽ (F q k/d ), and then multiply them by the cofactors c = #E (F q )/ r and c ′ = #Ẽ (F q k/d )/ r respectively. Usually, the multiplication by c′ is computationally expensive. In order to speed up such a computation, two different methods (by Scott et al. and by Fuentes et al. ) have been proposed. In this poster we consider these two methods for BLS pairing-friendly curves having k ∈ {12, 24, 30, 42,48}, providing efficiency comparisons. When k = 42,48, the Fuentes et al. method requires an expensive one-off pre-computation which was infeasible for the computational power at our disposal. In these cases, we theoretically obtain hashing maps that follow Fuentes et al. idea.
Secure Transmission of Space Images using Joint Encryption Compression

The Consultative Committee for Space Data Systems (CCSDS) has issued data compression standards for multi-spectral images aimed to reduce the amount of data transmitted from Earth observation satellites (EOS) to ground stations. Confidentiality of compressed image assumed to be provided by Separate modules which consume mass, power and volume of the space craft. This paper investigate the possibility of applying joint encryption compression to protect compressed image during its transmission by randomized switching between two coding modes in the entropy coder stage of CCSDS-123 compression standard which achieves sufficient security without introducing much complexity to the compression algorithm or affecting compression performance.
Freezing the driven pulsed information of a single qubit

We use a rectangular pulse to freeze the possibility of estimating the coherent parameters (θ, [Formula: see text]) of a single qubit, the local information, and the fidelity of the driven qubit. The possibility of freezing these three quantities depends on the initial state settings and the strength of the pulse. It is shown that the freezing areas increase as the detuning increases, while they decrease at smaller values of the pulse strength. Moreover, as the Fisher information reaches its minimum bounds, the local information and the fidelity are maximized. We show that, when the Fisher information is completely frozen, the pulsed local information and the fidelity are frozen. The freezing degree depends on the initial detuning and the pulse strength. Our results show that, despite the initial and final states of the system being different, the amount of local information is independent of the initial state of the system. These results may be useful in the context of quantum cryptography, teleportation, and secure communication.
New Locally Correctable Codes Based on Projective Reed–Muller Codes

Locally decodable codes and locally correctable codes (LCCs) have several important applications, such as private information retrieval, secure multiparty computation, and circuit lower bounds. Three major parameters are considered in LCCs: query complexity, message length, and codeword length. The most familiar LCCs in the regime of low query complexity are the generalized Reed-Muller (GRM) codes. However, it has not previously been determined whether there exist codes that have shorter codeword lengths than GRM codes with the same query complexity and message length. In this paper, we show that the projective Reed-Muller (PRM) codes are such LCCs for some parameters. The GRM code is specified by the alphabet size q, the number of variables m, and the degree d, where d ≤ q - 2. When d = q - 2 and q - 1 is a power of a prime, we prove that there exists a PRM code with shorter codeword length than the GRM code with the same query complexity and message length. We also present for these PRM codes a perfectly smooth local decoder to recover a symbol in a codeword by accessing not more than q symbols at the coordinates of the codeword.
MQL: Mixed Query Language for Querying MySQL and HBase Databases

During the last twenty years, the amount of stored digital data has witnessed an enormous growth. This data is usually divided by the scientific community into structured and unstructured data. Although unstructured data is becoming a significant part of big data, there are still a large number of data generators that save their data into traditional structured databases. With the advantages that exist in adopting non-structural database systems to save various kinds of data, many companies and institutions are shifting into acquiring and utilizing a professional non-structural DBMS. These organizations find themselves obliged to translate and convert their old structured data into new formats and forms, in order to make a copy of this data in the new system. In order to avoid the consequences of such costly operations, we present the basics of a query language that allows a database user to query structural and non-structural databases at the same time using a single query. Our proposed language allows a database user to view the two databases (structured and non-structured) as a single DB on which a single query is executed. We present a testing of our system by using MySQL and HBase as the structured and non-structured databases.