Reviewerly/exHarmony huggingface dataset:
Lane Compression

This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data. The proposed technique profiles machine learning data gathered ahead of run-time and partitions values bit-wise into different lanes with more distinctive statistical characteristics. Then the most appropriate compression technique is chosen for each lane out of a small number of low-cost compression techniques. Lane Compression’s compute and memory requirements are very low and yet it achieves a compression rate comparable to or better than Huffman coding. We evaluate and analyse Lane Compression on a wide range of machine learning networks for both inference and re-training. We also demonstrate the profiling prior to run-time and the ability to configure the hardware based on the profiling guarantee robust performance across different models and datasets. Hardware implementations are described and the scheme’s simplicity makes it suitable for compressing both on-chip and off-chip traffic.
The Applicability of Authorship Verification to Swedish Discussion Forums

The authorship verification problem of determining whether two collections of textual content have been written by the same author or not is relevant in several contexts, e.g., when law enforcement officers try to find out whether a suspect with a known user account has other user accounts in the same or other web forums. In this paper, we evaluate how well the recently suggested attention-based hierarchical neural network approach AdHominem works and if it can be used to link user accounts on Swedish discussion forums. The results are encouraging and show that using AdHominem is a promising way forward when linking user accounts both on the same discussion forum and in cross-domain settings in which users write on a large variety of topics.
Data Mining and Its Contribution to Decision-Making in Business Organizations

Data mining (DM) has emerged in response to the need for organizations to find a way to take advantage of the large data stored in their databases and repositories, from which traditional analysis methods are unable to extract useful information from them. This technology, based on intelligent deductive algorithms, allows the conversion of a huge amount of raw data into meaningful information, and into new knowledge, which is commonly used to support decision-making. Therefore, this chapter seeks to provide a theoretical study on how this technology contributes to support decision-making in business organizations. This is to sensitize organizations and practitioners of the advantages of this technology is adopted and arouse the curiosity of scholars and researchers to enrich the subject by studies and research, especially in economic and administrative sciences. 68To reach clear results, the descriptive and analytical approach was adopted in dealing with the subject by reviewing the articles, studies, and reports related to the research problem. These references were used in determining the basic theoretical concepts related to DM and their assumed role in the decision-making process. A practical case was also noted on the application of the technology by looking at the status of Dubai Airports. This research paper concluded that this smart technology works to search for relationships, trends, and patterns hidden in databases, to be used in building models of prediction, and in exploring the behavior of individuals, and in determining their general trends. This useful information is used in several areas, such as creating a competitive advantage that the organization focuses on, or improving the performance of its services. The systematic and orderly use of this technology also makes the organization’s information system an integrated system that discovers shares, and distributes knowledge. This allows the provision of accurate and rapid information, which contributes to better decision-making, especially on how to increase profits or reduce costs. This research concluded that Dubai Airports Corporation has been able to benefit from the applications of this technology, not only in supporting the decision-making process but also in creating a competitive advantage, improving services, reducing costs, and raising its overall performance.
The Linguistic and Situational features of WhatsApp Messages

&lt;p&gt;WhatsApp messages can be such a rich source for creative and spontaneous language geared towards more individual expression. This study aims to explore the linguistic features of text messaging communicative intent, content and context. 100 different texts from a high school student population in Canada were collected and analyzed using Biber and Conrad’s (2009) qualitative approach to register, genre, and style analysis. The result is that many people use a lot of clipped sentences in a free flow of casual speech. It is concluded that while it is true that a lot of common abbreviations are used and have become standard, there is a lot of individualistic variance in terms of style and language usage. &lt;/p&gt;
Resistant Blockchain Cryptography to Quantum Computing Attacks

Due to the need to maintain confidentiality, redundancy, and openness, the usage of Blockchain and other DLTs has dramatically advanced in recent years, and is being recommended for various applications. In blockchain, these capabilities are supplied by means of hash functions and public-key encryption. However, the rapid development of quantum computation in the near future has opened the door to the Grover and Shor algorithms. These algorithms challenge both public and hash encryption, causing blockchains to redesign and use quantum attack-tolerant cryptosystems; this produces cryptosystems which are considered post-quantum cryptosystems, which are quantum-resistant. This paper reviews current scientists on quantum blockchain for such purposes. In addition, the major challenges are studied with the most important post-quantum blockchain systems. In addition, the most promising post quantum signature encryption and digital blockchain signature schemes are detailed in terms of the functionality and durability of the most promising public signatures. In this article, researchers and developers in blockchain have an extensive perspective and practical advice on post-quantum blockchain protection.
A Study of Game Payment Data Mining: Predicting High-Value Users for MMORPGs

A user is high-value when his payment amount is more than a predetermined amount (such as 100 dollars). We first analyze payment data under the character level (CLV) dimension of six real mobile MMORPGs and verify the importance of high-value users for achieving game profit and the necessity of HU prediction. Afterward, we propose a CLV-based high-value user prediction (CLVHUP) model that solves limitations of the existing method (delayed identification, the uncertainty of the predetermined amount, and the inflexible time window). This model not only can predict high-value users with sparse features and a small volume of users but is also sensitive to the information underlying CLVs. To the best of our knowledge, this is the first work to analyze payment behavior under the CLV dimension and to predict high-value users by utilizing machine learning techniques and exploring the information underlying CLV. Comprehensive experimental results demonstrate that the performance of our solution is better than that of several baselines.
Scalable Statistical Inference of Photometric Redshift via Data Subsampling

Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform other models in quantifying uncertainties associated with model predictions. We develop a data-driven statistical modeling framework that combines the uncertainties from an ensemble of statistical models learned on smaller subsets of data carefully chosen to account for imbalances in the input space. We demonstrate this method on a photometric redshift estimation problem in cosmology, which seeks to infer a distribution of the redshift -- the stretching effect in observing the light of far-away galaxies -- given multivariate color information observed for an object in the sky. Our proposed method performs balanced partitioning, graph-based data subsampling across the partitions, and training of an ensemble of Gaussian process models.
Simple Automated Narrative Generator (SANG)

In this paper, we propose a planning-based story generation system SANG. SANG addresses some of the most important aspects of the story, causality, coherence, consistent characters, and emotions. For it, we built an action ontology representing causal information of events, and we used it to generate a plot between a pair of events. We then extend it to generating a plot between more events. We finally discuss how SANG uses the natural language generation to deliver the story in English. In the Turning Test performed over 106 participants, 64.1% of participants confidently believed that the story was written by a human being, higher than the baseline story generators.
Certain Investigation on Load Balancing using Cloudlet Assignment and min-max Algorithm

Cloud Computing is a trending technology where users can access the resources that run on servers.Cloud usage has major issues like scheduling and load balancing. As cloud users increase, there is an increase in computation costs, decrease in performance, and network traffic. This issue is quite common in most of the servers. Load balancing is the process of balancing overloads or underloads of the required server. It is required to design a load balancer that has the least CPU utilization time for the allocation and utilization of resources. This paper solves the issue of load balancing using machine learning algorithms thereby the means to optimize it. The future system reduces the waiting period and the energy depletion of the data centres in the cloud by 43.57%.
COMPARISON OF COLORIMETRIC-BASED RABIES POSTVACCINATION ANTIBODY TITER DETECTION USING ELISA READER AND MOBILE PHONE CAMERA

Rabies is an infectious disease, zoonotic, caused by virus from the genus Lyssa virus and generally transmitted by the bite of rabid animal, especially rabies infected dog. Rabies is preventable but is always fatal to humans if the central nervous system (CNS) is infected. Vaccination has been used as one of rabies prevention programmed. A total of 83 samples were tested using an Indirect ELISA (Enzyme Linked Immunosorbent Assay) to identify post-vaccination rabies antibody titer. Antibody titres correlated with absorbance values and standard solutions concentrations. Absorbance value can be determined using ELISA reader and mobile phone camera. Absorbance were read at 450 nm and 620 nm as reference using ELISA reader and image from mobile phone camera using image processing software (ImageJ). The aim of this study is to compared between ELISA reader as gold standard and mobile phone camera through validity testing such sensitivity, specificity, and accuracy. There is no significant difference between gold standard and alternative test equipment. The mobile phone camera has sensitivity 98,6%, specificity 88.8 % and accuracy 97,5%. The image processing method using ELISA reader is relatively expensive and difficult to hold in laboratory with minimum funds. Image processing method using a mobile phone camera with ImageJ application is expected to be an alternative tool to read the result of ELISA.
The Application Of Augmented Reality in Introducing Temples in Denpasar

Over time, the situation has changed the mindset of the Hindu community, especially in Bali in carrying out their worship. People no longer come to the temple just to pray and go home, but they wanted to know more about the temple itself. Due to the influence of the current era which quickly eroded historical existence, many were interested in knowing information about the temples they visited. However, this information does not just exist in a temple. The, specific information is stored in the literature and part of it is known by the temple administrators so that the public must look for it to find out. Due to lack of references and time constraints, people often give up their intention. By using augmented reality (AR) technology this problem can be overcome. AR technology is able to project virtual objects as if they look real to the human eye. In this case, AR is used to display the temple model and information with interactive illustrations to help the community recognize the temple. Users only need to install the application on their smartphone device and scan the QR code provided, then the temple as an object shows up and information can be displayed immediately. This application applies multi-target marker-based AR so that each temple information is stored and ran on different markers. Based on testing using the blackbox method, the results show that the application can run smoothly on several different devices both in terms of device resolution, memory speed and processor.
The information unit of topic: a crosslinguistic, statistical study based on spontaneous speech corpora

This study, carried out within the framework of the Language into Act Theory (L-AcT), constitutes a statistically based, crosslinguistic analysis of the information unit of Topic (TOP), whose function is to supply a cognitive domain for the interpretation of the Comment (COM), the information unit that conveys the illocutionary force. We looked at data from spontaneous speech corpora of Italian (IT), European Portuguese (EP), Brazilian Portuguese (BP), and American English (AE), all of which collected in accordance with the tenets and methods established by L-AcT, thus recording a variety of formal and informal communicative situations, prosodic segmentation, and text-to-speech alignment at the utterance level. Some of these linguistic resources also feature annotation of informational functions, particularly the IT, BP and AE minicorpora. The last two of these have been revised as part of the preparation of the data for this study. We conducted a critical review of some of the most prominent approaches to information structure in general, dedicating more attention to the term topic and the different meanings it has depending on the framework considered. We conducted a kappa test to establish the degree of agreement among four raters in a task of TOP annotation based on data from the C-ORAL-BRASIL II corpora. We used Functional Data Analysis (FDA) and Functional Principal Component Analysis (F-PCA) to verify whether the prosodic forms of TOP proposed in previous studies are separable and to propose statistical models for each of the forms. In addition, we used Analysis of Variance (ANOVA) to determine whether the nuclear and non-nuclear syllables of TOP are significantly different from each other in terms of duration. Our study has shown that the interrater agreement for the detection of TOP in spontaneous speech is substantial. Furthermore, it has provided compelling statistical evidence in support of the classification scheme for the prosodic forms of TOP proposed in previous studies, which are lacking in statistical validation. In addition, the study of syllable durations has shown that the nuclear syllables of TOP are significantly longer than the non-nuclear ones. Finally, the melodic curves of TOP were compared to those of Bound Comments (COB), which constitute illocutionary units that feature a prosodic signal of continuity. This was done using the FDA and F-PCA techniques with the aim to show that TOP is not simply a simply a bearer of a continuity signal. The analysis showed that the f0 curves of COB are separable from those of TOP, and in those cases where there is some similarity in the shape of TOP and COB curves, the number of nuclear syllables as well as their position within the unit clearly distinguish these two types of information unit.
Soft Sequence Heaps.

Chazelle [JACM00] introduced the soft heap as a building block for efficient minimum spanning tree algorithms, and recently Kaplan et al. [SOSA2019] showed how soft heaps can be applied to achieve simpler algorithms for various selection problems. A soft heap trades-off accuracy for efficiency, by allowing $\epsilon N$ of the items in a heap to be corrupted after a total of $N$ insertions, where a corrupted item is an item with artificially increased key and $0 < \epsilon \leq 1/2$ is a fixed error parameter. Chazelle's soft heaps are based on binomial trees and support insertions in amortized $O(\lg(1/\epsilon))$ time and extract-min operations in amortized $O(1)$ time. In this paper we explore the design space of soft heaps. The main contribution of this paper is an alternative soft heap implementation based on merging sorted sequences, with time bounds matching those of Chazelle's soft heaps. We also discuss a variation of the soft heap by Kaplan et al. [SICOMP2013], where we avoid performing insertions lazily. It is based on ternary trees instead of binary trees and matches the time bounds of Kaplan et al., i.e. amortized $O(1)$ insertions and amortized $O(\lg(1/\epsilon))$ extract-min. Both our data structures only introduce corruptions after extract-min operations which return the set of items corrupted by the operation.
P-Tree oriented Association Rule mining of Multiple data sources

A prominent research area in data mining field is (ARM). As distributed databases emerged, need to mine different patterns across them become necessary and hence distributed ARM algorithms were desired. But these algorithms increased communication complexity and overhead. This paper proposes a new algorithm P-tree oriented distributed association rule mining (PDAM) for mining association rules from distributed databases. This algorithm enables a quicker computation of support counts of item sets. P-tree, a special kind of data structure is used in the algorithm which holds the transactional data. These tree data structures do effective storage of data by employing lossless compression techniques. Message exchange optimisation is proposed in this paper. Both the database scans as well as message exchanges are reduced by the proposed method. It would also reduce the size of average transactions, data sets and message exchanges.
SCIENTIFIC RESEARCH ACTIVITY OF MASTERS IN THE SPECIALTY «LIBRARY AND INFORMATION ACTIVITY»

The article describes topics of master's theses in the field of «Library and Information Activities» at the Kemerovo State Institute of Culture. The authors pay special attention to the presented methods of scientific research. Fifty-three research methods have been identified and divided into three groups: theoretical, empirical and methods of processing and visualizing of empirical information.
Quantum Key Distribution: Attacks and Solutions

Communication in the present information era needs to tread beyond the scopes of codes to provide security. If on one hand advances in encryption technology has made progress, the sanctity of conventional methods has also been breached time and again on the other. In search of a better security providing methodology, algorithms involving the principles of Quantum Key Distribution (QKD) have shown promising results. They use laws of physics to provide unprecedented information security while rendering a wide scope of being the next big breakthrough in the field. While QKD is subjected to ongoing research, several vulnerabilities have been encountered for its algorithms which need to be addressed with strong countermeasures, thereby making QKD practically robust for wide-range applications.
Research on the Construction of Library Reference Service System under the New Situation

By analyzing the current situation of the development of new technologies and the challenges faced by traditional library reference services, this article analyzes the status of library reference services. On this basis, the article considers the reference service system of the library with the content, ways and methods. At the end of the article, a series of safeguard measures are proposed in terms of thinking mode, innovative technology and management mode.
High-Accuracy Crowd Counting Method Based on Mixed Labeled Dataset

Existing crowd counting technology mainly uses one single detection model or density map regression model to obtain the number of people in an image, but a single model is difficult to adapt to different camera position, viewing angle, resolution, and different crowd density. We believe that fusion of two different methods for prediction can greatly improve the accuracy of prediction in various situations. In this paper, we propose a method that improve the existing counting method of detection counting method, and then develop a network to fuse the results of regression prediction and detection prediction, so that not only can we make full use of the existing different labeled dataset but also the network generalize stronger by fusing completely two model and the counting accuracy will be higher. Experiments show that with the same model and parameters, the counting accuracy of the method proposed in this paper is much higher than any single model.
A new improved Approach for Feature Generation and Selection in multirelationalstatistical modelling using machine learning

Multi-relational classification is highly challengeable task in data mining, becauseso much data in our world is organised in multiple relations. The challenge comes from thehuge collection of search spaces and high calculation cost arises in the selection of featuredue to excessive complexity in the various relations. The state-of-the-art approach is based onclusters and inductive logical programming to retrieve important features and derivedhypothesis. However, those techniques are very slow and unable to create enough data andinformation to produce efficient classifiers. In the given paper, we proposed a fast andeffective method for the feature selection using multi-relational classification. Moreover weintroduced the natural join and SVM based feature selection in multi-relation statisticallearning. The performance of our model on various datasets indicates that our model isefficient, reliable and highly accurate.
Quantum Fourier Transform and Its Applications

When in 1822 Fourier published his most famous article (work), people originally used his transform in thermodynamics. Now, the most common use for the Fourier transform is in signal processing. A signal is given in the time domain: as a function mapping time to amplitude.
INTERACTIVE STRUCTURE OF LIBRARY COLLECTION ACQUISITION

The role of automation in the library collection acquisition procedure is considered. An interactive structure of acquisition of the library fund is proposed, its functions are revealed.
Illumination Inconsistency Reduction in Video Frames using DSIHE with Kekre’s LUV Color Space

The significance of quality in visual perception of video frames is inevitable. The video sequences have become ubiquitous in nature and there is a dire need for accurate interpretability of these video frames. The background model plays an instrumental role in excellent visual perception. Illumination variations in the video sequences is a concern and a challenging issue as primary applications being object detection and video surveillance gets affected. In the proposed work, the Histogram variation, Dualistic Sub-Image Histogram Equalization (DSIHE) with Kekre's LUV color space has been experimented for illumination inconsistency reduction with over bright and darker set of video frames and substantial average entropy of 7.4898 has been obtained.