Reviewerly/exHarmony huggingface dataset:
Classical algorithms for exact search

We kick the book off by looking at classical algorithms for exact search, that is, finding positions in a string where a pattern string matches precisely. This problem is so fundamental that it received much attention in the very early days of computing, and by now, there are tens if not hundreds of approaches. In this chapter, we see a few classics.
Blockwise and Low Density Key Error Correcting Codes

To protect the information from disturbances created by noisy channels, redundant symbols (called check symbols) with the information symbols are added. These extra symbols play important role for the efficiency of the communication system. It is always important to know how much these check symbols are required for a code designed for a specific purpose. In this communication, we give lower and upper bounds on check symbols needed to a linear code correcting key errors of length upto p which are confined to a single sub-block. We provide two examples of such linear codes. We, further, obtain those bounds for the case when key error occurs in the whole code length, but the number of disturbing components within key error is upto a certain number. Two examples in this case also are provided.
Increasing the Level of Reader Interest Among High School Students by Means of New Technologies

In this article, the problem of declining reader interest among high school students is considered. A way of solving this problem is proposed: increasing high school students' interest in reading by using modern technologies. The research methods used in this paper include theoretical (analysis of scientific, non-fiction and fiction literature, followed by analysis and synthesis of obtained data), as well as empirical (a survey in order to determine the reading activity of students, comparison and analysis of obtained data, systematization and statistical processing of research materials) methods. The author has examined several modern information and communication resources, of which those most suitable for the purpose of this study have been selected: an online survey, a poster, a book trailer and an Internet forum. The results of this study demonstrated a positive dynamic in 10th grade students' reader interest, thus confirming the hypothesis.
Zero-Shot Human-Object Interaction Recognition via Affordance Graphs

We propose a new approach for Zero-Shot Human-Object Interaction Recognition in the challenging setting that involves interactions with unseen actions (as opposed to just unseen combinations of seen actions and objects). Our approach makes use of knowledge external to the image content in the form of a graph that models affordance relations between actions and objects, i.e., whether an action can be performed on the given object or not. We propose a loss function with the aim of distilling the knowledge contained in the graph into the model, while also using the graph to regularise learnt representations by imposing a local structure on the latent space. We evaluate our approach on several datasets (including the popular HICO and HICO-DET) and show that it outperforms the current state of the art.
Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images

In recent years, there are many applications of object detection in remote sensing field, which demands a great number of labeled data. However, in many cases, data is extremely rare. In this paper, we proposed a few-shot object detector which is designed for detecting novel objects based on only a few examples. Through fully leveraging labeled base classes, our model that is composed of a feature-extractor, a feature attention highlight module as well as a two-stage detection backend can quickly adapt to novel classes. The pre-trained feature extractor whose parameters are shared produces general features. While the feature attention highlight module is designed to be light-weighted and simple in order to fit the few-shot cases. Although it is simple, the information provided by it in a serial way is helpful to make the general features to be specific for few-shot objects. Then the object-specific features are delivered to the two-stage detection backend for the detection results. The experiments demonstrate the effectiveness of the proposed method for few-shot cases.
MORPHOLOGICAL INTERFERENCE ON STUDENT�S WRITING

The aim of this research is to find out the types of morphological interference on students' English writing. Through qualitative research method applying a design of the case study, twenty of university students were chosen. Data were collected from written tests and classified based on the Politzer and Ramires (1973) on theory of Linguistic Category. The results revealed that, whilst it is inevitable that the English morphology features governed by the distinctive rules and systems have been the potential target for interference taking place, Bahasa Indonesia, which stands as the official language, becomes the source of transfer. From six types of morphological interference based on Polizter and Ramirez� (1973), in this study the writer only found three of them. These types encompass of the morphological properties from the use of article, plural marker and simple past tense. Furthermore, there are 40 morphological mistakes in total, in which four mistakes represent the intralingual transfer and 36 data describe the interference. In terms of the mistakes made as a result mostly from the interference transfer. This study makes a significant contribution to the teaching and learning process of linguistic interference.Keywords: linguistic interference; morphology; students� writing.
Comparison of Orientations to Activities on Exercise Self-efficacy and Indices of Well-being among Physically-motion Disables

Comparison of Orientations to Activities on Exercise Self-efficacy and Indices of Well-being among Physically-motion Disables
Multilevel Image Thresholding Using the Complement Feature

A novel Eigen formulation is proposed for image segmentation.Each pixel is represented by a unit vector having the x-component as the normalized gray value of the pixel.The axes of inertia are simply the Eigen vectors of the auto-correlation matrix.The largest Eigen vector is used as the point of split.Each sub-image is further split in the same way.The ratio of the smallest Eigen value to the sum of Eigen values represents the percentage of the minority and was used to control further splitting.The process continues until no sub-image is larger in size than this ratio.The results are very encouraging on a wide range of images.
MDCN: Multi-scale Dense Cross Network for Image Super-Resolution

Convolutional neural networks have been proven to be of great benefit for single-image super-resolution (SISR). However, previous works do not make full use of multi-scale features and ignore the inter-scale correlation between different upsampling factors, resulting in sub-optimal performance. Instead of blindly increasing the depth of the network, we are committed to mining image features and learning the inter-scale correlation between different upsampling factors. To achieve this, we propose a Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time. MDCN consists of multi-scale dense cross blocks (MDCBs), hierarchical feature distillation block (HFDB), and dynamic reconstruction block (DRB). Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model. It is worth noting that all these modules can run independently. It means that these modules can be selectively plugged into any CNN model to improve model performance. Extensive experiments show that MDCN achieves competitive results in SISR, especially in the reconstruction task with multiple upsampling factors. The code will be provided at https://github.com/MIVRC/MDCN-PyTorch.
Implementation of a Statistical Dialogue Manager for Commercial Conversational Systems

Conversational interfaces have recently become an ubiquitous element in both the personal sphere by improving individual’s quality of life, and industrial environments by the automation of services and its corresponding costs savings. However, designing the dialogue model used by these interfaces to decide the next response is a hard-to-accomplish task for complex conversational interactions. In this paper, we propose a statistical-based dialogue manager architecture, which provides flexibility to develop and maintain this module. Our proposal has been integrated with DialogFlow, a natural language understanding platform provided by Google to design conversational user interfaces. The proposed architecture has been assessed with a real use case for a train scheduling domain, proving that the user experience is of a high value and it can be integrated for commercial setups.
Visualizing information on watch faces: A survey with smartwatch users

People increasingly wear smartwatches that can track a wide variety of data. However, it is currently unknown which data people consume and how it is visualized. To better ground research on smartwatch visualization, it is important to understand the current use of these representation types on smartwatches, and to identify missed visualization opportunities. We present the findings of a survey with 237 smartwatch wearers, and assess the types of data and representations commonly displayed on watch faces. We found a predominant display of health & fitness data, with icons accompanied by text being the most frequent representation type. Combining these results with a further analysis of online searches of watch faces and the data tracked on smartwatches that are not commonly visualized, we discuss opportunities for visualization research.
Prospettive di ricerca sui risarcimenti per le relazioni d’amore: la parabola della seduzione

In the modern age, the stuprum sine vi discipline and the corresponding compensation for seduction damage are legal parts of the patriarchal system, which aims especially to legitimate the children. Both the legislative institutes represent the social attitude towards the relationship between men and women. The historical-legal analysis of these institutes points out that despite the radical changes of the legal institutions, there is a significant continuity between the ancient regime and the modern codified laws.
Geology and Structural Description of Shakrok Anticline; Northern Iraq

The studied area is a part of the Arabian plate located within the High Folded Zone of the Zagros Fold-Thrust Belt in northeastern Iraq (Kurdistan Region). The Study area deals with the Shakrok Anticlines is located between Safin Mountain and Sork Mountain. These structures are formed during the Alpine Orogeny in Cretaceous-Tertiary period. Generally, the folded structures are trending NW-SE direction which is parallel to the main Zagros Orogenic trends.&#x0D; The exposed stratigraphic succession of the studied area that represented by 4 formations deposited from the Early Cretaceous which are Shiranish, Aqrah, Bekhme and Qamchuqa formations. Shakrok Anticline are asymmetrical, double plunging and verging toward northeast. This establishes that Merawa is a Tertiary continuation part of Cretaceous Shakrok Anticline, but there is a deflection in the direction of the fold axis that affected the Merawa Anticline due to the effect of strike slip fault addition to Lineament. Shakrok Anticline with Cretaceous successions formed due to the effect of Cretaceous and Tertiary folding phases. But Merawa Anticline with Tertiary succession that formed due to the effect of Tertiary folding phases.&#x0D; The high stress and intensity of the major fault on the southwestern limb rotated and overturned Tertiary successions and changed its dip toward NE.&#x0D; The differences in fold geometry, fold axis, axial surface, and curvilinear hinge imply that the structure formed as a result of two folding phases &amp; lateral growth of folds that developed by changing the direction of the compressional tectonic processes due to Alpine Orogene of Zagros. The fold axis of Shakrok Anticline rotated 16o in anticlockwise trend from Merawa to Sork anticlines. Because of anticlockwise rotation of the Arabian plate due to its collision with Iranian and Anatolian plates.
Time-Space Tradeoffs for Distinguishing Distributions and Applications to Security of Goldreich's PRG

In this work, we establish lower-bounds against memory bounded algorithms for distinguishing between natural pairs of related distributions from samples that arrive in a streaming setting. In our first result, we show that any algorithm that distinguishes between uniform distribution on $\{0,1\}^n$ and uniform distribution on an $n/2$-dimensional linear subspace of $\{0,1\}^n$ with non-negligible advantage needs $2^{Ω(n)}$ samples or $Ω(n^2)$ memory. Our second result applies to distinguishing outputs of Goldreich's local pseudorandom generator from the uniform distribution on the output domain. Specifically, Goldreich's pseudorandom generator $G$ fixes a predicate $P:\{0,1\}^k \rightarrow \{0,1\}$ and a collection of subsets $S_1, S_2, \ldots, S_m \subseteq [n]$ of size $k$. For any seed $x \in \{0,1\}^n$, it outputs $P(x_{S_1}), P(x_{S_2}), \ldots, P(x_{S_m})$ where $x_{S_i}$ is the projection of $x$ to the coordinates in $S_i$. We prove that whenever $P$ is $t$-resilient (all non-zero Fourier coefficients of $(-1)^P$ are of degree $t$ or higher), then no algorithm, with $
Technology for Trusted Cloud Security: Survey and Open Issues

Trusted execution technology is increasingly successful in heterogeneous fields aiming at securing the execution of code and access control to premium content, though some criticalities associated with such technologies start becoming apparent. Among other stakeholders, the Cloud Security Alliance, whose mission is to promote the use of best practices for providing security assurance within cloud computing, offers cloud providers and clients with security models and tools that ease security management. This chapter highlights promising technology such as containers and their security aspects. It surveys trusted computing technologies, highlighting the pros and cons of established technologies and novel approaches, as well as the security issues that such approaches introduce ex novo or simply exacerbate. The chapter surveys some relevant, trusted computing environment solutions, such as SGX and containers. It shows how the cloud can make use of the analyzed trusted execution technology to help secure the execution of code and protect access to data.
Fully Projection-Free Proximal Stochastic Gradient Method With Optimal Convergence Rates

Proximal stochastic gradient plays an important role in large-scale machine learning and big data analysis. It needs to iteratively update models within a feasible set until convergence. The computational cost is usually high due to the projection over the feasible set. To reduce complexity, many projection-free methods such as Frank-Wolfe methods have been proposed. However, those projection-free methods have to solve a linear programming problem for every update of models which still leads to high computational cost for a complex feasible set, and can be unbearable in practical scenarios. Motivated by this problem, we propose a fully projection-free proximal stochastic gradient method, which has two advantages over previous methods. First, it enjoys high efficiency. The proposed method does not conduct projection directly but finds an approximately correct projection point with a very low computational cost. Second, it achieves tight and optimal convergence rates. Our theoretical analysis shows that the proposed method achieves convergence rates of O(1/√T) and O(log T/T) for convex and strongly convex functions, respectively. These convergence rates successfully match with the known lower bounds. Therefore, in this paper, we provide a valuable insight that some loss of accuracy of projection can improve the efficiency significantly, but does not impair convergence rates. Finally, empirical studies show that the proposed method achieves more than 5× speedup than previous methods.
Reference Class-Based Improvement of Object Detection Accuracy

<p class='IJASEITTitle'>To date, the Frames Per Second (FPS) and accuracy of object detection based on deep learning have made rapid progress. However, the accuracy is limited by issues such as false positive (FP) cases. FP cases can trigger malfunctions in applications requiring high accuracy, such as in autonomous vehicles, where it is essential to ensure driver safety when malfunctions occur. To reduce the occurrences of FP cases, we conducted an experiment to derive the association by separately detecting a highly relevant element called a reference class, in addition to the target class to be detected. To measure the association, we obtained the integrated association by first finding the associations between the bounding boxes of the target and reference classes. Then we generated a reference class-based model by applying the integrated association to a trained model. The reference class-based model achieved approximately 15% higher accuracy than the trained model at iteration 1,000. Besides, the proposed model reduced the FP cases to approximately half of the 18.964% in the conventional method; the FP reduction through an increase in iteration was only 11.008%. The reference class can be applied in various fields, such as security and autonomous vehicle technology. It can be used to reduce the FP cases and improve the accuracy performance limits in object detection. Furthermore, it is possible to reduce the cost of reinforcing the training dataset and using high-performance hardware, and the time cost of increasing training numbers.
Block Randomized Singular Value Decomposition on GPUs

Fast computation of singular value decomposition (SVD) is of great interest in various machine learning tasks. Recently, SVD methods based on randomized linear algebra have shown significant speedup in this regime. For processing large-scale data, computing systems with accelerators like GPUs have become the mainstream approach. In those systems, access to the input data dominates the overall process time; therefore, it is needed to design an out-of-core algorithm to dispatch the computation into accelerators. This paper proposes an accurate two-pass randomized SVD, named block randomized SVD (BRSVD), designed for matrices with a slow-decay singular spectrum that is often observed in image data. BRSVD fully utilizes the power of modern computing system architectures and efficiently processes large-scale data in a parallel and out-of-core fashion. Our experiments show that BRSVD effectively moves the performance bottleneck from data transfer to computation, so that outperforms existing randomized SVD methods in terms of speed with retaining similar accuracy.
A Review on Booth Encoded Multiplier

Arithmetic operations are performed in a wide range of computer processing. Obviously, lots of the so-called computer processing operations are involved with for main mathematical operations containing addition, subtraction, division, and multiplication. As a comparison, this can be noted that, division and multiplication operations make much more challenges than addition and subtraction operations that are because of their complexity, needed hardware units and employed clock cycles. Huge numbers appear in some of the computing and mathematical CPU jobs thus performing the mathematical operations over them will be created high complexity in both the time and space cases. So the speed of the operation falls down in parallel with growing the size of the digits. Also, the needed storage space will increase rapidly when the size of the digits grows up. In the multiplication operation, different radixes can be applied to multiplier and multiplicand: Radix-2, Radix-4, Radix-8, Radix-16, etc. Multiplication operation would be performed in n CPU clock cycles when the n-bit multiplier and multiplicand are represented in radix 2 notation. This means radix-2 multiplication handles one bit of multiplier in each cycle.
Deep unsupervised learning for Microscopy-Based Malaria detection

Malaria, a mosquito-borne disease caused by a parasite, kills over 1 million people globally each year. People, if left untreated, may develop severe complications, leading to death. Effective and accurate diagnosis is important for the management and control of malaria. Our research focuses on utilizing machine learning to improve the efficiency in Malaria diagnosis. We utilize a modified U-net architecture, as an unsupervised learning model, to conduct cell boundary detection. The blood cells infected by malaria are then identified in chromatic space by a Mahalanobis distance algorithm. Both the cell segmentation and Malaria detection process often requires intensive manual label, which we hope to eliminate via the unsupervised workflow.
A Metamodel and Framework for AGI

Can artificial intelligence systems exhibit superhuman performance, but in critical ways, lack the intelligence of even a single-celled organism? The answer is clearly 'yes' for narrow AI systems. Animals, plants, and even single-celled organisms learn to reliably avoid danger and move towards food. This is accomplished via a physical knowledge preserving metamodel that autonomously generates useful models of the world. We posit that preserving the structure of knowledge is critical for higher intelligences that manage increasingly higher levels of abstraction, be they human or artificial. This is the key lesson learned from applying AGI subsystems to complex real-world problems that require continuous learning and adaptation. In this paper, we introduce the Deep Fusion Reasoning Engine (DFRE), which implements a knowledge-preserving metamodel and framework for constructing applied AGI systems. The DFRE metamodel exhibits some important fundamental knowledge preserving properties such as clear distinctions between symmetric and antisymmetric relations, and the ability to create a hierarchical knowledge representation that clearly delineates between levels of abstraction. The DFRE metamodel, which incorporates these capabilities, demonstrates how this approach benefits AGI in specific ways such as managing combinatorial explosion and enabling cumulative, distributed and federated learning. Our experiments show that the proposed framework achieves 94% accuracy on average on unsupervised object detection and recognition. This work is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski's general semantics.
FunMap: Efficient Execution of Functional Mappings for Knowledge Graph  Creation

Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions -- specified with function-based mapping languages like FunUL and RML+FnO -- can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.