Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Plasmodium for Malaria Diagnosis based on Ensemble Neural Network

Malaria is one of the important public health issues of global concern. It is a kind of infectious disease caused by Plasmodium which can endanger human life and health. The examination of Plasmodium blood smear is the main method to diagnose malaria. Applying machine learning method to automatically analyze malaria smear images is very important for rapid diagnosis and surveillance of malaria. However, the existing machine learning methods need further improvement in feature extraction and generalization ability. For this reason, this paper introduces an ensemble neural network to automatically learn more accurate image features and achieve automatic classification of malaria images. In addition, we propose an adaptive threshold control method for cell segmentation, and then put the cell as center to extract images to obtain the training dataset, which solves the noise problem caused by sliding window cutting. And the idea of transfer learning is applied to solve the problem of shortage of training data. We additionally apply the proposed method to malaria image recognition and achieve 94.58% accuracy. The experimental results show that the model has good robustness and generalization ability and provides a valid data processing method for clinical malaria rapid diagnosis application.
Application of K-Means ++ Algorithm Based on t-SNE Dimension Reduction in Transformer District Clustering

The classification of transformer districts is very important for line loss evaluation, load characteristic analysis and so on. In this paper, a Clustering technique of K-Means++ based on t-SNE dimension reduction is proposed and realized by programming. Firstly, the original matrix representing the electrical characteristic index of the transformer district are established. Secondly, the original normalized matrix is reduced by t-SNE dimension reduction method to obtain the main characteristic matrix. After that, K-Means++ is used to cluster the main characteristic matrix. Then, the accuracy of clustering results is evaluated by Silhouette Coefficient (SC) and Calinski-Harabaz (CH) index. Finally, selecting 1959 transformer districts as an example, simulation and calculation are performed to verify the effectiveness and practicable of the K-Means++ algorithm based on t-SNE dimension reduction in transformer district clustering.
The subformula property of natural deduction derivations and analytic cuts

Abstract In derivations of a sequent system, $\mathcal{L}\mathcal{J}$, and a natural deduction system, $\mathcal{N}\mathcal{J}$, the trails of formulae and the subformula property based on these trails will be defined. The derivations of $\mathcal{N}\mathcal{J}$ and $\mathcal{L}\mathcal{J}$ will be connected by the map $g$, and it will be proved the following: an $\mathcal{N}\mathcal{J}$-derivation is normal $\Longleftrightarrow $ it has the subformula property based on trails $\Longleftrightarrow $ its $g$-image in $\mathcal{L}\mathcal{J}$ is without maximum cuts $\Longrightarrow $ that $g$-image has the subformula property based on trails. In $\mathcal{L}\mathcal{J}$-derivations, another type of cuts, sub-cuts, will be introduced, and it will be proved the following: all cuts of an $\mathcal{L}\mathcal{J}$-derivation are sub-cuts $\Longleftrightarrow $ it has the subformula property based on trails.
A semantics of intentional silence in omissive implicature

When people communicate, we often face situations where decisions have to be made, regardless of silence of one of the interlocutors. That is, we have to decide from incomplete information, guessing the intentions of the silent person. Implicatures allow to make inferences from what is said, but we can also infer from omission, or specifically from intentional silence in a conversation. In some contexts, not saying p generates a conversational implicature: that the speaker did not have sufficient reason, all things considered, to say p. This behaviour has been studied by several disciplines but barely touched in logic or artificial intelligence. After reviewing some previous studies of intentional silence and implicature, we formulate a semantics with five different interpretations of omissive implicature, in terms of the Says() predicate, and focus on puzzles involving assertions or testimonies, to analyze their implications. Several conclusions are derived from the different possibilities that were opened for analysis after taking into account silence. Finally, we develop a general strategy for the use of the proposed semantics in cases involving some kind of silence.
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing

Traditional Visual Question Answering (VQA) datasets typically contain questions related to the spatial information of objects, object attributes, or general scene questions. Recently, researchers have recognized the need to improve the balance of such datasets to reduce the system's dependency on memorized linguistic features and statistical biases, while aiming for enhanced visual understanding. However, it is unclear whether any latent patterns exist to quantify and explain these failures. As an initial step towards better quantifying our understanding of the performance of VQA models, we use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs. Each KG describes the reasoning abilities needed to arrive at a resolution, and failure to resolve gaps indicates an absence of the required reasoning ability. After identifying KGs for each question, we examine the skew in the distribution of questions for each KG. We then introduce a targeted question generation model to reduce this skew, which allows us to generate new types of questions for an image.
Transcripts DApp—A Blockchain-Based Solution for Transcript Application

There is a need for a reliable system with an efficient and simplified process for academic transcript application and procurement along with sufficient measures for authentication of issued transcripts and verification of the integrity of transcripts. The work presented in this paper proposes to create a blockchain-based decentralized application which can be used by students for application of transcripts, which can only be approved and issued by the intended institute, and by universities for the verification of the transcripts issued. The proposed system attempts to leverage the immutability and transparency of blockchain and versatility of programming provided by smart contracts to ensure that there is no avenue for alteration or forgery of transcripts.
QIM-Based Semi-Fragile Watermarking for H.264 Video Authentication

The paper presents a semi-fragile watermarking system designed for H.264 video authentication. The proposed system embeds a semi-fragile watermark into integer DCT coefficients of each keyframe of a video at the H.264 quantization step. The system localizes modified video regions of size 4×4 with close to 95% accuracy. The experimental part demonstrates the influence of the embedding process on the visual quality of the protected video and the watermark extraction error for different H.264 quality parameter values.
Occluded Visual Object Recognition Using Deep Conditional Generative Adversarial Nets and Feedforward Convolutional Neural Networks

Core object recognition is the task of recognizing objects without regard to any variations in the conditions like pose, illumination or any other structural modifications. This task is solved through the feedforward processing of information in the human visual system. Deep neural networks can perform like humans in this task. However, we do not know how object recognition under more challenging conditions like occlusion is solved. Some computational models imply that recurrent processing might be a solution to the beyond core object recognition task. The other potential mechanism for solving occlusion is to reconstruct the occluded part of the object taking advantage of generative models. Here we used Conditional Generative Adversarial Networks for reconstruction. For reasonable size occlusion, we were able to remove the effect of occlusion and we recovered the performance of the base model. We showed getting the benefit of GANs for reconstruction and adding information by generative models can cause a better performance in the object recognition task under occlusion.
Model of the Operating Device with a Tunable Structure for the Implementation of the Accelerated Deductive Inference Method

It is proposed to use the first-order predicate logic to represent knowledge in the selected subject area. The accelerated parallel inference method based on the disjuncts division operation is taken as a method for processing knowledge. To analyze the functioning of the abstract inference engine operating device, a model of logical-flow computing is used. Software implementation model of this device allows you to explore the possibilities of improving the performance of inference mechanisms and evaluate the effectiveness of various configurations of the executive part. Based on the analysis of the conducted experiments, formulas and recommendations for users on the choice of the operating device optimal structure are proposed, taking into account the existing features of specific applied tasks.
Natural Language Processing based Visual Question Answering Efficient: an EfficientDet Approach

This research paper is intended to propose a technique to solve the task of image-based question answering using EfficientDet and Bidirectional LSTM (BiLSTM). Visual Question Answering (VQA) requires a combination of Image recognition and Natural Language Processing (NLP) techniques. The proposed technique uses EfficientDet for image processing and BiLSTM for question processing. The model acts efficiently because of efficient image processing. The model takes an image and a question as input processes them independently, then fuses the processed versions and predicts the answer to the question. It outputs the result in an open domain based on input questions on the image. This study intends to help the visually impaired person. The model achieves similar performance as its contemporary models on the benchmark VQA dataset.
Media Streaming in Cloud with Special Reference to Amazon Web Services: A Comprehensive Review

Media Streaming is handling, consuming and saving multimedia content, such as audio, video and picture, using resources and software available in the cloud without manually gaining them. It is a data processing method so that it can be interpreted as a continuous stream. Cloud consumers can use their laptops or mobile devices to stream media content and enjoy music anywhere and at any time. The main objective is to give consumers uninterrupted streaming of videos with no buffering problems and to conserve their resources and furnish them with great quality and high bandwidth videos. AWS provides you with the mobility that implies you don't have to think about computation, encoding, storing data, data security and credibility. It immediately expands the cloud servers if information size rises. It even provides free-tier access to AWS console for a year in the beginning to allow the use of AWS services simple. And that is why Amazon Web Service is the perfect cloud service supplier in the entire globe. A detailed review of the Media Streaming and services of Amazon Web Service is done together.
A glance at digital forensic academic research demographics

Whilst the field of digital forensics is now well established, its research community can be considered relatively emerging in comparison to the associated areas of traditional forensic and computer sciences. As a result, this comment article takes a quick look at the demographics of digital forensics research over the last 20 years, with metadata from 6589 articles being extracted and analysed from Scopus in order to provide a brief insight into this field's research activity.
Vector Representation of Bengali Word Using Various Word Embedding Model

To transfer human understanding of language to a machine we need word embedding. Skipgram, CBOW, and fastText is a model which generate word embedding. But finding pretrained word embedding model for the Bengali language is difficult for researchers. Also, training word embedding is time-consuming. In this paper, we discussed different word embedding models. To train those models, we have collected around 500000 Bengali articles from various sources on the internet. Among them, we randomly chose 105000 articles. Those articles have 32 million words. We trained them on SkipGram and CBOW model of Word2Vec, fastText. We also trained those words in Glove model. Among the all result fastText (Word2Vec) gave us a satisfactory result.
A Simple Guide to Implement Data Retrieval through Natural Language Database Query Interface (NLDQ)

Natural Language Database Query (NLDQ) Processing is to make the system able to understand queries in natural language like in English, French or any other language sentence, which is to be interpreted by the system and a corresponding action triggered on the underlying database. Asking queries or questions to databases in natural language provides the ease to the user to access and retrieve data, especially for those who are not comfortable with formal query language such as SQL. This paper presents a model that allows users to interact with the database in natural language (in English language) and retrieve information from the relational database. The method is based on the literals of the sentence. This proposed interface allows users to ask queries or questions in natural language (English), which will be transformed into formal query by the system itself, i.e. SQL, which will fire over the underlying Database. The task of NLDQ is to transform the natural language query or question into formal Query Language Statement for information access and retrieval. This task requires the parsing of the input with syntactic understanding by the system. Then the parsed data with syntactic comprehension can be combined with relational database theories for extract the contextual meaning from the query and transforming it into formal database query statement that returns the required information from the associated database. This proposed method does not require all language specifications and grammar rules in the input query.
Leveraging Quantum Computing for Supervised Classification

Enhancing quantum computing for supervised machine learning is an innovative application in the field of smart computing. With recent advancements in quantum computing, and it is rising to coalesce with Artificial Intelligence, quantum computers can revolutionize the way to address previously untenable problems. As quantum computers can succeed in producing various intuitive patterns that are strenuous for a classical system to implement, it is reasonable to presume that these quantum machines can outperform a classical computer in various tasks. They can excel at solving problems which involve data crunching with a huge amount of inputs such as machine learning tasks, complex optimization problems, communication system analysis, etc. which require complex parallel computations for an efficient result. This paper attempts to analyze one such aspect of machine learning known as supervised classification with the help of a real Quantum hardware.
Generic Metadata Time Carving

Recovery of files can be a challenging task in file system investigations, and most carving techniques are based on file signatures or semantics within the file. However, these carving techniques often only recover the files, but not the metadata associated with the file. In this paper, we propose a novel, generic approach for carving metadata by searching for equal and co-located timestamps. The rationale is that there are some common metadata for files and directories within each file system. Our generic time carver provides potential timestamp locations for repeated timestamps in each metadata structure, identifying potential metadata for files. A semantic parser then filters the results with respect to the specific file system type. In our experiments, extraction of MFT entries in NTFS and inodes in Ext4 had near perfect precision for metadata entries with multiple equivalent timestamps, and for such metadata structures we obtained perfect recall for NTFS. For known file systems, we use the information found within identified metadata to recover files, and by recovering files and their associated metadata we increase the evidential value of recovered files.
Ant Colony Optimization Implementation for Reversible Synthesis in Walsh-Hadamard Domain

Reversible circuits are one of the technologies that can provide future low energy circuits. The synthesis of an optimal reversible circuit for a given function is an np-hard problem. The meta-heuristic approaches are one of the most promising methods for these types of optimization problems. In this paper, a new approach for ACO reversible synthesis is presented. Usually, authors build an ACO system with the use of truth table or permutation representation of the reversible function. In this work, a Walsh spectral representation of a Boolean function is used. This allows dividing search spaces into smaller "promising" areas with well-defined transition operations between them. As a result, we can minimize the enormous search space and generate better solutions than obtained by ACO synthesis with classical reversible function representation. The proposed approach was applied to benchmark reversible functions of 4,5 and 6 variables and compared to other meta-heuristic results and best-known solutions.
Deeper Weight Pruning without Accuracy Loss in Deep Neural Networks

This work overcomes the inherent limitation of the bit-level weight pruning, that is, the maximal computation speedup is bounded by the total number of non-zero bits of the weights and the bound is invariably considered "uncontrollable" (i.e., constant) for the neural network to be pruned. Precisely, this work, based on the canonical signed digit (CSD) encoding, (1) proposes a transformation technique which converts the two’s complement representation of every weight into a set of CSD representations of the minimal or near-minimal number of essential (i.e., non-zero) bits, (2) formulates the problem of selecting CSD representations of weights that maximize the parallelism of bit-level multiplication on the weights into a multi-objective shortest path problem and solves it efficiently using an approximation algorithm, and (3) proposes a supporting novel acceleration architecture with no additional inclusion of non-trivial hardware. Through experiments, it is shown that our proposed approach reduces the number of essential bits by 69% on AlexNet and 74% on VGG-16, by which our accelerator reduces the inference computation time by 47% on AlexNet and 50% on VGG-16 over the conventional bit-level weight pruning.
SpinSmart

Cost of data centers has risen sharply in the past few years. Today, it represents about 3% of total US energy consumption with projections to increase further in the coming years. In this paper, we focus on the server infrastructure and observe that workload consolidation techniques, which maximize power efficiency of server systems, do not automatically optimize the overall system power efficiency especially when compute engines and the corresponding on-board cooling systems are considered holistically. We design SpinSmart, a framework that explores optimal server fan speeds to minimize the overall system energy consumption. We explore core capping strategies that estimate the desired number of CPU cores to be used at any given time to minimize combined CPU+fan power. Our experimental results show that we are able to achieve 1) energy savings of up to 10% of total energy and 80% of cooling energy when compared to workload consolidation without core capping strategy; 2) cooling energy savings up to 42% when compared to the strategy that randomly assigns jobs to all the servers and cores.
Airborne multi-sensor target recognition method based on weighted fuzzy reasoning network and improved DS evidence theory

Abstract Difficult obtainment of basic probability assignment (BPA) and inaccurate recognition due to conflicting data are the problems in applying Dempster-Shafer (DS) evidence theory to airborne multi-sensor target recognition. In order to effectively deal with them, an airborne multi-sensor target recognition method based on weighted fuzzy reasoning network (WFRN) and improved Dempster-Shafer (IDS) evidence theory is proposed in this paper. First, the feature vector consisting of 5 feature components is constructed. And then a 4-layer WFRN consisting of 3 kinds of basic units is established to obtain BPA of the feature vector. Finally, conflict data is processed through IDS evidence theory to obtain the final fusion recognition result. The simulation results indicate that the proposed airborne multi-sensor target recognition method is able to obtain BPA reasonably and deal with conflict information.
Problems and Perspectives of Distance Learning in Modern Schools

The purpose of the article is to give recommendations on the organization of distance learning at school, identifying the main problems and prospects for its use in the educational process. Methods of research. During the study, the analysis of distance learning was carried out. The field of its application is defined, assessment of the advantages and disadvantages of this form of learning is presented, the basic means of interaction in the online environment in distance learning are revealed, the most effective teaching methods in distance learning are pointed out. Based on the results of the study, conclusions were made about the specifics of distance learning. The author emphasizes that the main advantages of distance learning as following: an individual approach, the ability to maintain constant feedback between the teacher and the pupil, the lack of strict control as an incentive to free creative activity of pupils. The main disadvantage of distance learning is the lack of direct communication between the teacher and peers. It is also concluded that it is necessary to use collective methods of distance learning in order to overcome the deficit of emotional communication. The most effective methods of distance learning are those that are focused on independent work and creative search. It is noted that further prospects of distance learning development associated with number of following conditions: holding stage of the preparatory work for the adaptation of educational material, acquisition of information technologies and increase of the level of computer skills and choosing the optimal teaching methods for distance learning process.
A Certificate Pin BGP Protocol: CP-BGP

Border Gateway Protocol (BGP) is always the target of the attacks since it is the only routing protocol connecting different Autonomous Systems (ASes) in Internet. BGP fraud is one of the common attacks targeting BGP routing on Internet. YouTube hijacking in 2008 and recent Google traffic hijacking in 2018 are because of routing hijacking. There are existing secure protocols designed to prevent BGP fraud. But they either heavily rely on the Central Authorities (CAs) or need complicate calculations, and thus result in inefficient implementation. We design a Certificate Pin BGP (CP-BGP) protocol to moves majority CA related communications and calculations offline and reduce the dependency of CAs. We analyze real-time BGP traffic data collected by Route Views Project of University of Oregon [1] to prove the performance of CP-BGP and show the proposed protocol, CP-BGP, provides routing hijacking prevention and prefix fraud prevention without decreasing much performance.