Reviewerly/exHarmony huggingface dataset:
The Analysis of Worldwide Research on Artificial Intelligence Assisted User Modeling

Information and communication technologies is being heralded as a catalyst for educational innovations. Artificial intelligence (AI) assisted user modeling has attracted great increasing interests from the academia with a growing research articles available. In this article, a bibliometric analysis of scientific literature concerning AI assisted user modeling was carried out. 333 articles from Web of Science were retrieved and analyzed to comprehensively understand trends and developments of the research field. Specifically, we analyzed the articles in terms of article count and citation count, influential journals, subjects, authors, and keyword occurrence. Finally, special attention was paid to the study of leading countries/regions and institutions. Findings of this work are useful in helping scholars as well as practitioners better understand the development trend of research of AI assisted user modeling, as well as being more aware of the research hotspots.
Batch-dynamic Algorithms via Parallel Change Propagation and Applications to Dynamic Trees.

The dynamic trees problem is to maintain a forest subject to edge insertions and deletions while facilitating queries such as connectivity, path weights, and subtree weights. Dynamic trees are a fundamental building block of a large number of graph algorithms. Although traditionally studied in the single-update setting, dynamic algorithms capable of supporting batches of updates are increasingly relevant today due to the emergence of rapidly evolving dynamic datasets. Since processing updates on a single processor is often unrealistic for large batches of updates, designing parallel batch-dynamic algorithms that achieve provably low span is important for many applications. In this work, we design the first work-efficient parallel batch-dynamic algorithm for dynamic trees that is capable of supporting both path queries and subtree queries, as well as a variety of non-local queries. To achieve this, we propose a framework for algorithmically dynamizing static round-synchronous algorithms that allows us to obtain parallel batch-dynamic algorithms with good bounds on their work and span. In our framework, the algorithm designer can apply the technique to any suitably defined static algorithm. We then obtain theoretical guarantees for algorithms in our framework by defining the notion of a computation distance between two executions of the underlying algorithm. Our dynamic trees algorithm is obtained by applying our dynamization framework to the parallel tree contraction algorithm of Miller and Reif, and then performing a novel analysis of the computation distance of this algorithm under batch updates. We show that $k$ updates can be performed in $O(k \log(1+n/k))$ work in expectation, which matches an existing algorithm of Tseng et al. while providing support for a substantially larger number of queries and applications.
Hybrid feature fusion for person recognition in photo albums

The current work on person recognition in photo albums mainly utilize pure deep convolutional features to describe a person’s image. However, we observe that the hand-crafted features are usually able to provide complementary information and are more stable for identity recognition under some challenging circumstances. In view of this, we propose a novel hybrid method for person recognition in photo albums. In the proposed method, both the hand-crafted features and deep convolutional features are extracted from every person’s image. These multi-modality features are then fused by a weighted average method and classified by a pre-trained SVM in the recognition procedure. The experimental results demonstrates the effectiveness of the proposed method.
All the HIGHT You Need on Cortex–M4

In this paper, we present high-speed and secure implementations of HIGHT block cipher on 32-bit ARM Cortex-M4 microcontrollers. We utilized both data parallelism and task parallelism to reduce the execution timing. In particular, we used the 32-bit wise ARM–SIMD instruction sets to perform the parallel computations in efficient way. Since the HIGHT block cipher is constructed upon 8-bit word, four 8-bit operations are performed in the 32-bit wise ARM–SIMD instruction of ARM Cortex-M4 microcontrollers. We also presented a novel countermeasure against fault attack on target microcontrollers. The method achieved the fault attack resistance with intra-instruction redundancy feature with reasonable performance. Finally, the proposed HIGHT implementation achieved much better performance and security level than previous works.
Segmentation of Words Written in the Latin Alphabet: A Systematic Review

In this systematic literature review (SLR) we summarize studies that address the word segmentation problem (WSP) for Latin-based languages. We adopted the protocol of Kitchenham et al. for the review. The search in academic repositories found 771 works, from which 89 were selected. After a quality assessment step, 69 papers were chosen for data extraction. The results point to a divergence in terminology of this problem, two of which are more relevant, having specific techniques, corpus and application context: compound splitting and identifier splitting. We analyze the state of the art of each context, pointing out differences and similarities in approaches. We hope that these results can serve as a guide for future investigations and advancement of WSP.
Exploring the Insecurity of Google Account Registration Protocol via Model Checking

People nowadays use online service accounts (e.g., Google, Facebook, Twitter) to access certain services. Among them, Google accounts have become increasingly important for users. Not only do many Google services (e.g., Gmail, Google Voice, Google Play, etc.) require them, but many online services also trust and rely on them for operational needs (e.g., login based on Google accounts). This trend introduces a new type of attacks that create a large number of fake, but valid, Google accounts. The fake Google accounts allow the adversary to launch various cyber attacks towards Google account-related services. It motivates us to conduct an empirical security study on the Google account registration service. In this paper, we apply model checking techniques to systematically analyze the insecurity of Google account registration service. We develop a model-checking tool, GAcctAnalyzer, which consists of two phases: (1) service screening phase, which generates counterexamples from the violation of desired properties, and (2) experimental validation phase, which validates the counterexamples through real experiments. We use GAcctAnalyzer to discover four security vulnerabilities including design defects, operational slips, etc. Based on the discovered vulnerabilities, we devise two practical attacks against mobile users and Google: fake Google account generation and Google text/voice spamming attack. They can not only threaten the security of mobile applications and online services, but also cause the Google company to receive user complaints and lawsuits. We finally confirm the feasibility of these attacks through experiments, assess the real-world impact, and propose recommended solutions.
Introducing audio-vision into evidence: the impact of audio recordings and their technical limitations in police use of force cases

This article explores the technical limitations of audio recordings and how those limitations impact the reliability of sound evidence in police use of force cases. In audiovisual recordings, audio is often assumed neutral, redundant or to have the same limitations as its visual counterpart. Bringing together film theorist Michel Chion’s concept of audio-vision and the technical specifications of mobile audio recording, this article highlights how design priorities and compression processes can influence the way sound evidence is perceived. By failing to acknowledge audio recordings as distinct from their visual counterparts, they are rendered invisible and are therefore under scrutinized throughout legal processes. This neglect becomes notably problematic in cases of police use of force where audio/visual recordings often work to bolster the already privileged officer testimony.
The finiteness of the first meeting time between a Gaussian jump and a Brownian particles in the fluid

In this paper, we present more analysis about the finiteness of the first meeting time between Gaussian jump and Brownian particles in the fluid. Rather than using the uniform integrability conditions which are detailed in El-Hadidy [Mod. Phys. Lett. B 33(22) (2019) 1950256], we show the triangular arrays of the random sequence that represents the probable positions of the first meeting at any time [Formula: see text], to get the stronger moment conditions which are sufficient for the finiteness of this time.
Toward Autonomy: Symbiotic Formal and Statistical Machine Reasoning

Different types of machine learning, statistical types, where its knowledge in contained in set of numbers, and formal types, where its knowledge is contained in set of rules or statements, have their own strengths and weaknesses. We argue that their strengths and weaknesses are complementary, and develop a concept called Learn2Reason to harness their collective strength, without inheriting their weaknesses. The efficacy of Learn2Reason concept has been successfully demonstrated in software/binary analysis and cyber security areas. Adoption of the concept significantly improve the performance and scalability of software/binary analysis and cyber security applications and tools.
Aspect-Level Sentiment Analysis of Online Product Reviews Based on Multi-features

Aspect-level sentiment analysis aims to identify the sentiment polarity of fine-grained opinion targets. Existing methods are usually performed on structured standard datasets. We propose a model for a specific dataset which has a complex structure. First, we utilize some matching rules to extract implicit aspects, then we use the extracted aspect words to segment the corpus into samples. Finally, we propose a set of methods to construct data-based features, and try to fuse multi-features for classifier training. Experiments show that the method integrated three features has the highest F1 score, and the sentiment analysis results are more accurate.
Grey-Level Text Encryption Using Chaotic Maps and Arnold Transform

Data in today’s world is ubiquitous and often regarded more valuable than the paper currency itself. More often than not, sensitive data needs to be transmitted from a sender to a receiver in an encrypted format. This paper proposes a novel secret message transmission method through image encryption. The proposed system highlights four techniques; Bit Plane Splicing (BPS), Arnold Transform, a combination of logistic and tent maps. The original text is obtained after decryption, reinforcing its efficiency, security and reliability of this scheme. The security of this scheme has been rigorously analysed through image metrics like Correlation, Entropy, NPCR and UACI, histogram deviation and are tabulated for reference. The results show good security performance against threats. Chosen plaintext attack has been carried out, on two sets of different plain texts, and the results obtained have been favourable.
Predicting Days on Market to Optimize Real Estate Sales Strategy

Irregularities and frauds are frequent in the real estate market in Bulgaria due to the substantial lack of rigorous legislation. For instance, agencies frequently publish unreal or unavailable apartment listings for a cheap price, as a method to attract the attention of unaware potential new customers. For this reason, systems able to identify unreal listings and improve the transparency of listings authenticity and availability are much on demand. Recent research has highlighted that the number of days a published listing remains online can have a strong correlation with the probability of a listing being unreal. For this reason, building an accurate predictive model for the number of days a published listing will be online can be very helpful to accomplish the task of identifying fake listings. In this paper, we investigate the use of four different machine learning algorithms for this task: Lasso, Ridge, Elastic Net, and Artificial Neural Networks. The results, obtained on a vast dataset made available by the Bulgarian company Homeheed, show the appropriateness of Lasso regression.
A Survey on Representation for Itemsets in Association Rule Mining

Mining frequent itemset is one of the main challenges in association rule mining. The efficiency of frequent itemsets depends on the computation time and the data structure used for storing the itemsets. The data structure greatly influences the space requirement. Most of the algorithms work well for a sparse dataset. However, if the dataset is large, it becomes difficult for computation, which eventually increases the execution time. This will affect the scalability of the algorithm. With a compact and concise representation of the itemsets, the itemsets can fit in the memory and hence, do not require any I/O operations. The data structures that are mostly used are array, tree, and trie. In this paper, we present a comparison of the different data structures that are used by the mining algorithms.
HIPAA Security Rule and Cybersecurity Operations

Before jumping into specific safeguards of HIPAA and how security operations center (SOC) activities relate, some background may help put things in perspective. The Health Insurance Portability and Accountability Act (HIPAA) was enacted on August 21, 1996. HIPAA focused on health coverage during gaps when workers change jobs to another. The act provided early incentives for entities to adopt digital records. The Security Rule was implemented on April 21, 2005, focusing on electronic Protected Health Information (ePHI) stored digitally. Enforcement of HIPAA was granted to the Department of Health and Human Services (HHS) on March 16, 2006, under the Enforcement Rule. HHS had the authority to investigate complaints and levy fines for privacy violations. The Health Information Technology for Economic and Clinical Act (HITECH) of 2009 incentivized healthcare organizations for adopting digital medical records. These incentives were part of a program termed Meaningful Use. Finally, in 2013 the Final Omnibus Rule went into effect. It was at this point when news of proactive audit programs surfaced, more news about investigations by HHS after breaches and fines became mainstream news.
Perpetual Solar Potential of a Village by Machine Learning and Feature Extraction in UAV

The process of sustained rural development mandates uninterrupted supply of electricity and water without dependency of the urban infrastructure. Rural areas are often neglected causing power cuts for hours in villages particularly during summer. The study area, a village very near to coast, has high potential for harvesting solar energy. Scientific investigation of the feasibility and utilization of solar irradiance is to be evaluated, to identify the potential hotspots. UAV based Remote Sensing approach associated with socio-economic characteristics, was adopted for the study. A field survey was carried out for assessing the power consumption patterns of individual households. Feature extraction was performed using machine learning technique resulting in faster and efficient extraction of spatial information from UAV. A Regression analysis is carried out to correlate household power consumption with the physical and economic characteristic variables of individual buildings. The cumulative quantity of solar irradiance was used to quantify solar energy harvesting potential using UAV to promote sustained source of green energy for the village. A suitability analysis was performed for optimal location of the community solar power plant to make the village self-sufficient with respect to Electricity. The economic feasibility of the power plant was carried out to demonstrate the feasibility of the project.
The problem of personal data in the Internet of Things’ systems with elements of artificial intelligence

Possible mechanisms of uncontrolled generation of sets of personal data by systems of the Internet of things with elements of artificial intelligence are considered in the work.
A cryptographic algorithm using wavelet transforms over max-plus algebra

Cryptography has a role to secure an important information. Until now, many varieties of cryptographic algorithms are available in the literature. In this paper, we propose a cryptographic algorithm based on Type IVa max-plus wavelet transforms (MP-Wavelets). Encryption and decryption algorithms are constructed based on the analysis and synthesis process of Type IVa MP-Wavelets, respectively. The encryption key contains the number of channels in all levels. The encryption key is chosen such that multiplication of the number of channels in all levels is greater than or equal to the number of characters in the Plaintext. The decryption key consists of the encryption key and a sequence generated by the binary encoding of detail components. This guarantees that the decryption key is very difficult to obtain using the brute-force method. The cryptographic process involves only maximization and addition operations as main operations. The experiments and analysis show that the algorithm is a good cryptographic algorithm based on the correlation between Plaintext and Ciphertext, encryption quality, the decryption key space, cryptanalysis (Ciphertext-only attack) and security analysis (entropy analysis, key sensitivity, Plaintext sensitivity). This algorithm is also efficient in the running time, because the complexity is linear w.r.t. the number of characters in the Plaintext.
KIL: Knowledge Interactiveness Learning for Joint Depth Estimation and Semantic Segmentation

Depth estimation and semantic segmentation are two important yet challenging tasks in the field of pixel-level scene understanding. Previous works often solve the two tasks as the parallel decoding/modeling process, but cannot well consider strongly correlated relationships between these tasks. In this paper, given an input image, we propose to learn knowledge interactiveness of depth estimation and semantic segmentation for jointly predicting results in an end-to-end way. Especially, the key Knowledge Interactiveness Learning (KIL) module can mine and leverage the connections/complementations of these two tasks effectively. Furthermore, the network parameters can be jointly optimized for boosting the final predictions of depth estimation and semantic segmentation with the coarse-to-fine strategy. Extensive experiments on SUN-RGBD and NYU Depth-V2 datasets demonstrate state-of-the-art performance of the proposed unified framework for both depth estimation and semantic segmentation tasks.
Improving Uyghur ASR Systems With Decoders Using Morpheme-based Language Models

We develop and open-source a morpheme-based decoder wrapper, Morpheme Lattice Dynamically Generating Decoder(MLDG-Decoder) for Uyghur Deep Neural Network-Hidden Markov Model(DNN-HMM) systems. The Morpheme Lattice Dynamically Generating Decoder employs an algorithm, named as “on-the-fly composition with Forbidden Epsilons Activated by Back-Off States(FEABOS),” to allow the back-off states and back-off transitions to play the role of a relay station during on-the-fly composition. The algorithm empowers the dynamically generated graph to constrain the morpheme sequences in the lattices as effectively as the static and fully composed graph does when a 4-Gram morpheme-based Language Model (LM) is used. Based on the deeper and wider neural network acoustic models and the Morpheme Lattice Dynamically Generating Decoder, we experiment with three morpheme-based decoding strategies for Deep Neural Network-Hidden Markov Model hybrid systems: using a Morpheme Lattice Dynamically Generating Decoder, the two-pass decoding based on rescoring, and the fully static and morpheme-based decoding. The results show that decoding based on a fully static decoding graph reduces the minimum Word Error Rate(WER) to 14.24%. Morpheme Lattice Dynamically Generating Decoder reduces Word Error Rate to 14.54% and maintains reasonable memory consumption.
The application of privacy protection and artificial intelligence technology in the information auxiliary system of the prevention and control of COVID-19/ 中华医学科研管理杂志

ObjectiveThe outbreak of novel coronavirus raised many problems in the auxiliary information system of epidemic prevention and control, which including the need to prevent key data from being illegal modification, traceability, lack of decision support systems at different levels, barriers to cross regional cooperation and low automation of case diagnosis.MethodsIn this paper, artificial intelligence, security computing supporting privacy protection, block chain and other emerging technologies are introduced into the epidemic prevention and control auxiliary information system.ResultsThis paper discusses how to utilize modern cryptography and block chain technology to establish a traceability system that could assure the security of epidemic information; design a distributed decision support system; solve the privacy-preserving problems of Federated Learning based on SGX technology, and present a group architecture to alleviate the performance cost of SGX.ConclusionsThe schemes above can help to achieve the security and traceability of epidemic information, also improve the automation and decision-making efficiency of the auxiliary information system for epidemic prevention and control.Key words: Epidemic prevention and control; Privacy protection; Block chain; Artificial intelligence; Decision Support
Block Steganography Based Secure Key Encryption to Improve Data Security

In digital communication secure communication sessions must be provided. The data transmitted across the global network should be more secure so that any Intruder can’t interpret the data. To improve the security features in case of data transmission and data hiding Cryptography and Steganography are the two popular methods. In this paper, we are presenting the combination of Cryptography and Steganography techniques in one system. Our approach is to develop a new technique to encrypt secret information to cipher text and hide it into an image, with the benefits of combining cryptography and steganography. Firstly, the message is encrypted by using a Secret key and XOR its with the main text using binary representation. After that, we performed Steganography. Some modifications are done on LSB algorithm by adding a sequence algorithm for pixel selection. Results we get indicate that our proposed work is encouraging in terms of robustness and security.
A Facial Expression Recognition Model using Hybrid Feature Selection and Support Vector Machines

Facial expression recognition is a challenging issue in the field of computer vision. Due to the limited feature extraction capability of a single feature descriptor, in this paper, a hybrid feature extraction is utilised. The proposed methodology includes local and global feature extractions that is done by local binary pattern (LBP) and histogram orientation gradient (HOG) respectively. Before applying the feature extraction process, pre-processing and face detection is applied on the face image to extract the useful features. The Viola and Jones algorithm is utilised for face detection and the hybrid Laplacian of Gaussian (HLOG) is used for pre-processing stage. The orthogonal local preserving projection (OLPP)-based dimension reduction algorithm is applied to the extracted features to minimise the computational complexity of the classification algorithm. The SVM classification algorithm is utilised for identifying the facial expression. Here, standard CK+ facial expression dataset is used for evaluating the proposed methodology. The proposed methodology performed well in terms of accuracy compared to the existing PCA + Gabor and PCA + LBP methodology.