Reviewerly/exHarmony huggingface dataset:
PSR-Based Feature Adaptation with Anomaly Re-Prediction for Maritime Target Tracking

The tracking method for the relevant filter cannot track when the target is affected by the background and in the scene of occlusion. We propose a target tracking algorithm based on the peak-to-sidelobe ratio (PSR) feature weight adaptation with anomaly re-detection based on the efficient convolution operator for manual features (ECO_HC) algorithm. The weights of the HOG features and CN features corresponding to the peak-to-sidelobe ratio are used as dynamic weight coefficients, and the maximum response value after their fusion is the area where the target may appear. Determine the target tracking status by calculating the tracking confidence of the response map, and when there is a tracking anomaly, the ECO_HC tracker is enhanced using the Kalman filter to re-predict the target position. The proposed algorithm has experimented on the OTB-50 dataset. The results show that our algorithm can cope with complex scenarios and improves by 3.0% in tracking accuracy and 2.8% in tracking success rate compared with the baseline algorithm ECO_HC. Finally, conduct tracking experiments on the actual sea surface scenes, and the proposed algorithm effectively suppresses the effects of common interference factors on the tracking effect.
REALIZATION OF INNOVATIVE METHODS AS A CONDITION FOR THE FORMATION OF PROFESSIONAL ORIENTATION OF STUDENTS OF THE SPECIALTY “PHARMACY”

The article is devoted to the actual topic of the formation of the professional orientation of students of the specialty “Pharmacy” through a system of practice-oriented classroom and extracurricular classes using modern teaching methods. Such a set of measures in higher education, taking into account the conditions of the educational paradigm and the labor market, is aimed at increasing the level of professional training and creating a psychologically adapted education program.&#x0D; The purpose of the study is a diagnostic study of the formation of the professional orientation of students of the specialty “Pharmacy” in the educational space of the university using modern teaching methods.&#x0D; Methodology. The following methods were used to disclose the topic of research: analysis of scientific literature; implementation of diagnostic tools; content analysis; pedagogical experiment.&#x0D; The work is based on system-activity (L.S. Vygotsky, A.N. Leontiev, S.L. Rubinstein, B.G. Ananiev, V.D. Shadrikov), competence-based approaches (V.I. Baidenko, A.A. Verbitsky, I.A. Zimnyaya, N.V. Kuzmina, V.V. Kraevsky, A.K. Markova, L.I. Mitina, J. Raven, Yu.G. Tatur, A.V. Khutorskoy), as well as on the theory of professional self-determination of a person (J. Holland, Eli Ginsberg, D. Super, E.A. Klimov, E.F. Zeer, I.L. Fedotenko).&#x0D; Results. Analysis of the data obtained during the experiment showed that the scientific novelty of the research is as follows:&#x0D; 1. the categorical-conceptual apparatus was concretized and clarified: “professional orientation”, “stages of formation of professional orientation”, “orientation structure”, “modern teaching methods”;&#x0D; 2. substantiated theories and concepts necessary for the successful formation of the professional orientation of future pharmacists in the educational space of the university;&#x0D; 3. the psychological and pedagogical conditions for the successful formation of the professional orientation of pharmacists are highlighted;&#x0D; 4. implemented diagnostic tools to determine the level of professional orientation of students of the specialty “Pharmacy”.&#x0D; Practical implications. The obtained practical results, diagnostic and research materials (adapted methods, tests, cases, projects, practice-oriented and competency-based tasks, quests, quizzes) can, with a certain adaptation, be broadcast in various educational organizations to form a professional orientation for students of other specialties.
3D CNN for Micro Expression Detection

Detecting true expression is important for many applications such as psychology consulting, lie detection, and observing student attitudes during teaching activities. True expression, however, is hard to detect because it happens in a short time. Longer facial expressions sometimes cannot represent the true emotion of people. Deep Learning for visual data has been shown as a powerful method in many recognition tasks. A convolutional neural network (CNN) based on a convolution operation has identified important clues from the data and provides more effective computation in many recognition tasks. A three-dimensional Convolutional neural network (3D-CNN) is designed to work on spatial-temporal data. Processing video with considering temporal space fit to work with 3D-CNN. This paper discusses the implementation of 3D CNN on a high-resolution microexpression video data
Background Subtraction Based on Visual Saliency

Visual salience plays a significant role in the process of real life to quickly extract necessary information from complex scenes. The state-of-the-art algorithms impose restrictions on various reality conditions, such as dynamic background and lighting, which will lead to misjudgment and false detection. To alleviate the problem that dynamic background is hard to be compensated, this paper proposes a background subtraction method based on visual saliency, which can fully release the advantages of human visual saliency. Experimental comparison is firstly conducted to confirm that the visual salient region is a potential moving target area. Then feature corner samples is extracted from this area, and sparse optical flow method is adopted to determine the moving target in the current frame. To further enhance the perception ability of moving target in dynamic background, an improved vibe method based on partially random background updating strategy is developed to realize accurate moving object extraction. In addition, the results of motion perception are purposefully used for background set updating. Experimental results indicate that the proposed model can effectively extract the moving target.
Database Schemas Used in SQL University Courses – State of the Art

The body of knowledge in IT courses area includes topics about relational databases in which students have to obtain at least basic knowledge and skills in SQL. Also for the students in the areas of computer science majors SQL is an obligatory part of knowledge and skills. To be effective, SQL training in university courses has to use appropriate case studies (i.e. database schemas) which are semantically clear, simple, and suitable to demonstrate the major SQL functionality not only “on paper” but also in real DBMS environment (or DBMS sandbox). Moreover, the appropriate database state is important to demonstrate understandable results from SQL queries, which can be manually evaluated by students to compare with results of execution in real database environment. In this paper some popular database schemas and states used in SQL training in university level courses are analyzed from these points of view. For this study, six schemas from popular database books used in university courses are considered. Two major streams in database schemas and states are distinguishable. One is focused on overall solution, that covers database design topics and SQL training, another is focused only on SQL training. It is found that a possible break point between these streams is to use database schema which is complex enough for demonstration of the design process, and after that to use simplified version of the database schema with reduced number of records for SQL training.
Speak Up! Legal Challenges in the Light of the Determination of Emotion in Conducting Targeted Advertising through a Voice Recognition Device

Artificial Intelligence (AI) technology keeps on being developed and has started to emerge into some devices and/or machines. AI can be embedded in many forms, and one of them is voice recognition technology. This technology that offered a human-like interactive feature has featured such as in iPhone Siri and Amazon Alexa. The rising trend of this technology also showed when Amazon has been granted a new patent regarding its virtual assistant, Alexa, in 2018. According to the patent, the device will automatically infer the user’s voice to determine their emotion in order to generate targeted advertisements. This patent hence posed a risk of harming the user’s autonomy since the user is unable to leave their emotions to go undetected by the device. Therefore, the commodification of emotion could challenge the adequacy of the current legal regimes, in particular, data protection and consumer laws since both have the primary concern to protect the autonomy and dignity of consumers. This research aimed to analyse whether the current European data protection and consumer laws are adequate to mitigate the risks posed by the commodification of emotion to conduct targeted advertising in the voice recognition technology.
Geometric Analysis and Metric Learning of Instruction Embeddings

Embeddings for instructions have been shown to be essential for software reverse engineering and automated program analysis. However, due to the complexity of dependencies and inherent variability of instructions, instruction embeddings using models that are successful for natural language processing may not be effective. In this paper, we perform geometric analysis of instruction embeddings at the token level and instruction family level, showing much greater variability and leading to degraded performance on intrinsic analyses. Then we propose to use metric learning to improve the relationships among instructions using triplet loss. Our results on a large dataset of instruction groups shows significant improvements. We also provide a theoretical analysis of the instruction embeddings by looking at the BERT components and characteristics of inner-product matrices for attention in the transformer blocks. The code will be available publicly after the paper is accepted for publication.
Emotion Driven Chatbot Using Natural Language Generation Techniques

The increasing developments in chatbot architectures introduce a choice between lack of generality in the chatbot responses or an increase in the complexity of optimization. To address this compromise, we propose a divide and conquer approach in the architecture of the chatbot. This behavior in the chatbot is demonstrated by making the chatbot emotionally and contextually aware of the user's intent while maintaining a knowledge base for world knowledge-based queries. We propose a chatbot system which can generate more natural responses based on the abstract emotional knowledge acquired by the user input and the database of pre-labeled responses. The responses are generated using sequence to sequence model introduced in Learning Phase Representation using RNN Encoder- Decoder for statistical Machine Translation. The goal is to generate responses which can be helpful in changing users’ emotional state while satisfying some criteria to measure the effectiveness of the system. Chatbot here will provide the basic conversation functionality based on emotional state of user and also carry on a normal day to day conversation. Several chatbots are available but they require user to setup key phrases manually and does not work for different set of key phrases other than the knowledge base. Our proposed system ranks every response, every time, they interact with a user and also lead to addition of new phrases and responses in sequential interactions with the user.
Learning to Predict 3D Mesh Saliency

Mesh saliency, which measures the perceptual importance of different regions on a mesh, benefits a wide range of applications. However, existing mesh saliency models are largely built with hard-coded formulae, which cannot capture true human perception. Some existing techniques utilise indirect measures to capture user perception (e.g., mouse clicks), which can be unreliable. In this work, we collect eye-tracking data for 3D objects seen from different views, and develop an optimisation-based approach to fusing heat-maps captured from individual views to form consistent saliency maps on meshes. To predict mesh saliency on a new shape, we further develop a learning-based approach that regresses local surface characteristics based on a set of input features. Experimental results show that our learning-based method achieves better performance than state-of-the-art methods for unseen shapes. We will make our dataset publicly available.
Improving weight clipping in Wasserstein GANs

Weight clipping is a well-known strategy to keep the Lipschitz constant of the critic under control, in Wasserstein GAN training. After each training iteration, all parameters of the critic are clipped to a given box, impacting the progress made by the optimizer. In this work, we propose a new strategy for weight clipping in Wasserstein GANs. Instead of directly clipping the parameters, we first obtain an equivalent model that is closer to the clipping box, and only then clip the parameters. Our motivation is to decrease the impact of the clipping strategy on the objective, at each iteration. This equivalent model is obtained by following invariant curves in the critic loss landscape, whose existence is a consequence of the positive homogeneity of common activations: rescaling the input and output signals to each activation by inverse factors preserves the loss. We provide preliminary experiments showing that the proposed strategy speeds up training on Wasserstein GANs with simple feed-forward architectures.
Particle swarm optimization (PSO), crow search algorithm (CSA), charged system search algorithm (CSSA) and big-bang big crunch optimization (B-BBCO) - Optimization methods – A critical review

This dissertation concentrates on the application of non-traditional optimization techniques. To solve the optimization problem, the various non-traditional optimization methods are reviewed critically. The methods that are going to review in this survey are Particle Swarm optimization (PSO), Crow Search Algorithm (CSA), Charged system search algorithm (CSSA) and Big-bang big crunch optimization (B-BBCO). These methods are applied to investigate the optimum values. The both linear and non-linear optimization problems are solved to get global solutions.
A Scalable Design Approach for State Propagation in Serverless Workflow

Serverless development is challenging as applications are composed of stateless and short-lived functions. Many workflows require time-bound functions to transfer their state to other function before termination. The serverless Function-as-a-Service offerings lack state management support; therefore, it must be handled at application-level. In this paper, we propose a scalable design approach that simplifies development of workflows that require sharing of ephemeral intermediate data. Our design uses object serialization/deserialization with cloud object storage to share state across functions. It provides a mechanism for fine-grained support for state propagation and synchronization in a serverless workflow. This solution is cost-effective and efficient as it does not depend on any external database or cache for state management. The design has been validated by implementing ‘Word Count’- a classic MapReduce use case. Our results show that the proposed scalable design can process input of any size and can handle state propagation in complex serverless workflow.
Time-Lock Encryption for Multiple Consecutive Periods and its Application

Time-lock encryption is a method to encrypt a message to a future time such that it can only be decrypted after that time. Previous time-lock encryption schemes rely on a trusted server equipped with a clock that periodically issued decryption keys at specific times based on long-term secrets. While such schemes allow encryption of a message into the future, they provide limited support for decryption for some intermediate consecutive period of time within the lifetime. Using the bilinear pairing, we address this problem by giving a new concept of time-lock encryption, where the aggregated decryption key can be used to decrypt any ciphertexts which are generated from the same plaintext and are locked to some consecutive periods of time within its lifetime. We call our concept key aggregated time-lock encryption (KATLE). Then We propose a practical construction that supports compact decryption keys and compact ciphertexts. Finally we show how our scheme can be used in the Black Friday Coupon Issuing System.
A construction of generalized quasi-cyclic codes over finite field using gray map

Cyclic code is one of the important type of linear codes. This type of codes has interesting algebraic sturctures and important applications. One of the generalization of cyclic codes is quasi cyclic codes which also could be generalized further to be Generalized Quasi-Cyclic (GQC) code. The latter codes use arbitrary permutation instead of cyclic shift as in cyclic codes. The GQC code has been an interesting topic to study until now and has an important application in Post-Quantum Cryptography. For instance, it has been used as keys in McEliece cryptosystem. In this paper, we give a construction of q-ary GQC code using Gray Map from GQC code over the ring B1. The main result in this paper is if we have a GQC code over B1, then the image of such code under certain Gray map is a q-ary GQC code. Also, some examples of optimal codes produced by computer search based on the main result.
SoftId: An autoencoder-based one-class classification model for software authorship identification

Software authorship identification has a major relevance in software development, as it can be of help in detecting potential problems with the source code, identifying false reporting of authorship within a project and it can contribute to the improvement of the code review and testing effort prioritization. This paper introduces SoftId, a one-class classification model. The model is trained to encode and recognize the programming style of a given set of software developers, thus gaining the ability to detect whether a certain source code is authored by a developer from the original set or it is an “unknown” software developer. The novelty of the SoftId classifier resides in solving the software authorship identification task in an open-set configuration using a deep autoencoder and based on textual representations of the source codes. The experimental results highlight a significant improvement of SoftId compared to One-class Support Vector Machine classifier used in the literature for anomaly and novelty detection.
Distributed EK-NN Classification

For big data, the Evidential K-Nearest Neighbor (EK-NN) classifier is still impractical due to the restrictions of time and memory. In both the training and testing stage, searching for K closest neighbors requires intensive quadratic computation and has to be repeated for each input sample. To address this issue, we propose a distributed EK-NN classifier, named Global Exact EK-NN, for fast processing with Apache Spark. We compare the proposed classifier, which can be scaled to 48 nodes (2688 cores) at a cluster named the Texas Advanced Computing Center Frontera, with several other parallel K-NN based algorithms over 4 large datasets. Our method is able to achieve state-of-the-art scaling efficiency and accuracy on the large datasets having more than 10 million samples.
SMRT:An Effective Malicious Node Resistance Design for Mixnets

With the increasing requirement of communication privacy protection, anonymous communication networks are widely used to transmit sensitive data or important business information. With its advantages of low latency and high scalability, mixnets model has become the basic model of many anonymous communication networks. Research has shown that by controlling certain nodes in a mixnets, it is possible to de-anonymize the network or perform some extremely harmful attacks, such as dropping messages, delaying messages, etc. This paper proposes SMRT (Separation Multiple Reorganization Test), which is a solution to improve the reliability of the mixnets model. The SMRT method dynamically analyzes the behavior of malicious nodes through the cooperation of network nodes, and inhibits nodes from participating in data forwarding according to the frequency of malicious behavior. We implement a simulation system in the Internet environment to verify the effectiveness of the method. The experimental results show that SMRT is capable of handling a maximum malicious node share of 33%.
Modifiable Blockchain Based on Chebyshev Polynomial and Chameleon Hash Function

Blockchain has become one of the most important technologies in the financial sector. The blockchain based on cryptographic hash algorithm has a strong anti-tampering function. But it also makes it nearly impossible for the blockchain to modify any bad transactions that could be detrimental to the economy as a whole. In order to solve the problem of difficulty in blockchain modification, this paper proposes a modifiable blockchain based on chameleon hash and Chebyshev polynomials (MBCC). We use Chebyshev polynomials to concatenate blocks and use the chameleon hash function to ensure transaction modifiability. The blockchain we designed inherits the advantages of the classic blockchain well, adopts a new proof-of-work mode, and supports transaction-level modification. And MBCC maintains strong consistency in the verification process of all blocks (modified and unmodified). Finally, we conducted an experimental analysis and believed that the structure of this blockchain can meet the actual requirements and have a wide range of application scenarios on the premise of ensuring security.
An Enhanced Reversible Data Hiding Algorithm to Maintain Privacy of Multimedia in Cloud

This paper proposes high-capacity image-based reversible data-hiding techniques that may be tested in both plaintext and encrypted domains. We proposed methods for maintaining the authenticity and privacy of multimedia material without danger of intrusion. All of these techniques employ encrypted copies of natural photos as a host image on which extra data is encoded, and they all allow for complete reversibility in noise-free retrieval of these natural images. The content owner might use these methods to outsource an embedding algorithm to an untrusted third party in a cloud environment without providing the cover to the embedder since it includes very sensitive information. The owner encrypts the cover picture to keep the data private, and the embedder homomorphically embeds the secret data on the encrypted image coefficients without knowing the host image. The owner might enable anybody with a valid data hiding key to extract the encrypted domain's contained secret data, but only the intended single receiver with the associated decryption key could decode the marked data and disclose the cover. Algorithms are improved to accomplish data embedding in the encrypted transform domain to improve resilience against typical signal processing procedures. This is done using the integer discrete cosine transform in the encrypted domain (e-DCT). When compared to current state-of-the-art reversible data concealing methods, the suggested approaches provide improved embedding capacity and picture quality. In the encrypted domain, the methods perform well in the face of several common noise assaults.