Reviewerly/exHarmony huggingface dataset:
A Performance Study Depending on Execution Times of Various Frameworks in Machine Learning Inference

This work is intended to compare the latency of various frameworks in machine learning inference through an average power calculation model. This model is created in terms of a 2-layer neural network with PyTorch, in Python. Then, it is converted to a traced Torch Script module and also to ONNX file format. Afterwards, the C++ front-end is used for the inference process. The traced model is run with Libtorch on CPU and GPU, the ONNX file is run with ONNX Runtime on both CPU and GPU and it is also run with TensorRT on GPU. The inference execution times for 100 trials are averaged for all cases and it is realized that TensorRT with ONNX file format significantly outperforms its counterparts as expected. Hence, this work highlights the performance of TensorRT in machine learning inference and sheds light into the future by proposing several extensions.
Toward Automated Game Balance: A Systematic Engineering Design Approach

This study proposes a framework to automatically assess and bring balance in real-time strategy (RTS) games. A three-layered framework comprising intelligent bots, deep machine learning, explainable artificial intelligence (XAI), uncertainty quantification (UQ), and optimal learning is presented. For preliminary analysis, we conducted a study using the mi-croRTS game built specifically for advancing AI research. Data is generated through self play games between the intelligent bots, and game balance is measured through the predicted probability of each player winning a game. To demonstrate game re-balancing using this approach, a sample unbalanced game is shown along with proposed perturbations on important features identified using a popular XAI technique called SHapley Additive exPlanations (SHAP). Results indicate this framework enables efficient identification of game parameters causing imbalance and iterates over game parameters to restore balance. The three-layered framework is designed to be generic and applicable to more complicated RTS games, such as StarCraft II.
Analysis and Design Of Implementation Guidelines Data Security Management Assessment Techniques Based On DAMA-DMBOKv2

One of the company's critical assets is data which is the basis for knowledge and decision making. Nowadays, data can be easily accessed, transferred, and communicated. However, behind it, security threats also lurk. So it is necessary to protect information related to confidentiality, integrity, and availability. This problem occurs due to the absence of data security management and lack of data governance planning. The absence of good data security management allows an increased risk of data theft or other actions that can harm the company. These problems are caused by implementing data security management. Data security management helps companies ensure business continuity, minimize damage, and increase company value. The process-related to data security management is not in detail, so this research focuses on designing guidelines that contain detailed steps. Development of guidelines and data security assessment techniques using DAMA-DMBOKv2 and Process Assessment Model (PAM). Proposed guidelines and assessment techniques need to be validated by experts to ensure the results of the guidelines are appropriate so that they can apply to company objects. Appropriate guidelines and assessment techniques will undoubtedly support the data governance process to realize good governance.
Biobjective Optimization Problems in Simple Neural Networks as Binary Associative Memories

This paper studies biobjective optimization problems in synthesis of binary associative memories characterized by ternary cross-connection parameters and the signum activation function. The system simplicity enables us to give a condition of parameters that guarantees storage of desired memories. In order to synthesize efficient associative memories, we define biobjective optimization problems based on two objectives which evaluate stability of stored desired memories and sparsity of connection parameters. In order to solve the problems, we have applied a simple evolutionary algorithm to typical examples and have obtained interesting results: a suitable connection sparsity exists where the memory stability is the strongest, and a trade-off exists between the memory stability and the connection sparsity.
Effectiveness Evaluation of Business English Practice Teaching Based on Ant Colony Algorithm

With the rapid development of domestic economy, the demand for talents is also increasing. There is a phenomenon that the cultivation of students in Colleges and universities is not in line with the actual needs of the society. Based on this problem, educators began to use ant colony algorithm to establish the effectiveness evaluation system of business English practice teaching in Colleges and universities. According to the ant colony algorithm, it optimizes the data of business English in Colleges and universities. This paper is to use the advantages of ant colony algorithm to deal with the data, according to the current problems in the practical teaching of business English in Colleges and universities to carry out an effective evaluation, and according to the evaluation results, improve the practice teaching strategy, and cultivate more qualified English talents for the society.
A Survey on Deep Learning Methods in Image Analytics

The domain of machine learning (ML) is at bright phase as deep learning (DL) gradually turns into pioneer in this sphere. DL utilizes numerous covers to address the reflections of information to construct computational prototypes. The main empowering agent DL algorithms, for example, generative adversarial networks, convolutional neural networks, and model exchanges have totally changed our impression of data handling. Notwithstanding, there happens a gap of comprehension behind this colossally speedy domain, since it was never recently addressed from a multi-scope viewpoint. The absence of center arrangement delivers these incredible techniques as discovery machines that repress improvement at an essential level. In addition, DL has repeatedly been seen as a silver slug to all hindrances in ML, which is a long way from reality. This paper presents a thorough review of authentic and late condition state-of-the art approaches in visual, sound, and text preparing; informal community examination; and common language handling, trailed by the top to bottom investigation on turning and pivotal improvements in DL claims. Likewise, attempted to audit the issues looked in deep learning, for example, unsupervised learning, discovery models, and Internet learning and to delineate how these difficulties can be changed into productive future exploration roads.
Parallel Algorithm to Efficiently Mine High Utility Itemset

Finding high utility itemset (HUI) from transactional databases like customer transaction data is not an easy task. The generated HUI should represent the real-world condition which contains set of items that likely to be purchased together and also has high profit. Many algorithms have been introduced to overcome that issue. Parallelism is one of the good architectures that can be adapted for mining algorithms, and it is rarely used for HUI Mining. The basic idea that can be adapted is by dividing the mining process based on subsearch spaces in concurrent time to boost the performance. This paper proposed two new frameworks that adapted parallel mining namely CLB and PLB which are extension of ULB-Miner algorithm. CLB is hybrid algorithm from CTU-PRO and ULB-Miner, and PLB is proposed because of inefficient process in CLB. From the experimental evaluation, PLB outperforms ULB-Miner in all cases regarding execution time.
Unsupervised Type Constraint Inference in Bilinear Knowledge Graph Completion Models

Knowledge graph completion (KGC) models aim to provide a feasible way of manipulating facts in knowledge graphs. Most KGC models do not consider type constraint in relations due to the scarcity of type information in training data. We proposed an unsupervised method for inferring type constraint based on existing bilinear KGC models. Our method induces two type indicators into every relation and adjusts the location of entity embeddings in feature space to match the type indicators. Our approach eliminates the external feature space for entity types and type constraints in relations and has a consistent feature space; therefore, it has fewer parameters than other methods. Experiments show that our methods can improve the performance of the base models and outperform other methods on datasets about general knowledge.
Live from the metaverse

In the Fall of 2019, Lifelike & Believable Animation Design, in partnership with Animatrik Film Design, began a multi-year collaboration with world-renowned circus arts collective, Les 7 Doigts (The 7 Fingers) to explore the combination of realtime motion-capture, rendering and projection with traditional circus disciplines to create unique theatrical performances simultaneously presented before live theatre audiences and remotely-connected VR participants. This collaboration came to be known as "The LiViCi Series" (for Live Virtual Circus).
Protected solid-state qubits

The implementation of large-scale fault-tolerant quantum computers calls for the integration of millions of physical qubits with very low error rates. This outstanding engineering challenge may benefit from emerging qubits that are protected from dominating noise sources in the qubits' environment. In addition to different noise reduction techniques, protective approaches typically encode qubits in global or local decoherence-free subspaces, or in dynamical sweet spots of driven systems. We exemplify such protected qubits by reviewing the state-of-art in protected solid-state qubits based on semiconductors, superconductors, and hybrid devices.
Analysis of File Carving Approaches: A Literature Review

Digital forensics is a crucial process of identifying, conserving, retrieving, evaluating, and documenting digital evidence obtained on computers and other electronic devices. Data restoration and analysis on file systems is one of digital forensic science’s most fundamental practices. There is a lot of research being done in developing file carving approaches and different researches focused on different aspects. With the increasing numbers of literature that are covering this research area, there is a need to review this literature for further reference. A review is carried out reviewing different works of literature covering various aspects of carving approaches from multiple digital data sources including IEEE Xplore, Google Scholar, Web of Science, etc. This analysis is done to consider several perspectives which are the current research direction of the file carving approach, the classification for the file carving approaches, and also the challenges are to be highlighted. Based on the analysis, we are able to state the current state of the art of file carving. We classify the carving approach into five classifications which are general carving, carving by specific file type, carving by structure, carving by the file system, and carving by fragmentation. We are also able to highlight several of the challenges for file carving mentioned in the past research. This study will serve as a reference for scientists to evaluate different strategies and obstacles for carving so that they may choose the suitable carving approaches for their study and also future developments.
Combating Online Hate Speech: The Impact of Legislation on Twitter

We analyze the impact of the Network Enforcement Act, the first regulation which aims at restraining hate speech on large social media platforms. Using a difference-in- differences framework, we measure the causal impact of the German law on the prevalence of hateful content on German Twitter. We find evidence of a significant and robust decrease in the intensity and volume of hate speech in tweets tackling sensitive migration-related topics. Importantly, tweets tackling other topics as well as the tweeting style of users are not affected by the regulation, which is in line with its aim. Our results highlight that legislation for combating harmful online content can influence the prevalence of hate speech even in the presence of platform governance mechanisms.
Activity Monitoring Through Wireless Sensor Networks Embedded Into Smart Sport Equipments: The Nordic Walking Training Utility

This paper presents the study of Nordic Walking providing objective evaluations based on real time acquisition of kinematic parameters during the sport practice. It is possible to carefully monitor the athletic gesture through the integration of conventional poles with inertial sensors, composed of a triaxial accelerometer, a triaxial gyroscope, a pressure sensor positioned on the handle, and a load cell, which constitute a Wireless Sensor Network whose nodes are appropriately synchronized. The integration of such sensors, which must be unobstructive and not change the functionality of the poles, is dictated by the ultimate goal of providing a real time biofeedback in two possible scenarios. The first is intended for Nordic Walking’s instructors, who have the opportunity to verify the proper practice execution by their trainees through the availability of real time objective data, in addition to their personal experience. The second is devoted to amateur players who can practice alone, after the training session with the instructor, and can independently correct any imperfections in real time using a software tool running on their smartphone. Using the Dynamic Time Warping algorithm, the proposed system identifies the most frequent errors in performing athletic gesture, allowing adjustment in real time of the sporting exercise, through the detection, quantification and correction of errors. The obtained results show that the developed system is able to provide an accurate analysis of the athletic gesture and the proposed algorithm allows a quantitative monitoring of the progress achieved by each subject over time.
Pedestrian Intention Prediction via Depth Augmented Scene Restoration

Minimizing traffic accidents between vehicles and pedestrians is of great importance in constructing intelligent transportation systems. Pedestrian behavior prediction is one of the major solutions to achieve this goal. The current methods are all extracting key information in the two-dimensional plane of image (e.g., the scene map around pedestrians, semantic map), and there is no specific use of the unique information in the three-dimensional space. Besides, the relative distance information that accounts for the interaction between the target pedestrian and the scene has not been properly utilized, and this information is missing in the current behavioral benchmark dataset. To solve these challenges, we introduce a new view for pedestrian intention prediction. The distance from each pixel to the camera is firstly estimated by the method of monocular depth estimation, so that the two-dimensional pixels are remapped to the three-dimensional space. Then, a new deep learning variant model is proposed to adequately fuse information from different perspectives. In particular, the experiments based on large-scale traffic dataset JAAD [1] and PIE [2] show that the multi-view architecture has an outstanding performance than state-of-the-art (SOTA) methods.
Automated Classification Map Generation of Prostate Cancer using Deep Learning

Whole slide images are examined by pathologists and scored according to the Gleason grading system. It is a time-consuming task and may involve assessing variability between different pathologists. In this work, a deep learning system is presented that generates classification maps for whole slide images. This system produces patch-level results first and then predicts a classification map for each prostate cancer slide. The classification maps contain regional cancer severity for each biopsy and are compared with provided mask images. Both provided mask images and predicted mask images are then reviewed by an experienced pathologist to evaluate classification performance. Most state-of-the-art deep learning methods cannot explain how they output classification results. With this work’s classification maps, pathologists can see the regional classification results that explain the algorithm’s classification.
A comprehensive review and evaluation on text predictive and entertainment systems

One of the most important ways to experience communication and interact with the systems is by handling the prediction of the most likely words to happen after typing letters or words. It is helpful for people with disabilities due to disabling people who could type or enter texts at a limited slow speed. Also, it is beneficial for people with dyslexia and those people who are not well with spells of words. Though, an input technology, for instance, the next word suggestion facilitates the typing process in smartphones as an example. This means that when a user types a word, then the system suggests the next words to be chosen in which the necessary word by the user. Besides, it can be used in entertainment as a gam, for example, to determine a target word and reach it or tackle it within 10 attempts of prediction. Generally, the systems depend on a text corpus, which was provided in the system to conduct the prediction. Writing every single word is time-consuming, therefore, it is vitally important to decrease time consumption by reducing efforts to input texts in the systems by offering most probable words for the user to select, this could be done via next word prediction systems. There are several techniques can be found in literature, which is utilized to conduct a variety of next word prediction systems by using different approaches. In this paper, a survey of miscellaneous techniques towards the next word prediction systems will be addressed. Besides, the evaluation of the prediction systems will be discussed. Then, a modal technique will be determined to be utilized for the next word prediction system from the perspective of easiness of implementation and obtaining a good result.
Task and intelligent path planning algorithm for teams of AGVs based on multi-stage method

Attributed to the rapid development of ecommerce, the demand for logistics is increasing day by day. In this context, modern warehousing technology with Multi-AGV Cooperation System (MACS) as the core came into being. The large-scale Automated Guided Vehicle (AGV) can transport multiple cargos in a single task owing to its large load capacity and strong endurance, which makes it a trend to use large-scale AGV to transport cargos in indoor warehouses or outdoor parks. However, it should be noted that there are a series of problems, such as resource allocation and path conflict, when using teams of AGVs to perform multiple transportation tasks, and it is a challenging task to find an ideal task and path planning scheme for the scenario where multi-AGV perform multiple transportation tasks. In view of this, the present study proposed a multi-stage optimization method to effectively obtain the approximate optimal solution to solve the aforesaid problem. In this proposed method, a 0-1 integer linear programming model was constructed to solve the problem of assigning multiple transportation tasks to multiple AGVs in the first stage, and then in the second stage we planned the optimization sequence in which AGV passes through each pick-up/delivery location when performing multiple transportation tasks. In the third stage we solved the path planning problem of teams of AGVs moving to each target location without collision. In addition, simulation experiments were carried out, which verified that the proposed method can achieve superior performance in path distance and task completion time when performing multiple tasks.
Apply Blockchain Technology for Security of IoT Devices

The world is only beginning to see the value and potential impact of the internet of things (IoT). Until recently, access to the internet was bounded via desktop, tablet, or smartphone. With the (IoT), practically all devices and objects can be connected to the internet and monitored remotely. IoT devices simplify our lives and make organizations more efficient; however, there are still challenges to address, particularly in the security context. As we continue to embed these connected objects and a wider variety of wireless devices, it is mandatory to provide confidence in this vast incoming information source. Blockchain has emerged as a disruptive technology that will transform the way we store, share information, and impose restrictions to know the authentications. The data distribution and robust level of encryption will remove the need for trust among the involved parties and add another security layer for IoT data. IoT devices generate too much data using sensors and stored, processed, accessed the same using cloud computing and achieve security some extend using big-data. Big-data security mechanism is not sufficient to meet the security requirements of IoT devices. We have proposed the Blockchain encryption mechanism using different layers architecture for the IoT devices to achieve the desired security level. In this paper, we have focused on how Blockchain could possibly improve IoT security. We also survey the most relevant work to investigate challenges associate with IoT Blockchain convergence. This proposed mechanism will achieve the security mechanism in IoT devices some extend.
Person Search by a Bi-Directional Task-Consistent Learning Model

Two-stage person search methods achieve the state-of-the-art performance by separate detection and re-ID stages, but neglect the consistency needs between these two stages. The re-ID stage needs more accurate query bounding boxes and fewer boxes of distractors; The detection stage needs the re-ID stage to have robustness against unavailable detection errors. In this paper, we introduce a novel Bi-directional Task-Consistent Learning (BTCL) person search framework, including a Target-Specific Detector (TSD) and a re-ID model with Dynamic Adaptive Learning Structure (DALS). For the former consistency need, we add a verification head for predicting the similarity scores between query and proposals in parallel with the existing heads for bounding box recognition. Thus, TSD generates accurate boxes for the query-like pedestrians, which are suitable for the re-ID stage. For the re-ID robustness need, DALS dynamically generates a large number of possible detection results in line with the real distribution. By training the re-ID model on data with different types of detection errors, DLAS improves the model robustness to detection inputs. Experimental results show our framework achieves state-of-the-art performance on two widely-used person search datasets.