Reviewerly/exHarmony huggingface dataset:
Innovation Strategy of Cultivating Innovative Enterprise Talents for Young Entrepreneurs Under Higher Education

A time series model is designed based on the backpropagation neural network to further optimize the innovation and development of new ventures. The specific situation of two factors is primarily analyzed as follows: the supply and demand ratio of enterprise talents and the state of entrepreneurship in the development of new ventures. The results show that the potential demand of future enterprises for big data talents can be obtained by fitting prediction sequences. Based on the Backpropagation–Autoregressive Integrated Moving Average model, the post modeling and prediction are carried out, and the coefficient 0.6235 is obtained by substituting the equation of Pearson's correlation coefficient. The analysis results suggest that the matching needs to be strengthened between the cultivation of innovative talents in universities and the demand trend of big data-related positions in enterprises. Moreover, there is a mismatch between the cultivation of innovative talents and the demand for innovative talents. Meanwhile, the mental health level of young entrepreneurs is concerned. The mental health status of young entrepreneurs is compared with the national norm data through the questionnaire survey and statistical data analysis. The results reveal that the mental health level of young entrepreneurs is significantly lower than that of the national norm, and the proportion of anxiety and depression is 29.4% and 27.5%, respectively. Considering the professional characteristics and competitive environment of young entrepreneurs, busy work and the multiple missions given by society to entrepreneurs are the major reasons for their pressure, and mental health problems are serious.
Research on Path Planning of Mobile Robot Based on Improved Jump Point Search Algorithm

In view of the traditional jump point search algorithm, there are too many turning points in the path generated, which leads to its shortcomings, such as the path finding is not optimal, and the path smoothness is poor. This paper presents an improved jump point search algorithm. Firstly, the shortest search strategy is adopted to backtrack and generate pathfinding paths in the process of quadratic path planning, and the redundant nodes with only changing direction are eliminated to realize the long jump. Then the pathfinding generated path using quadratic bezier curve is at a turning point to path curve, aims to improve the smoothness of the path, the feasibility of the algorithm are verified through the test, and for a variety of different specifications of two-dimensional grid network map simulation experiment, the result of simulation shows that the improved shorter path length, and the smoothness of the path obviously improved. In order to test the implementation effect of the improved algorithm in the actual environment, it was carried on the ROS mobile robot for experiment. The experimental results show that the path generated by the improved JPS algorithm is more conducive to the robot operation and the execution efficiency is higher.
Research Perspectives: From Other Worlds: Speculative Engagement Through Digital Geographies

Our ability to predict, explain, or control sociotechnical realities is being increasingly called into question by unprecedented phenomena in surveillance, in markets, and in other social and political domains. The apparatus of research - our current categories, instruments, arguments, and epistemic choices - rely on what is empirically accessible, i.e., on the past. Our research orientation toward the future assumes continuity and the extension of past patterns into a predictable and thus manageable future. In this research, we propose speculative engagement through digital geographies to make visible the processes of technological and cultural reconfiguration that result in unprecedented change. After describing the conception of “the future” in widely used research methods, we describe speculative engagement as a research orientation to disclose new categories, relationships, and values and a commitment to the performative relationships of our current research practices with potential future(s). Digital geographies are internally consistent and coherent worlds that are cognitively plausible but estranging. They are carriers of meaning and culture that underpin a broad class of methods to provide richly experienced “other worlds.” We posit principles for effective digital geographies and provide an illustrative example of a digital human artifact that estranges us from current assumptions. Finally, we argue that our approach enables researchers to engage with the future on its own terms. In this way, researchers, designers, and policy makers can open current practices to new categories, relationships, logics, and values and make visible the unprecedented reconfigurations in which our research is implicated.
Improved Bounds on Fourier Entropy and Min-Entropy

Given a Boolean function f:{-1,1}ⁿ→ {-1,1}, define the Fourier distribution to be the distribution on subsets of [n], where each S ⊆ [n] is sampled with probability f(S)². The Fourier Entropy-Influence (FEI) conjecture of Friedgut and Kalai [E. Friedgut and G. Kalai, 1996] seeks to relate two fundamental measures associated with the Fourier distribution: does there exist a universal constant C>0 such that ℍ(f²)≤ C⋅ Inf(f), where ℍ(f²) is the Shannon entropy of the Fourier distribution of f and Inf(f) is the total influence of f?In this paper we present three new contributions towards the FEI conjecture: ii) Our first contribution shows that ℍ(f²) ≤ 2⋅ aUC^⊕(f), where aUC^⊕(f) is the average unambiguous parity-certificate complexity of f. This improves upon several bounds shown by Chakraborty et al. [S. Chakraborty et al., 2016]. We further improve this bound for unambiguous DNFs.iii) We next consider the weaker Fourier Min-entropy-Influence (FMEI) conjecture posed by O'Donnell and others [R. O'Donnell et al., 2011; R. O'Donnell, 2014] which asks if ℍ_{∞}(f²) ≤ C⋅ Inf(f), where ℍ_{∞}(f²) is the min-entropy of the Fourier distribution. We show ℍ_{∞}(f²) ≤ 2⋅𝖢_{min}^⊕(f), where 𝖢_{min}^⊕(f) is the minimum parity certificate complexity of f. We also show that for all e ≥ 0, we have ℍ_{∞}(f²) ≤ 2log (‖f‖_{1,e}/(1-e)), where ‖f‖_{1,e} is the approximate spectral norm of f. As a corollary, we verify the FMEI conjecture for the class of read-k DNFs (for constant k). iv) Our third contribution is to better understand implications of the FEI conjecture for the structure of polynomials that 1/3-approximate a Boolean function on the Boolean cube. We pose a conjecture: no flat polynomial (whose non-zero Fourier coefficients have the same magnitude) of degree d and sparsity 2^ω(d) can 1/3-approximate a Boolean function. This conjecture is known to be true assuming FEI and we prove the conjecture unconditionally (i.e., without assuming the FEI conjecture) for a class of polynomials. We discuss an intriguing connection between our conjecture and the constant for the Bohnenblust-Hille inequality, which has been extensively studied in functional analysis.
Non-Governmental Control in the Sphere of National Security of Ukraine

The article is devoted to the content and concepts of democratic civilian control and non-government control in the sphere of national security.The process of the emergence of democratic civilian control in the field of national security in Ukraine is considered.The international obligations of Ukraine in the framework of ensuring democratic civilian control over the security sector have been systematized.The features of national legal acts have been clarified.In this work a distinction is made between the terms "civil control" and " non-government control".Emphasized that the legislator, relying on international standards of democratic citizen control, has applied English terms in national legislation in a superficial manner.Proved that control by civil society, has the meaning of «civilian control» embedded in international norms, can be defined as «non-government control», laying down one of the main principlestransparency, accessibility and accountability by civil society.It has been determined that civil society should be aimed at promoting the effective functioning of the security sector bodies and the fulfilment of their functions, and at strengthening the national security of Ukraine, although it may be of a recommendatory nature.It is concluded that democratic civilian control over the national security sector should be considered from the perspective of different components -social and government.
A Stacking Approach for Cross-Domain Argument Identification

Argument identification is the cornerstone of a complete argument mining pipeline. Furthermore, it is the essential key for a wide spectrum of applications such as decision making, assisted writing, and legal counselling. Nevertheless, most existing argument mining approaches are limited to a single, specific domain. The problem of building a robust system whose models are able to generalize over heterogeneous datasets remains fairly unexplored. In this paper, we tackle the argument identification task on two different datasets (Student Essays and Web Discourse), following two approaches: a classical machine learning approach and a DistilBert-based approach. Moreover, this paper sheds light on a new direction for researchers in this domain since we validate the principle of ensemble learning. In other words, we show that combining multiple approaches via a well stacked model improves the system performance. The results are very promising with respect to the recent findings in the literature.
kosp2e: Korean Speech to English Translation Corpus

Most speech-to-text (S2T) translation studies use English speech as a source, which makes it difficult for non-English speakers to take advantage of the S2T technologies. For some languages, this problem was tackled through corpus construction, but the farther linguistically from English or the more under-resourced, this deficiency and underrepresentedness becomes more significant. In this paper, we introduce kosp2e (read as `kospi'), a corpus that allows Korean speech to be translated into English text in an end-to-end manner. We adopt open license speech recognition corpus, translation corpus, and spoken language corpora to make our dataset freely available to the public, and check the performance through the pipeline and training-based approaches. Using pipeline and various end-to-end schemes, we obtain the highest BLEU of 21.3 and 18.0 for each based on the English hypothesis, validating the feasibility of our data. We plan to supplement annotations for other target languages through community contributions in the future.
Prioritized Level Replay

Simulated environments with procedurally generated content have become popular benchmarks for testing systematic generalization of reinforcement learning agents. Every level in such an environment is algorithmically created, thereby exhibiting a unique configuration of underlying factors of variation, such as layout, positions of entities, asset appearances, or even the rules governing environment transitions. Fixed sets of training levels can be determined to aid comparison and reproducibility, and test levels can be held out to evaluate the generalization and robustness of agents. While prior work samples training levels in a direct way (e.g.~uniformly) for the agent to learn from, we investigate the hypothesis that different levels provide different learning progress for an agent at specific times during training. We introduce Prioritized Level Replay, a general framework for estimating the future learning potential of a level given the current state of the agent's policy. We find that temporal-difference (TD) errors, while previously used to selectively sample past transitions, also prove effective for scoring a level's future learning potential when the agent replays (that is, revisits) that level to generate entirely new episodes of experiences from it. We report significantly improved sample-efficiency and generalization on the majority of Procgen Benchmark environments as well as two challenging MiniGrid environments. Lastly, we present a qualitative analysis showing that Prioritized Level Replay induces an implicit curriculum, taking the agent gradually from easier to harder levels.
SVM and Cross Validation using R Studio

Each passing day data is getting multiplied. It is difficult to extract useful information from such big data. Data Mining is used to extract useful information. Data mining is used in majorly all fields like healthcare, marketing, social media platforms and so on. In this paper, data is loaded and preprocessed by dealing with some missing values. The dataset used is of Airbnb, the platform used for lodging and tourism industry. Analyzing the data by plotting correlation using spearman method. Further, applying PCA and Support Vector Machine classification technique on the dataset. There are various applications of SVM, it is used in face-detection, text and hypertext categorization, classification of images, bioinformatics and so on. SVM has high dimensional input space, sparse document vectors and regularization parameters therefore it is appropriate to use SVM. Cross-validation gives more accurate result. The dataset is divided into folds. The end product is the test set which is similar to full dataset. Confusion matrix is evaluated, grid approach is followed for building the matrix at various seeds and kernels (RBF, Polynomial). The aim of this research is to see which is the best kernel for the dataset.
Proposed method for scale drawing calculating depending on the line detector and length detector

A scale drawing is a significant tool in many fields such as geography, mathematics, and astronomy research as it has a variety of applications, including Google maps. A new way to benefit from the scale drawing in soccer image is presented in this paper. When the distance of the camera is uncertain, the scale drawing can be used to determine the relationship between the ball and players to estimate the ball size and determine the zooms in and out. The scale drawing and factor are used in architecture and image processing. After calculating the scale factor, the length of every part or line in the image can be computed by multiplying the original length by the scale factor. The research findings indicated that the proposed work was successful in computing the scale drawing in two ways, namely, line detector and length detector, with approximate results to the desired results
Topic Modeling for Analyzing Topic Manipulation Skills

There are many ways to communicate with people, the most representative of which is a conversation. A smooth conversation should not only be written in a grammatically appropriate manner, but also deal with the subject of conversation; this is known as language ability. In the past, this ability has been evaluated by language analysis/therapy experts. However, this process is time-consuming and costly. In this study, the researchers developed a Hallym Systematic Analyzer of Korean language to automate the conversation analysis process traditionally conducted by language analysis/treatment experts. However, current morpheme analyzers or parsing analyzers can only evaluate certain elements of a conversation. Therefore, in this paper, we added the ability to analyze the topic manipulation skills (the number of topics and the rate of topic maintenance) using the existing Hallym Systematic Analyzer of Korean language. The purpose of this study was to utilize the topic modeling technique to automatically evaluate topic manipulation skills. By quantitatively evaluating the topic management capabilities that were previously evaluated in a conventional manner, it was possible to automatically analyze language ability in a wider range of aspects. The experimental results show that the automatic analysis methodology presented in this study achieved a very high level of correlation with language analysis/therapy professionals.
LstFcFedLear: A LSTM-FC with Vertical Federated Learning Network for Fault Prediction

The firefighting IoT platform links multiple firefighting subsystems. The data of each subsystem belongs to the sensitive data of the profession. Failure prediction is a crucial topic for firefighting IoT platforms, because failures may cause equipment injuries. Currently, in the maintenance of fire IoT terminal equipment, fault prediction based on equipment time series has not been included. The use of intelligent technology to continuously predict the failure of firefighting IoT equipment can not only eliminate the intervention of regular maintenance but also provide early warning of upcoming failures. In order to solve this problem, we propose a vertical federated learning framework based on LSTM fault classification network (LstFcFedLear). The advantage of this framework is that it can encrypt and integrate the data on the entire firefighting IoT platform to form a new dataset. After the synthesized data is trained through each model, the optimal model parameters can be finally updated. At the same time, it can ensure that the data of each business system is not leaked. The framework can predict when IoT equipment will fail in the future and then provide what measures should be used. The experimental results show that the LstFcFedLear model provides an effective method for fault prediction, and its results are comparable to the baseline.
Automated model-based performance analysis of software product lines under uncertainty

In the context of Software Product Lines (SPLs), the performance evaluation of the different products is highly relevant, especially if such products include a set of features that are subject to uncertainties (e.g., the service time of a certain functionality may be subject to fluctuations). To this aim, variability modeling notations have been extended with the capability of assigning to the features some attributes that are defined over numeric domains (i.e., attributed feature models), possibly subject to lower and upper bounds capturing their uncertainties.
Practical security analysis against the Trojan-horse attacks on fiber-based phase-coding QKD system in the wide spectral range

The Trojan-horse attacks on quantum key distribution (QKD) systems are known to be a threat to secure key sharing between two parties (Alice and Bob). To protect a QKD system against such attacks a variety of passive optical and active optical devices embedded in Alice’s and Bob’s setup are widely used. However, a security analysis against the Trojan-horse attack is required to ensure the unconditional security of a QKD system. We present a security analysis against the Trojan-horse attacks on a practical fiber-based QKD phase-coding system in 400–1700 nm spectral range. The obtained results made it possible to calculate the higher limit of average photon number returned to Eve and the probabilities of Eve’s success of the attack on a particular QKD system.
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs

A major focus of recent research in spoken language understanding (SLU) has been on the end-to-end approach where a single model can predict intents directly from speech inputs without intermediate transcripts. However, this approach presents some challenges. First, since speech can be considered as personally identifiable information, in some cases only automatic speech recognition (ASR) transcripts are accessible. Second, intent-labeled speech data is scarce. To address the first challenge, we propose a novel system that can predict intents from flexible types of inputs: speech, ASR transcripts, or both. We demonstrate strong performance for either modality separately, and when both speech and ASR transcripts are available, through system combination, we achieve better results than using a single input modality. To address the second challenge, we leverage a semantically robust pre-trained BERT model and adopt a cross-modal system that co-trains text embeddings and acoustic embeddings in a shared latent space. We further enhance this system by utilizing an acoustic module pre-trained on LibriSpeech and domain-adapting the text module on our target datasets. Our experiments show significant advantages for these pre-training and fine-tuning strategies, resulting in a system that achieves competitive intent-classification performance on Snips SLU and Fluent Speech Commands datasets.
A Deep-Network Piecewise Linear Approximation Formula

The mathematical foundation of deep learning is the theorem that any continuous function can be approximated within any specified accuracy by using a neural network with certain non-linear activation functions. However, this theorem does not tell us what the network architecture should be and what the values of the weights are. One must train the network to estimate the weights. There is no guarantee that the optimal weights can be reached after training. This paper develops an explicit architecture of a universal deep network by using the Gray code order and develops an explicit formula for the weights of this deep network. This architecture is target function independent. Once the target function is known, the weights are calculated by the proposed formula, and no training is required. There is no concern whether the training may or may not reach the optimal weights. This deep network gives the same result as the shallow piecewise linear interpolation function for an arbitrary target function.
Channel-Wise Spatiotemporal Aggregation Technology for Face Video Forensics

Recent progress in deep learning, in particular the generative models, makes it easier to synthesize sophisticated forged faces in videos, leading to severe threats on social media about personal privacy and reputation. It is therefore highly necessary to develop forensics approaches to distinguish those forged videos from the authentic. Existing works are absorbed in exploring frame-level cues but insufficient in leveraging affluent temporal information. Although some approaches identify forgeries from the perspective of motion inconsistency, there is so far not a promising spatiotemporal feature fusion strategy. Towards this end, we propose the Channel-Wise Spatiotemporal Aggregation (CWSA) module to fuse deep features of continuous video frames without any recurrent units. Our approach starts by cropping the face region with some background remained, which transforms the learning objective from manipulations to the difference between pristine and manipulated pixels. A deep convolutional neural network (CNN) with skip connections that are conducive to the preservation of detection-helpful low-level features is then utilized to extract frame-level features. The CWSA module finally makes the real or fake decision by aggregating deep features of the frame sequence. Evaluation against a list of large facial video manipulation benchmarks has illustrated its effectiveness. On all three datasets, FaceForensics++, Celeb-DF, and DeepFake Detection Challenge Preview, the proposed approach outperforms the state-of-the-art methods with significant advantages.