Search results for: voice activity detection (VAD)

Authors: Andrii Shalaginov, Katrin Franke, Xiongwei Huang

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One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities.

Keywords: malware detection, network security, targeted attack, computational intelligence

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Authors: Célia Barreto Carvalho, Carolina da Motta, José Pinto-Gouveia, Ermelindo Bernardo Peixoto

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Auditory hallucinations among the most invalidating and distressing experiences reported by patients diagnosed with schizophrenia, leading to feelings of powerlessness and helplessness towards their illness. In more severe cases, these auditory hallucinations can take the form of commanding voices, which are often related to high suicidality rates in these patients. Several authors propose that the meanings attributed to the hallucinatory experience, rather than characteristics like form and content, can be determinant in patients’ reactions to hallucinatory activity, particularly in the case of voice-hearing experiences. In this study, 48 patients diagnosed with paranoid schizophrenia presenting auditory hallucinations were studied. Multiple regression analyses were computed to study the influence of several developmental aspects, such as family and social dynamics, bullying, depression, and socio-cognitive variables on the auditory hallucinations, on patients’ attributions and relationships with their voices, and on the resulting invalidation of hallucinatory experience. Overall, results showed how relationships with voices can mirror several aspects of interpersonal relationship with others, and how self-schemas, depression and actual social relationships help shaping the voice-hearing experience. Early experiences of victimization and submission help predict the attributions of omnipotence of the voices, and increased hostility from parents seems to increase the malevolence of the voices, suggesting that socio-cognitive factors can significantly contribute to the etiology and maintenance of auditory hallucinations. The understanding of the characteristics of auditory hallucinations and the relationships patients established with their voices can allow the development of more promising therapeutic interventions that can be more effective in decreasing invalidation caused by this devastating mental illness.

Keywords: auditory hallucination, beliefs, life events, schizophrenia

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Authors: Khenfer-Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

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this paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a man-machine interface with a voice recognition system that allows the operator to tele-operate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands spoken in two languages: French and Arabic. The recognition rate obtained is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equal to 30 db, the Arabic speech recognition rate is 69% and 80% for French speech recognition rate. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: voice command, HMM, TIMIT, noise, HTK, Arabic, speech recognition

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Authors: LuoJiaoyang, Yu Hongyang

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In this paper, we experimented with a new approach to multimodal identification using RGB, RGB-D and voice data. The multimodal combination of RGB and voice data has been applied in tasks such as emotion recognition and has shown good results and stability, and it is also the same in identity recognition tasks. We believe that the data of different modalities can enhance the effect of the model through mutual reinforcement. We try to increase the three modalities on the basis of the dual modalities and try to improve the effectiveness of the network by increasing the number of modalities. We also implemented the single-modal identification system separately, tested the data of these different modalities under clean and noisy conditions, and compared the performance with the multimodal model. In the process of designing the multimodal model, we tried a variety of different fusion strategies and finally chose the fusion method with the best performance. The experimental results show that the performance of the multimodal system is better than that of the single modality, especially in dealing with noise, and the multimodal system can achieve an average improvement of 5%.

Keywords: multimodal, three modalities, RGB-D, identity verification

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Authors: Sanskar, Abhinav Pal, Aryush Gupta, Sushil Kumar Mishra

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One of the critical and tedious assignments in agricultural practices is the detection of diseases on vegetation. Agricultural production is very important in today’s economy because plant diseases are common, and early detection of plant diseases is important in agriculture. Automatic detection of such early diseases is useful because it reduces control efforts in large productive farms. Using digital image processing and machine learning algorithms, this paper presents a method for plant disease detection. Detection of the disease occurs on different leaves of the plant. The proposed system for plant disease detection is simple and computationally efficient, requiring less time than learning-based approaches. The accuracy of various plant and foliar diseases is calculated and presented in this paper.

Keywords: plant diseases, machine learning, image processing, deep learning

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Authors: Sulaiman Al amro, Ali Alkhalifah

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The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses.

Keywords: computer viruses, virus detection, signature-based, behaviour-based, heuristic-based

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Authors: Kristin Murray

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The proposed paper exams prologues in 20th and 21st century American literature in order to show how Native American writer Tommy Orange’s Prologue in his 2018 novel There, Thereis different. In an interview about his 2018 novel There, There, explains he feels “a kind of burden to catch the general reader up with what really happened, because history has got it so wrong and still continue to” (Laubernds). Orange, thus, includes a “Prologue” in his novel to do this work, catching readers upon Native Americans and their history. Prologues are usually from the narrator’s voice, a character’s voice, or even from a fictionalized version of the author, but the tone of Orange’s “Prologue” is that of a non-fictional first-person essayist. Examining prologues in American literature posits Orange’s prologue outside the norm. This paper also examines other introductory sections, the preface, in particular. The research and examination reveal that Orange is adding his personal voice in the Prologue to the multiple narratorsof the novel, and his is the voice of a writer who knows that his audience comes to his novel with a plethora of misinformation. The truths he tells are horrifying and hopeful. He tells of Thanksgiving as a “land deal” and a “successful massacre,” but he also tellsreaders how urban Indians have found a sense of the land, even through concrete. Native American writers contributed and still contribute to the genre of autobiography in ways that have changed our understanding of this genre. This examination of Orange’s Prologue reveals the new and unexpected way to view this often under-examined introductory section, the prologue.

Keywords: native american literature, prologues, prefaces, 20th century american literature

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Authors: Alexandra Roy

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This research examines how physical and vocal attractiveness affect impressions of an applicant and whether these impressions are affected by gender or job type. Findings, based on two samples, indicate that individuals with less attractiveness voice and physical appearance were viewed as less suitable job applicants and as possessing more negative characteristics than those others. These negative impressions were pervasive and unaffected by either applicant gender or job type. Specifically, we found that job candidates with an attractive voice or physique were perceived as more extroverted, less agreeable, less conscientious, less trustworthy less competent, less sociable and less recruitable. Results are robust to various sensitivity checks.

Keywords: discrimination, nonverbal, hiring, attractiveness

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Authors: Kaewmart Pongakkasira

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This study investigated how different levels of pixelation affect face detection in natural scenes. Eye movements and reaction times, while observers searched for faces in natural scenes rendered in different ranges of pixels, were recorded. Detection performance for coarse visual detail at lower pixel size (3 x 3) was better than with very blurred detail carried by higher pixel size (9 x 9). The result is consistent with the notion that face detection relies on gross detail information of face-shape template, containing crude shape structure and features. In contrast, detection was impaired when face shape and features are obscured. However, it was considered that the degradation of scenic information might also contribute to the effect. In the next experiment, a more direct measurement of the effect of pixelation on face detection, only the embedded face photographs, but not the scene background, will be filtered.

Keywords: eye movements, face detection, face-shape information, pixelation

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Authors: M. Ranjeeth, S. Anuradha

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Spectrum sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. In this paper we compare the theoretical results of detection probability of different fading environments like Rayleigh, Rician, Nakagami-m fading channels with the simulation results using energy detection based spectrum sensing. The numerical results are plotted as P_f Vs P_d for different SNR values, fading parameters. It is observed that Nakagami fading channel performance is better than other fading channels by using energy detection in spectrum sensing. A MATLAB simulation test bench has been implemented to know the performance of energy detection in different fading channel environment.

Keywords: spectrum sensing, energy detection, fading channels, probability of detection, probability of false alarm

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Authors: B. S. Premalatha, Kausalya Sahani

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Background: Voice is multidimensional which convey emotion, feelings, and communication. Voice disorders have an adverse effect on the physical, emotional and functional domains of an individual. Self-rating by clients about their voice problem helps the clinicians to plan intervention strategies. Voice handicap index is one such self-rating scale contains 30 questions that quantify the functional, physical and emotional impacts of a voice disorder on a patient’s quality of life. Each subsection has 10 questions. Though adapted and validated versions of VHI are available in other Indian languages but not in Telugu, which is a Dravidian language native to India. It is mainly spoken in Andhra Pradesh and neighbouring states in southern India. Objectives: To adapt and validate the English version of Voice Handicap Index (VHI) into Telugu language and evaluate its internal consistency and clinical validate in Telugu speaking population. Materials: The study carried out in three stages. First stage was a forward translation of English version of VHI, was given to ten experts, who were well proficient in writing and reading Telugu and five speech-language pathologists to translate into Telugu. Second Stage was backward translation where translated version of Telugu was given to a different group of ten experts (who were well proficient in writing and reading Telugu) and five speech-language pathologists who were native Telugu speakers and had good proficiency in Telugu and English. The third stage was an administration of translated version on Telugu to the targeted population. Totally 40 clinical subjects and 40 normal controls served as participants, and each group had 26 males and 14 females’ age range of 20 to 60 years. Clinical group comprised of individuals with laryngectomee with the Tracheoesophageal puncture (n=18), laryngitis (n=11), vocal nodules (n=7) and vocal fold palsy (n=4). Participants were asked to mark of their each experience on a 5 point equal appearing scale (0=never, 1=almost never, 2=sometimes, 3=almost always, 4=always) with a maximum total score of 120. Results: Statistical analysis was made by using SPSS software (22.0.0 Version). Mean, standard deviation and percentage (%) were calculated all the participants for both the groups. Internal consistency of VHI in Telugu was found to be excellent with the consistency scores for all the domains such as physical, emotional and functional are 0.742, 0.934and 0.938. The validity of scores showed a significant difference between clinical population and control group for domains like physical, emotional and functional and total scores. P value found to be less than 0.001( < 0.001). Negative correlation found in age and gender among self-domains such as physical, emotional and functional total scores in dysphonic and control group. Conclusion: The present study indicated that VHI in Telugu is able to discriminate participants having voice pathology from normal populations, which make this as a valid tool to collect information about their voice from the participants.

Keywords: adaptation, Telugu Version, translation, Voice Handicap Index (VHI)

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Authors: L. Bruckert, A. Mansourzadeh

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The voice has a major influence on interpersonal communication in everyday life via the perception of pleasantness. The evolutionary perspective postulates that the mechanisms underlying the pleasantness judgments are universal adaptations that have evolved in the service of choosing a mate (through the process of sexual selection). From this point of view, the favorite voices would be those with more marked sexually dimorphic characteristics; for example, in men with lower voice pitch, pitch is the main criterion. On the other hand, one can postulate that the mechanisms involved are gradually established since childhood through exposure to the environment, and thus the prosodic elements could take precedence in everyday life communication as it conveys information about the speaker's attitude (willingness to communicate, interest toward the interlocutors). Our study focuses on voice pleasantness and its relationship with social impression formation, exploring both the spectral aspects (pitch, timbre) and the prosodic ones. In our study, we recorded the voices through two vocal corpus (five vowels and a reading text) of 25 French males speaking French and 25 Iranian males speaking Farsi. French listeners (40 male/40 female) listened to the French voices and made a judgment either on the voice's pleasantness or on the speaker (judgment about his intelligence, honesty, sociability). The regression analyses from our acoustic measures showed that the prosodic elements (for example, the intonation and the speech rate) are the most important criteria concerning pleasantness, whatever the corpus or the listener's gender. Moreover, the correlation analyses showed that the speakers with the voices judged as the most pleasant are considered the most intelligent, sociable, and honest. The voices in Farsi have been judged by 80 other French listeners (40 male/40 female), and we found the same effect of intonation concerning the judgment of pleasantness with the corpus «vowel» whereas with the corpus «text» the pitch is more important than the prosody. It may suggest that voice perception contains some elements invariant across culture/language, whereas others are influenced by the cultural/linguistic background of the listener. Shortly in the future, Iranian people will be asked to listen either to the French voices for half of them or to the Farsi voices for the other half and produce the same judgments as the French listeners. This experimental design could potentially make it possible to distinguish what is linked to culture and what is linked to language in the case of differences in voice perception.

Keywords: cross-cultural psychology, impression formation, pleasantness, voice perception

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Authors: John Onyima, Ikechukwu Ezepue

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Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

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Authors: Aw Adi Arryansyah

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Using electronic stethoscope for detecting heartbeat sound, and breath sounds, are the effective way to investigate cardiovascular diseases. On the other side, technology is growing towards mobile. Almost everyone has a smartphone. Smartphone has many platforms. Creating mobile applications also became easier. We also can use HTML5 technology to creating mobile apps. Android is the most widely used type. This is the reason for us to make a wireless electronic stethoscope based on Android mobile. Android based Wireless Electronic Stethoscope designed by a simple system, uses sound sensors mounted membrane, then connected with Bluetooth module which will send the heart auscultation voice input data by Bluetooth signal to an android platform. On the software side, android will read the voice input then it will translate to beautiful visualization and release the voice output which can be regulated about how much of it is going to be released. We can change the heart beat sound into BPM data, and heart beat analysis, like normal beat, bradycardia or tachycardia.

Keywords: wireless, HTML 5, auscultation, bradycardia, tachycardia

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Authors: Doaa Wael, Naswa Abdelbaky

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Malware is malicious software that is built to cause destructive actions and damage information systems and networks. Malware infections increase rapidly, and types of malware have become more sophisticated, which makes the malware detection process more difficult. On the other side, the Internet of Things IoT technology is vulnerable to malware attacks. These IoT devices are always connected to the internet and lack security. This makes them easy for hackers to access. These malware attacks are becoming the go-to attack for hackers. Thus, in order to deal with this challenge, new malware detection techniques are needed. Currently, building a blockchain solution that allows IoT devices to download any file from the internet and to verify/approve whether it is malicious or not is the need of the hour. In recent years, blockchain technology has stood as a solution to everything due to its features like decentralization, persistence, and anonymity. Moreover, using blockchain technology overcomes some difficulties in malware detection and improves the malware detection ratio over-than the techniques that do not utilize blockchain technology. In this paper, we study malware detection models which are based on blockchain technology. Furthermore, we elaborate on the effect of blockchain technology in malware detection, especially in the android environment.

Keywords: malware analysis, blockchain, malware attacks, malware detection approaches

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Authors: Anindita Banik, Anjali Kant, Aninda Duti Banik, Arun Banik

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Speech is a complex, dynamic unique motor activity through which we express thoughts and emotions and respond to and control our environment. The aim was based to compare select Motor Speech parameters and their sub parameters across typical Marathi speaking adults and children. The subjects included a total of 300 divided into Group I, II, III including males and females. Subjects included were reported of no significant medical history and had a rating of 0-1 on GRBAS scale. The recordings were obtained utilizing three stimuli for the acoustic analysis of Diadochokinetic rate (DDK), Second Formant Transition, Voice and Tremor and its sub parameters. And these aforementioned parameters were acoustically analyzed in Motor Speech Profile software in VisiPitch IV. The statistical analyses were done by applying descriptive statistics and Two- Way ANOVA.The results obtained showed statistically significant difference across age groups and gender for the aforementioned parameters and its sub parameters.In DDK, for avp (ms) there was a significant difference only across age groups. However, for avr (/s) there was a significant difference across age groups and gender. It was observed that there was an increase in rate with an increase in age groups. The second formant transition sub parameter F2 magn (Hz) also showed a statistically significant difference across both age groups and gender. There was an increase in mean value with an increase in age. Females had a higher mean when compared to males. For F2 rate (/s) a statistically significant difference was observed across age groups. There was an increase in mean value with increase in age. It was observed for Voice and Tremor MFTR (%) that a statistically significant difference was present across age groups and gender. Also for RATR (Hz) there was statistically significant difference across both age groups and gender. In other words, the values of MFTR and RATR increased with an increase in age. Thus, this study highlights the variation of the motor speech parameters amongst the typical population which would be beneficial for comparison with the individuals with motor speech disorders for assessment and management.

Keywords: adult, children, diadochokinetic rate, second formant transition, tremor, voice

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Authors: Abdulsalam M. Alhawsawi

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Estimation of a radiation detector's efficiency plays a significant role in calculating the activity of radioactive samples. Detector efficiency is measured using sources that emit a variety of energies from low to high-energy photons along the energy spectrum. Some photon energies are hard to find in lab settings either because check sources are hard to obtain or the sources have short half-lives. This work aims to develop a method to determine the efficiency of a High Purity Germanium Detector (HPGe) based on the 662 keV gamma ray photon emitted from Cs-137. Cesium-137 is readily available in most labs with radiation detection and health physics applications and has a long half-life of ~30 years. Several photon efficiencies were calculated using the MCNP5 simulation code. The simulated efficiency of the 662 keV photon was used as a base to calculate other photon efficiencies in a point source and a Marinelli Beaker form. In the Marinelli Beaker filled with water case, the efficiency of the 59 keV low energy photons from Am-241 was estimated with a 9% error compared to the MCNP5 simulated efficiency. The 1.17 and 1.33 MeV high energy photons emitted by Co-60 had errors of 4% and 5%, respectively. The estimated errors are considered acceptable in calculating the activity of unknown samples as they fall within the 95% confidence level.

Keywords: MCNP5, MonteCarlo simulations, efficiency calculation, absolute efficiency, activity estimation, Cs-137

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Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: Hyun-Koo Kim, Yonghun Kim, Yong-Hoon Kim, Ju Hee Lee, Myungho Song

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In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm.

Keywords: advanced driver assistance system, pedestrian detection, stereo matching method, stereo long-wave IR camera

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Shigenori Togashi, Kei Takenaka

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We developed new processes which can collect and detect rapidly airborne pathogens such as the avian flu virus for the pandemic prevention. The fluorescence antibody technique is known as one of high-sensitive detection methods for viruses, but this needs up to a few hours to bind sufficient fluorescence dyes to viruses for detection. In this paper, we developed a mist-labeling can detect substitution viruses in a short time to improve the binding rate of fluorescent dyes and substitution viruses by the micro reaction process. Moreover, we developed the rapid detection system with the above 'mist labeling'. The detection system set with a sampling bag collecting patient’s breath and a cartridge can detect automatically pathogens within 10 minutes.

Keywords: viruses, sampler, mist, detection, fluorescent dyes, microreaction

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Authors: Igor Izosimov

Abstract:

This work is devoted to applications of the Time-resolved laser-induced luminescence (TRLIF) spectroscopy and time-resolved laser-induced chemiluminescence spectroscopy for detection of lanthanides and actinides. Results of the experiments on Eu, Sm, U, and Pu detection in solutions are presented. The limit of uranyl detection (LOD) in urine in our TRLIF experiments was up to 5 pg/ml. In blood plasma LOD was 0.1 ng/ml and after mineralization was up to 8pg/ml – 10pg/ml. In pure solution, the limit of detection of europium was 0.005ng/ml and samarium, 0.07ng/ml. After addition urine, the limit of detection of europium was 0.015 ng/ml and samarium, 0.2 ng/ml. Pu, Np, and some U compounds do not produce direct luminescence in solutions, but when excited by laser radiation, they can induce chemiluminescence of some chemiluminogen (luminol in our experiments). It is shown that multi-photon scheme of chemiluminescence excitation makes chemiluminescence not only a highly sensitive but also a highly selective tool for the detection of lanthanides/actinides in solutions.

Keywords: actinides/lanthanides detection, laser spectroscopy with time resolution, luminescence/chemiluminescence, solutions

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Authors: Jyoti Bharti, M. K. Gupta, Astha Jain

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This paper proposes a new improved approach for false positives filtering of detected face images on OpenCV’s Viola Jones Algorithm In this approach, for Filtering of False Positives, Skin Detection in two colour spaces i.e. HSV (Hue, Saturation and Value) and YCrCb (Y is luma component and Cr- red difference, Cb- Blue difference) is used. As a result, it is found that false detection has been reduced. Our proposed method reaches the accuracy of about 98.7%. Thus, a better recognition rate is achieved.

Keywords: face detection, Viola Jones, false positives, OpenCV

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Authors: Khenfer Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

Abstract:

This paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a human-machine interface with a voice recognition system that allows the operator to teleoperate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands pronounced in two languages: French and Arabic. The obtained recognition rate is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equals 30 dB, in this case; the Arabic speech recognition rate is 69%, and the French speech recognition rate is 80%. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: Arabic speech recognition, Hidden Markov Model (HMM), HTK, noise, TIMIT, voice command

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Authors: Kanyapat U. Tapao

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The purpose of this study is to analyze the operation strategy strategies and public relations trend for public relations strategies development in public television station in Thailand. This study is a qualitative approach by indent interview from the 6 key informants that are managers of Voice TV and Thairath TV Channel. The results showed that both TV stations have to do research before making a release on the operation strategy policy such as a slogan, segmentation, integrated marketing communication and PR activity and also in term of Public Relations trend are including online media, online content and online training before opening the station and start promoting. By the way, we found the PR strategy for both TV station should be including application on mobile, online content, CRM activity, online banner, special event, and brand ambassador in order to bring a very reliable way.

Keywords: online banner, operation strategy, public relations trend, public relations strategies development

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Authors: Lan Du, Yan Wang, Hui Dai

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Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method.

Keywords: change detection, Synthetic Aperture Radar (SAR), Scale-Invariant Feature Transformation (SIFT), segmentation

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Authors: Rika Aoki

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The aim of this paper is to investigate whether being bilinguals facilitates or impedes the perception of a third language. The present study conducted a perception experiment in which Mandarin-Japanese bilinguals categorized a Voice-Onset-Time (VOT) continuum into English /b/ or /p/. The results show that early bilinguals were influenced by both Mandarin and Japanese, while late bilinguals behaved in a similar manner to Mandarin monolinguals Thus, it can be concluded that in the present study having two languages did not help bilinguals to perceive L3 stop contrast native-likely.

Keywords: bilinguals, perception, third language acquisition, voice-onset-time

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Authors: Samane Sharifi Monfared, Lavdie Rada

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Nowadays, autonomous vehicles are developing rapidly toward facilitating human car driving. One of the main issues is road lane detection for a suitable guidance direction and car accident prevention. This paper aims to improve and optimize road line detection based on a combination of camera calibration, the Hough transform, and Canny edge detection. The video processing is implemented using the Open CV library with the novelty of having a scale able region masking. The aim of the study is to introduce automatic road lane detection techniques with the user’s minimum manual intervention.

Keywords: hough transform, canny edge detection, optimisation, scaleable masking, camera calibration, improving the quality of image, image processing, video processing

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Authors: Karl Baker-Green, Ian Woolsey

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Historically, a variety of methods have been used to access the student voice within higher education, including module evaluations and informal classroom feedback. However, currently, the views articulated in student-staff-committee meetings bear the most weight and can therefore have the most significant impact on departmental policy. Arguably, these forums are exclusionary as several students, including those who experience severe anxiety, might feel unable to participate in this face-to-face (large) group activities. Similarly, students who declare a disability, but are not in possession of a learning contract, are more likely to withdraw from their studies than those whose additional needs have been formally recognised. It is also worth noting that whilst the number of disabled students in Higher Education has increased in recent years, the percentage of those who have been issued a learning contract has decreased. These issues foreground the need to explore the educational experiences of students with or without a learning contract in order to identify their respective aspirations and needs and therefore help shape education policy. This is in keeping with the ‘Nothing about us without us’, agenda, which recognises that disabled individuals are best placed to understand their own requirements and the most effective strategies to meet these.

Keywords: education, student voice, student experience, student retention

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Authors: Abdol-Hassan Doulah

Abstract:

In this study, antimicrobial activity of essential oil and ethyl acetate and ether extracts of S. nemorosa were examined against some species of bacteria and fungi. The essential oil of the aerial part of S. nemorosa was examined by GC and GC-MS. In the essential oil of S. nemorosa 26 Compounds have been identified. 2-Nonanone (44.09 %), 2-Undecanone (33.79 %), E-Caryophyllene (3.74 %) and 2-Decanone (2.89 %) were the main components of the essential oil. The essential oil analysis showed greatest antimicrobial activity against Staphylococcus epidermidis (5.3 μg/ml) and S. cerevisiae (9.3 μg/ml). The ethyl acetate showed greatest antimicrobial activity against Bacillus subtilis (106.7 μg/ml), Candida albicans (5.3 μg/ml) and ether extract showed greatest antimicrobial activity against Klebseilla pneumoniae (10.7 μg/ml) and Saccharomyces cerevisiae (10.7 μg/ml). In conclusion, we suggest that the antimicrobial activity of S. nemorosa may be due to its content of germacrene and linalool.

Keywords: antibacterial activity, antifungal activity, Salvia nemorosa L., essential oils, biological activity

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